This PDF file contains the front matter associated with SPIE Proceedings Volume 8436, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

It has been shown that the original light field camera behaves the opposite of a conventional one: Image blur decays away from a certain plane in space, a property that is called inverted depth of field.¹ Moreover, the blur at such out of focus plane is bounded from above. This property allows the light field camera to exceed the depth of field of conventional imaging systems as well as that of other computational devices. In this paper, we propose a novel design to further improve the light field camera. The proposed system can drastically reduce the loss in resolution at the out of focus plane while retaining the advantages of the original camera design. We introduce a beam splitter (prism or glass) to provide the same field of view on two halves of the sensor. We also split a microlens array in two halves matching the sensor partition. The focal length of the microlenses in one half can be different from those of the other half. In the geometric optics approximation this architecture ensures that each half of the sensor can measure light ray samples with different (and partially complementary) aliasing properties.

The introduction of lasers for projection applications is hampered by the emergence of speckle. In order to evaluate the speckle distorted image quality, it is important to devise an objective way to measure the amount of speckle. Mathematically, speckle can be described by its speckle contrast value C, which is given by the ratio between the standard deviation of the intensity fluctuations and the mean intensity. Because the measured speckle contrast strongly depends on the parameters of the measurement setup, in this paper we propose a standardized procedure to measure the amount of speckle in laser based projection systems. To obtain such a procedure, the influence of relevant measurement set-up parameters is investigated. The resulting measurement procedure consists of a single digital image sensor in combination with a camera lens. The parameters of the camera lens are chosen such that the measured speckle contrast values correspond with the subjective speckle perception of a human observer, independent of the projector's speckle reduction mechanism(s). Finally, the speckle measurement procedure was performed with different cameras and the results were compared.

This paper deals with the criteria definition of image quality in astronomy and their comparison with common multimedia approaches. Astronomical images have typical specific properties - high grayscale bit depth, size, high noise occurrence, sensitivity to point spread function deformation and special processing algorithms. They belong to the class of scientific images as well as medical or similar. Their processing and compression is quite different from the classical approach of multimedia image processing. The new compression algorithm based on JPEG2000 is selected as a distortion source in this paper. Selected image quality criteria (multimedia and optimized for astronomical images) are tested on the set of images from the DEIMOS image database with miscellaneous level of the thermally generated CCD noise. The deformation of the point spread function (PSF) is also measured for chosen compression approach.

Several professional photographic applications uses the merging of consecutive overlapping images in order to obtain bigger files by means of stitching techniques or extended field of view (FOV) for panoramic images. All of those applications share the fact that the final composed image is obtained by overlapping the neighboring areas of consecutive individual images taken as a mosaic or a series of tiles over the scene, from the same point of view. Any individual image taken with a given lens can carry residual aberrations and several of them will affect more probably the borders of the image frame. Furthermore, the amount of distortion aberration present in the images of a given lens will be reversed in position for the two overlapping areas of a pair of consecutive takings. Finally, the different images used in composing the final one have corresponding overlapping areas taken with different perspective. From all the previously stated can be derived that the software employed must remap all the pixel information in order to resize and match image features in those overlapping areas, providing a final composed image with the desired perspective projection. The work presented analyse two panoramic format images taken with a pair of lenses and composed by means of a state of the art stitching software. Then, a series of images are taken to cover an FOV three times the original lens FOV, the images are merged by means of a software of common use in professional panoramic photography and the final image quality is evaluated through a series of targets positioned in strategic locations over the whole taking field of view. That allows measuring the resulting Resolution and Modulation Transfer Function (MTF). The results are shown compared with the previous measures on the original individual images.

There is a vast body of literature concerning the capture, storing, transmission and display of High Dynamic Range (HDR) imaging. Nevertheless, there are few works that try to address the problem of getting HDR on mobile devices. Their hardware limitations, such as processing power, storage space, graphics capabilities and screen characteristics, transform that problem in a big challenge. However, since more and more HDR content is being produced and given that in a few years it can become a standard, it is necessary to provide the means to visualize HDR images and video on mobile devices. The main goal of this paper is to present a survey on HDR visualization approaches and techniques developed specifically for mobile devices. To understand what are the main challenges that need to be addressed in order to visualize HDR on mobile devices, an overview of their main characteristics is given. The very low dynamic range of most of mobile devices' displays implies that a tone mapping operator (TMO) must be applied in order to visualize the HDR content. The current status of the research on TMO will be presented and analyzed, a special attention will be given to the ones that were developed taking in account the limited characteristics of the mobile devices' displays. Another important issue is visualization quality assessment, meaning visualize HDR content without losing the main characteristics of the original HDR content. Thus, evaluation studies of HDR content visualization on mobile devices will be presented and their results analyzed.

This paper presents a two layer CODEC architecture for high dynamic range image compression. The first layer contains the tone mapped image obtained using a conventional low dynamic range encoding approach, such as JPEG. The second layer contains the image difference, in perceptually uniform colour space, between the result of inverse tone mapped low dynamic range content and the original image. We present techniques for efficient implementation and encoding of nonuniform tone mapping operators. Different colourspaces and compression algorithms are compared.

The limited dynamic range of digital images can be extended by composing photographs of the same scene taken with the same camera at the same view point at dierent exposure times. This is a standard procedure for static scenes but a challenging task for dynamic ones. Several methods have been presented but few recover high dynamic range within moving areas. We present a method to recover full high dynamic range (HDR) images from dynamic scenes, even in moving regions. Our method has 3 steps. Firstly, areas aected by motion are detected to generate a ghost mask. Secondly, we register dynamic objects over a reference image (the best exposed image in the input sequence). Thirdly, we combine the registered input photographs to recover HDR values in a whole image using a weighted average function. Once matching is found, the assembling step guarantees that all aligned pixels will contribute to the nal result, including dynamic content. Tests were made on more than 20 sets of sequences, with moving cars or pedestrians and dierent background. Our results show that Image Mapping Function approach detects best motion regions while Normalized Cross Correlation oers the best deal speed-accuracy for image registration. Results from our method oers better result when moving object are roughly rigid and their movement is mostly rigid. The nal composition is an HDR image with no ghosting and all dynamic content present in HDR values.

We propose a new methodology to acquire HDR video content for autostereoscopic displays by adapting and augmenting an eight view video camera with standard sensors. To augment the intensity capacity of the sensors, we combine images taken at dierent exposures. Since the exposure has to be the same for all objectives of our camera, we x the exposure variation by applying neutral density lters on each objective. Such an approach has two advantages: several exposures are known for each video frame and we do not need to worry about synchronization. For each pixel of each view, an HDR value is computed by a weighted average function applied to the values of matching pixels from all views. The building of the pixel match list is simplied by the property of our camera which has eight aligned, equally distributed objectives. At each frame, this results in an individual HDR image for each view while only one exposition per view was taken. The nal eight HDR images are tone-mapped and interleaved for autostereoscopic display.

Local backlight dimming in Liquid Crystal Displays (LCD) is a technique for reducing power consumption and simultaneously increasing contrast ratio to provide a High Dynamic Range (HDR) image reproduction. Several backlight dimming algorithms exist with focus on reducing power consumption, while other algorithms aim at enhancing contrast, with power savings as a side effect. In our earlier work, we have modeled backlight dimming as a linear programming problem, where the target is to minimize the cost function measuring the distance between ideal and actual output. In this paper, we propose a version of the abovementioned algorithm, speeding up execution by decreasing the number of input variables. This is done by using a subset of the input pixels, selected among the ones experiencing leakage or clipping distortions. The optimization problem is then solved on this subset. Sample reduction can also be beneficial in conjunction with other approaches, such as an algorithm based on gradient descent, also presented here. All the proposals have been compared against other known approaches on simulated edge- and direct-lit displays, and the results show that the optimal distortion level can be reached using a subset of pixels, with significantly reduced computational load compared to the optimal algorithm with the full image.

Medical digital imaging has become a key element of modern health care procedures. It provides visual documentation and a permanent record for the patients, and most important the ability to extract information about many diseases. Modern ophthalmology thrives and develops on the advances in digital imaging and computing power. In this work we present an overview of recent image processing techniques proposed by the authors in the area of digital eye fundus photography. Our applications range from retinal image quality assessment to image restoration via blind deconvolution and visualization of structural changes in time between patient visits. All proposed within a framework for improving and assisting the medical practice and the forthcoming scenario of the information chain in telemedicine.

Corneal Ulcers are a common eye disease that requires prompt treatment. Recently a number of treatment approaches have been introduced that have been proven to be very effective. Unfortunately, the monitoring process of the treatment procedure remains manual and hence time consuming and prone to human errors. In this research we propose an automatic image analysis based approach to measure the size of an ulcer and its subsequent further investigation to determine the effectiveness of any treatment process followed. In Ophthalmology an ulcer area is detected for further inspection via luminous excitation of a dye. Usually in the imaging systems utilised for this purpose (i.e. a slit lamp with an appropriate dye) the ulcer area is excited to be luminous green in colour as compared to rest of the cornea which appears blue/brown. In the proposed approach we analyse the image in the HVS colour space. Initially a pre-processing stage that carries out a local histogram equalisation is used to bring back detail in any over or under exposed areas. Secondly we deal with the removal of potential reflections from the affected areas by making use of image registration of two candidate corneal images based on the detected corneal areas. Thirdly the exact corneal boundary is detected by initially registering an ellipse to the candidate corneal boundary detected via edge detection and subsequently allowing the user to modify the boundary to overlap with the boundary of the ulcer being observed. Although this step makes the approach semi automatic, it removes the impact of breakages of the corneal boundary due to occlusion, noise, image quality degradations. The ratio between the ulcer area confined within the corneal area to the corneal area is used as a measure of comparison. We demonstrate the use of the proposed tool in the analysis of the effectiveness of a treatment procedure adopted for corneal ulcers in patients by comparing the variation of corneal size over time.

One of the main goals of texture analysis is to provide a robust mathematical description of the spatial behavior of intensity values in any given neighborhood. These local distributions {called textures{ characterize object surfaces and are used for pattern identication and recognition of images. However, some spatial patterns may vary from quite simple stripes to randomness, where textures look like unstructured noise. Since textures can exhibit a large number of properties such as surface materials and geometry of the lighting sources, many dierent approaches have been proposed. A featured method is the modication of Wang's algorithm made by Ojala et al, the so-called local binary patterns (LBP). The LBP algorithm uses a 3×3 square mask named "texture spectrum" which represents a neighborhood around a central pixel. The values in the square mask are compared with the central pixel and then multiplied by a weighting function according with their positions. This technique has become popular due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. Specially, it has gained increased importance in image classication, where the success not only depends on a robust classier but also relies in a good selection of the feature descriptors. However, Ojala's algorithm presents some limitations such as noise sensitivity and lack of invariance to rotational changes. This fact has fostered many extensions of the original LBP approach that in many cases are based on minor changes in order to attain e.g. illumination and rotational invariance or improving the robustness to noise. In this paper we present a detailed overview of the LBP algorithm and other recently modications. In addition, we perform a texture classication study with seven algorithms in presence of rotational changes, noise degradation, contrast information, and dierent sizes of LBP masks using the USC-SIPI database. The LBP histograms have been evaluated using the Kullback-Leibler distance. This study will be a valuable insight for establishing a robust and ecient texture descriptor to solve real world problems.

Commonly, visual inspection tasks in the textile industry are performed by human experts. The major drawback of this type of inspection is the human subjectivity, which affects accuracy and repeatability. Objectivity, accuracy and repeatability can be achieved by analysing visual characteristics of the products using computer vision. Particularly, automatic real time inspection systems based on texture analysis can be implemented using Local Binary Pattern (LBP) techniques. A recent variation of the LBP techniques, named Geometric Local Binary Pattern (GLBP) technique, showed an increase in the performance for detecting small changes of local texture. In this paper a real time implementation of the algorithm is presented by using a Graphic Processing Unit (GPU). The LBP and GLBP techniques are compared in terms of speed and accuracy while implemented on a Central Processing Unit (CPU) and GPU environments. Algorithms are tested for detecting defects in fabrics as well as for evaluating global deviations of texture, which are due to the degradation of the surface in carpets. Results show that higher discriminant power between similar textures is obtained when using the GLBP technique.

Logarithmic High Dynamic Range CMOS (HDRC) image sensors exhibit the highest Dynamic Range and a straight forward image acquisition compared to other High Dynamic Range imagers or techniques. The nearly constant pixel random noise over the illumination range, in contrast to sensors with linear or piece-wise linear Opto Electronic Conversion Function (OECF), gives rise to a balanced contrast resolution. The Noise Equivalent Contrast (NEC) will be introduced as a figure of merit to compare both imager types with nonlinear and linear OECF, which leads to the incremental Signal-to-Noise Ratio (iSNR) and SNR, respectively. This will be shown by measurements of OECF and NEC performed with a logarithmic HDRC imager. The resulting iSNR, related to ISO15739, will be quantitatively compared to SNR data of a linear imager according to EMVA1288 standard. The benefits of the logarithmic imager come with the necessity to correct CMOS technology dependent pixel to pixel variations, namely the MOS transistor threshold and gain variations and the photodiode dark current distribution contributing to an overlaid Fixed Pattern in the raw image data. Depending on the required quality and the allowed computational complexity a Fixed Pattern Correction (FPC) algorithm should correct from the most dominant up to all three non-uniformity parameters per pixel in the digital signal chain of a camera.

Today's digital image sensors are used as passive photon integrators and image processing is essentially performed by digital processors separated from the image sensing part. This approach imposes to the processing part to deal with already grabbed pictures with possible unadjusted exposition parameters. This paper presents a fast self-adaptable preprocessing architecture with fast feedbacks on the sensing level. These feedbacks are controlled by digital processing in order to modify the sensor parameters during exposure time. Exposition and processing parameters are tuned in real life to fit with applications requirement depending on scene parameters. Considering emerging integration technologies such as 3D stacking, this paper presents an innovative way of designing smart vision sensors, integrating feedback control and opening new approaches for machine vision architectures.

The domain of the low light imaging systems progresses very fast, thanks to detection and electronic multiplication technology evolution, such as the emCCD (electron multiplying CCD) or the ebCMOS (electron bombarded CMOS). We present an ebCMOS camera system that is able to track every 2 ms more than 2000 targets with a mean number of photons per target lower than two. The point light sources (targets) are spots generated by a microlens array (Shack-Hartmann) used in adaptive optics. The Multiple-Target-Tracking designed and implemented on a rugged workstation is described. The results and the performances of the system on the identification and tracking are presented and discussed.

This work aims to design a filter to attenuate high- and medium- frequency noise in optical test images without changing the edges and original characteristics of the test image, generated by traditional filters (spatial or frequential). The noise produced by the LCD pixels (used as a diffraction grating in the Ronchi test) was analyzed. The diffraction is modulated by the spherical wavefront of the mirror under test, generating at least two frequency band noise levels. To reduce this bi-frequential noise, we propose to use an array of filters with the following structure: a low-pass frequential filter LPFF, a band- pass frequential filter BPFF and a circular mask spatial filter CMSF; thus obtaining the composed filter CF=LPFF-(BPFF)(CMSF). Various sizes of filters were used to compare their signal-to-noise ratio against simple filters (low-pass and band-stop).

This paper provides a brief outline of the approaches to modeling human visual attention. Bottom-up and top-down mechanisms are described together with some of the problems that they face. It has been suggested in brain science that memory functions by trading measurement precision for associative power; sensory inputs from the environment are never identical on separate occasions, but the associations with memory compensate for the differences. A graphical representation for image similarity is described that relies on the size of maximally associative structures (cliques) that are found to reflect between pairs of images. This is applied to the recognition of movie posters, the location and recognition of characters, and the recognition of faces. The similarity mechanism is shown to model popout effects when constraints are placed on the physical separation of pixels that correspond to nodes in the maximal cliques. The effect extends to modeling human visual behaviour on the Poggendorff illusion.

Motion is known to play a fundamental role in attentional capture, still it is not always included in computational models of visual attention. A wealth of literature in the past years has investigated natural image statistics at the centre of gaze to assess static low-level features accounting for fixation capture on images. A motion counterpart describing which features trigger saccades on dynamic scenes has been less looked into, whereas it would provide significant insight on the visuomotor behaviour when attending to events instead of less realistic still images. Such knowledge would be paramount to devise active vision systems that can spot interesting or malicious activities and disregard less relevant patterns. In this paper, we present an analysis of spatiotemporal features at the future centre of gaze to extract possible regularities in the fixation distribution to contrast with the feature distribution of non-fixated points. A substantial novelty in the methodology is the evaluation of the features in a gaze-contingent reference. Each video sequence fragment is indeed foveated with respect to the current fixation, while features are collected at the next saccade landing point. This allows us to estimate covertly selected motion cues in a retinotopic fashion. We consider video sequences and eye-tracking data from a recent state-of-the art dataset and test a bottom-up motion saliency measure against human performance. Obtained results can be used to further tune saliency computational models and to learn to predict human fixations on video sequences or generate meaningful shifts of active sensors in real world scenarios.

Visual attention deployment mechanisms allow the Human Visual System to cope with an overwhelming amount of visual data by dedicating most of the processing power to objects of interest. The ability to automatically detect areas of the visual scene that will be attended to by humans is of interest for a large number of applications, from video coding, video quality assessment to scene understanding. Due to this fact, visual saliency (bottom-up attention) models have generated significant scientific interest in recent years. Most recent work in this area deals with dynamic models of attention that deal with moving stimuli (videos) instead of traditionally used still images. Visual saliency models are usually evaluated against ground-truth eye-tracking data collected from human subjects. However, there are precious few recently published approaches that try to learn saliency from eyetracking data and, to the best of our knowledge, no approaches that try to do so when dynamic saliency is concerned. The paper attempts to fill this gap and describes an approach to data-driven dynamic saliency model learning. A framework is proposed that enables the use of eye-tracking data to train an arbitrary machine learning algorithm, using arbitrary features derived from the scene. We evaluate the methodology using features from a state-of-the art dynamic saliency model and show how simple machine learning algorithms can be trained to distinguish between visually salient and non-salient parts of the scene.

Providing real time analysis of the huge amount of data generated by computer vision algorithms in interactive applications is still an open problem. It promises great advances across a wide variety of fields. When using dynamics scene analysis algorithms for computer vision, a trade-off must be found between the quality of the results expected, and the amount of computer resources allocated for each task. It is usually a design time decision, implemented through the choice of pre-defined algorithms and parameters. However, this way of doing limits the generality of the system. Using an adaptive vision system provides a more flexible solution as its analysis strategy can be changed according to the new information available. As a consequence, such a system requires some kind of guiding mechanism to explore the scene faster and more efficiently. We propose a visual attention system that it adapts its processing according to the interest (or salience) of each element of the dynamic scene. Somewhere in between hierarchical salience based and competitive distributed, we propose a hierarchical yet competitive and non salience based model. Our original approach allows the generation of attentional focus points without the need of neither saliency map nor explicit inhibition of return mechanism. This new realtime computational model is based on a preys / predators system. The use of this kind of dynamical system is justified by an adjustable trade-off between nondeterministic attentional behavior and properties of stability, reproducibility and reactiveness.

Analysis of vibrations and displacements is a hot topic in structural engineering. Video cameras can provide good accuracy at reasonable cost. Proper system configuration and adequate image processing algorithms provide a reliable method for measuring vibrations and displacements in structures. In this communication we propose using a pocket camera (Casio) for measuring small vibrations and displacements. Low end cameras can acquire high speed video sequences at very low resolutions. Nevertheless, many applications do not need precise replication of the scene, but detecting its relative position. By using targets with known geometrical shapes we are able to mathematically obtain subpixel information about its position and thus increase the system resolution. The proposal is demonstrated by using circular and elliptic targets on moving bodies The used method combines image processing and least squares fitting and the obtained accuracy multiplies by 10 the original resolution. Results form the low-end camera (400 euros) working at 224×168 px are compared with those obtained with a high-end camera (10000 euros) with a spatial resolution of 800×560 px. Although the low-end camera introduces a lot of noise in the detected trajectory, we obtained that results are comparable. Thus for particular applications, low-end pocket cameras can be a real alternative to more sophisticated and expensive devices.

Multi-exposure image fusion algorithms are used for enhancing the perceptual quality of an image captured by sensors of limited dynamic range. This is achieved by rendering a single scene based on multiple images captured at different exposure times. Similarly, multi-focus image fusion is used when the limited depth of focus on a selected focus setting of a camera results in parts of an image being out of focus. The solution adopted is to fuse together a number of multi-focus images to create an image that is focused throughout. In this paper we propose a single algorithm that can perform both multi-focus and multi-exposure image fusion. This algorithm is a novel approach in which a set of unregistered multiexposure/ focus images is first registered before being fused. The registration of images is done via identifying matching key points in constituent images using Scale Invariant Feature Transforms (SIFT). The RANdom SAmple Consensus (RANSAC) algorithm is used to identify inliers of SIFT key points removing outliers that can cause errors in the registration process. Finally we use the Coherent Point Drift algorithm to register the images, preparing them to be fused in the subsequent fusion stage. For the fusion of images, a novel approach based on an improved version of a Wavelet Based Contourlet Transform (WBCT) is used. The experimental results as follows prove that the proposed algorithm is capable of producing HDR, or multi-focus images by registering and fusing a set of multi-exposure or multi-focus images taken in the presence of camera shake.

Wavefront sensorless adaptive optics methodologies are considered in many applications where the deployment of a dedicated wavefront sensor is inconvenient, such as in fluorescence microscopy. In these methodologies, aberration correction is achieved by sequentially changing the settings of the adaptive optical element until a predetermined imaging quality metric is optimised. Reducing the time required for this optimisation is a challenge. In this paper, a two stage data driven optimisation procedure is presented and validated in a laboratory environment. In the first stage, known aberrations are introduced by a deformable mirror and the corresponding intensities are measured by a photodiode masked by a pinhole. A generic quadratic metric is fitted to this collection of aberrations and intensity measurements. In the second stage, this quadratic metric is used in order to estimate and correct for optical aberrations. A closed form expression for the optimisation of the quadratic metric is derived by solving a linear system of equations. This requires a minimum of N +1 pairs of deformable mirror settings and intensity measurements, where N is the number of modes of the aberrations.

Fatigue and distraction effects in drivers represent a great risk for road safety. For both types of driver behavior problems, image analysis of eyes, mouth and head movements gives valuable information. We present in this paper a system for monitoring fatigue and distraction in drivers by evaluating their performance using image processing. We extract visual features related to nod, yawn, eye closure and opening, and mouth movements to detect fatigue as well as to identify diversion of attention from the road. We achieve an average of 98.3% and 98.8% in terms of sensitivity and specificity for detection of driver's fatigue, and 97.3% and 99.2% for detection of driver's distraction when evaluating four video sequences with different drivers.

In recent years, the amount of digitally captured traces at crime scenes increased rapidly. There are various kinds of such traces, like pick marks on locks, latent fingerprints on various surfaces as well as different micro traces. Those traces are different from each other not only in kind but also in which information they provide. Every kind of trace has its own properties (e.g., minutiae for fingerprints, or raking traces for locks) but there are also large amounts of metadata which all traces have in common like location, time and other additional information in relation to crime scenes. For selected types of crime scene traces, type-specific databases already exist, such as the ViCLAS for sexual offences, the IBIS for ballistic forensics or the AFIS for fingerprints. These existing forensic databases strongly differ in the trace description models. For forensic experts it would be beneficial to work with only one database capable of handling all possible forensic traces acquired at a crime scene. This is especially the case when different kinds of traces are interrelated (e.g., fingerprints and ballistic marks on a bullet casing). Unfortunately, current research on interrelated traces as well as general forensic data models and structures is not mature enough to build such an encompassing forensic database. Nevertheless, recent advances in the field of contact-less scanning make it possible to acquire different kinds of traces with the same device. Therefore the data of these traces is structured similarly what simplifies the design of a general forensic data model for different kinds of traces. In this paper we introduce a first common description model for different forensic trace types. Furthermore, we apply for selected trace types from the well established database schema development process the phases of transferring expert knowledge in the corresponding forensic fields into an extendible, database-driven, generalised forensic description model. The trace types considered here are fingerprint traces, traces at locks, micro traces and ballistic traces. Based on these basic trace types, also combined traces (multiple or overlapped fingerprints, fingerprints on bullet casings, etc) and partial traces are considered.

The technology-aided support of forensic experts while investigating crime scenes and collecting traces becomes a more and more important part in the domains of image acquisition and signal processing. The manual lifting of latent fingerprints using conventional methods like the use of carbon black powder is time-consuming and very limited in its scope of application. New technologies for a contact-less and non-invasive acquisition and automatic processing of latent fingerprints, promise the possibilities to inspect much more and larger surface areas and can significantly simplify and speed up the workflow. Furthermore, it allows multiple investigations of the same trace, subsequent chemical analysis of the residue left behind and the acquisition of latent fingerprints on sensitive surfaces without destroying the surface itself. In this work, a FRT MicroProf200 surface measurement device equipped with a chromatic white-light sensor CWL600 is used. The device provides a gray-scale intensity image and 3D-topography data simultaneously. While large area scans are time-consuming, the detection and localization of finger traces are done based on low-resolution scans. The localized areas are scanned again with higher resolution. Due to the broad variety of different surface characteristics the fingerprint pattern is often overlaid by the surface structure or texture. Thus, image processing and classification techniques are proposed for validation and visualization of ridge lines in high-resolution scans. Positively validated regions containing complete or sufficient partial fingerprints are passed on to forensic experts. The experiments are provided on a set of three surfaces with different reflection and texture characteristics, and fingerprints from ten different persons.

When an individual carries an object, such as a briefcase, conventional gait recognition algorithms based on average silhouette/Gait Energy Image (GEI) do not always perform well as the object carried may have the potential of being mistakenly regarded as a part of the human body. To solve such a problem, in this paper, instead of directly applying GEI to represent the gait information, we propose a novel dynamic feature template for classification. Based on this extracted dynamic information and some static feature templates (i.e., head part and trunk part), we cast gait recognition on the large USF (University of South Florida) database by adopting a static/dynamic fusion strategy. For the experiments involving carrying condition covariate, significant improvements are achieved when compared with other classic algorithms.

In future applications of contactless acquisition techniques for latent fingerprints the automatic localization of potential fingerprint traces in crime scenes is required. Our goal is to study the application of a camera-based approach1 comparing with the performance of chromatic white light (CWL) techniques2 for the latent fingerprint localization in coarse and the resulting acquisition using detailed scans. Furthermore, we briefly evaluate the suitability of the camera-based acquisition for the detection of malicious fingerprint traces using an extended camera setup in comparison to Kiltz et al.3 Our experimental setup includes a Canon EOS 550D4 digital single-lens reflex (DSLR) camera and a FRT MicroProf2005 surface measurement device with CWL6002 sensor. We apply at least two fingerprints to each surface in our test set with 8 different either smooth, textured and structured surfaces to evaluate the detection performance of the two localization techniques using different pre-processing and feature extraction techniques. Printed fingerprint patterns as reproducible but potentially malicious traces3 are additionally acquired and analyzed on foil and compact discs. Our results indicate positive tendency towards a fast localization using the camera-based technique. All fingerprints that are located using the CWL sensor are found using the camera. However,the disadvantage of the camera-based technique is that the size of the region of interest for the detailed scan for each potential latent fingerprint is usually slightly larger compared to the CWL-based localization. Furthermore, this technique does not acquire 3D data and the resulting images are distorted due to the necessary angle between the camera and the surface. When applying the camera-based approach, it is required to optimize the feature extraction and classification. Furthermore, the required acquisition time for each potential fingerprint needs to be estimated to determine the time-savings of the camera-based localization approach throughout the entire acquisition of traces. The analysis of camera images of printed fingerprint patterns shows positive tendencies, too. However, only small sections of the fingerprint are sharply acquirable within a single photo, large sections of the image are usually blurred due to the depth of field of the camera lens.

In the field of crime scene forensics, current methods of evidence collection, such as the acquisition of shoe-marks, tireimpressions, palm-prints or fingerprints are in most cases still performed in an analogue way. For example, fingerprints are captured by powdering and sticky tape lifting, ninhydrine bathing or cyanoacrylate fuming and subsequent photographing. Images of the evidence are then further processed by forensic experts. With the upcoming use of new multimedia systems for the digital capturing and processing of crime scene traces in forensics, higher resolutions can be achieved, leading to a much better quality of forensic images. Furthermore, the fast and mostly automated preprocessing of such data using digital signal processing techniques is an emerging field. Also, by the optical and non-destructive lifting of forensic evidence, traces are not destroyed and therefore can be re-captured, e.g. by creating time series of a trace, to extract its aging behavior and maybe determine the time the trace was left. However, such new methods and tools face different challenges, which need to be addressed before a practical application in the field. Based on the example of fingerprint age determination, which is an unresolved research challenge to forensic experts since decades, we evaluate the influences of different environmental conditions as well as different types of sweating and their implications to the capturing sensory, preprocessing methods and feature extraction. We use a Chromatic White Light (CWL) sensor to exemplary represent such a new optical and contactless measurement device and investigate the influence of 16 different environmental conditions, 8 different sweat types and 11 different preprocessing methods on the aging behavior of 48 fingerprint time series (2592 fingerprint scans in total). We show the challenges that arise for such new multimedia systems capturing and processing forensic evidence

Innovation in multimedia systems impacts on our society. For example surveillance camera systems combine video and audio information. Currently a new sensor for capturing fingerprint traces is being researched. It combines greyscale images to determine the intensity of the image signal, on one hand, and topographic information to determine fingerprint texture on a variety of surface materials, on the other. This research proposes new application areas which will be analyzed from a technical-legal view point. It assesses how technology design can promote legal criteria of German and European privacy and data protection. For this we focus on one technology goal as an example.

The free space optical links have found their major application in today's technological society. The demand for quality broadband is a must for all types of end users in these times. Because of the large jamming from wireless radio networks in non-licensed ISM bands, the free space optical links provide bridging of some densely populated urban areas. Their advantage is the high transmission rate for relatively long distances. However, the disadvantage is the dependence of free space optical links on atmospheric influences. Aired collimated optical beam passes through the atmospheric transmission environment and by its influence cause the deformation of the optical beam. Author's team decided to construct a special measuring device for measurement of optical power in FSO link beam cross-section. The equipment is mobile and can be rearranged and adjust according to the given location and placement of the FSO link at any time. The article describes the individual structural elements of the measuring equipment, its controlling and application for evaluation and adjustment of measuring steps. The graphs from optical power measurements in the beam cross-section of professional FSO links are presented at the end.

Gradient-index (GRIN) lenses have excellent optical properties that are not generally observed in the case of homogeneous lenses. Hence, GRIN lenses are used to fabricate new optical elements that have promising applications in optical information processing and optical communication. For example, it is widely used for scanner, fax machines and copiers etc. One of the low cost fabricating methods of these lenses involves pulling up the core fiber vertically from a polymer solution whose refractive index has been adjusted to the desired value. But in fact, the refractive-index distribution is not ideal because of several factors in manufacturing. When a GRIN lens has the refractive-index distribution which is not ideal, it degrades modulation transfer function (MTF) extremely. In this paper, we studied the picture reconstruction by using Bayes' theorem. Bayes' theorem is applied to the degraded picture obtained in an experiment with the plastic rod lens, and as a result MTF has extremely improved. First, spatial distribution of point spread function (PSF) is calculated from the refractive index distribution inside a rod lens. The 4096 PSFs of spatial distributions are obtained by this calculation. By applying image processing using the Bayes' theorem, MTF becomes about 92.9% after the application, even if MTF is 23.3%. These researches show that Bayes' theorem is very effective in image restoration.

The method to do an shift multiplexing by using spherical reference light was examined. The density growth can be expected by overwriting the hologram using spherical reference light. The hologram recording is carried out by shifting the block where the multiplexed hologram was recorded. In addition, a further large capacity can be expected by using the transmission type together with the reflection type hologram recording. In this paper, the result of verifying fundamental proof of these methods was reported by the record reproduction experiment.

A compact optical correlator that can retrieve shape, color, and texture information was improved and optimized for medical applications. Some optical components of the optical correlator were changed to eliminate stray light. Tumor and normal cell images from rats can be clearly distinguished by using their color and luminance information. Here, the color and luminance data from the cell images were converted into two-dimensional patterns on the x-y chromaticity diagram and the luminance histogram, respectively. The tumor cell images were clearly distinguished from large numbers of cells by retrieving the color and luminance patterns. Based on these results, we have demonstrated that our optical correlator is an effective tool for retrieval of complicated large volume information, such as that of cell images.

This article deals with spectral characteristics measurement of fiber couplers which are used for FTTx networks. Due to WDM systems we are able to communicate with several wavelengths at a time. In xPON systems the data transmission runs at wavelengths 1310 nm, 1490 and 1550 nm, in case of using singlemode fibers, or at 850 nm and 1300 nm in case of using multimode fibers. The target of this work is a testing how the individual parameters of fiber coupler behave whether broad spectrum light source is connected to the input. In sum it was measured four most often used fiber couplers, fiber coupler in port configuration 1x2 with coupling ratio 50/50%, fiber coupler in port configuration 1x2 with coupling ratio 30/70%, fiber coupler in port configuration 1x2 with coupling ratio 10/90% and fiber coupler in port configuration 1×4 with coupling ratio 4×25%. For these fiber couplers it was set insertion losses, coupling ratios, homogeneities and total losses by using a broad spectrum light source. The results are valuable information for companies which deal with optical networks.

Image change detection in the presence of unknown non-uniform intensity transformations is addressed. Multiplicative and additive uniform intensity transformations in image difference are solved using operators based on local vector space geometry. We now generalize this method for situations where a linear intensity gradient across the image can be present. This situation can occur in active illumination systems or when curved surfaces are illuminated from a source at an angle away from the line of sight. The method proposed is based on calculating geometrical functions by projecting the local vectors upon a certain subspace defined by the reference. Various experiments are carried out to confirm the correct image detection. In addition, all difference local operators are defined in terms of correlations which can be useful for optical implementations using conventional Vander Lugt or joint transform correlators.

Translucent fabrics transmit light but provide sufficient diffusion to eliminate perception of distinct images. Traditional translucent woven fabrics include organdie, silk chiffon and muslin cotton, among many others. When such materials are used in curtains they provide even light transmission, solar protection and natural lighting, with a wide range of degrees of view-through. Since all these properties can be referred to the concepts of privacy, space and boundary, they are highly appreciated in interior design and architecture. The conventional metrics used to characterize a translucent fabric are the UV or Visible light transmission, the cover factor and the shading coefficient. In this work we propose to use other metrics commonly utilized to characterize imaging systems such as the Modulation Transfer Function (MTF). When looking through a curtain, the translucent fabric can be modeled as a low-pass filter that is combined with human eye imaging system. We replace our visual system by a high-quality still photographic camera. The MTF curve allows one to characterize the view-through performance of the translucent fabric in a more realistic way than the simple light transmittance, cover factor or shading coefficient. Two object tests, placed at a distance from the fabric, have been used to experimentally derive the MTF of the whole imaging system (translucent fabric and camera): a USAF test and a Slanted Edge (ES) test. In the latter case the Line Spread Function (LSF) is firstly obtained and the MTF estimated. The method has been applied to a set of translucent fabrics with different thread diameters and densities. From the MTF curves obtained using both tests, the transparency of the fabrics is objectively and quantitatively characterized in terms of view-through. The results are presented and discussed.

Digitization of existing documents containing images is an important body of work for many archives ranging from individuals to institutional organizations. The methods and file formats used in this digitization is usually a trade off between budget, file volume size and image quality, while not necessarily in this order. The use of most commons and standardized file formats, JPEG and TIFF, prompts the operator to decide the compression ratio that affects both the final file volume size and the quality of the resulting image version. The evaluation of the image quality achieved by a system can be done by means of several measures and methods, being the Modulation Transfer Function (MTF) one of most used. The methods employed by the compression algorithms affect in a different way the two basic features of the image contents, edges and textures. Those basic features are too differently affected by the amount of noise generated at the digitization stage. Therefore, the target used in the measurement should be related with the features usually presents in general imaging. This work presents a comparison between the results obtained by measuring the MTF of images taken with a professional camera system and saved in several file formats compression ratios. In order to accomplish with the needs early stated, the MTF measurement has been done by two separate methods using the slanted edge and dead leaves targets respectively. The measurement results are shown and compared related with the respective file volume size.

The goal of this paper is to simulate the functionality of a digital camera system. The simulations cover the conversion from light to numerical signal and the color processing and rendering. We consider the image acquisition system to be linear shift invariant and axial. The light propagation is orthogonal to the system. We use a spectral image processing algorithm in order to simulate the radiometric properties of a digital camera. In the algorithm we take into consideration the transmittances of the: light source, lenses, filters and the quantum efficiency of a CMOS (complementary metal oxide semiconductor) sensor. The optical part is characterized by a multiple convolution between the different points spread functions of the optical components. We use a Cooke triplet, the aperture, the light fall off and the optical part of the CMOS sensor. The electrical part consists of the: Bayer sampling, interpolation, signal to noise ratio, dynamic range, analog to digital conversion and JPG compression. We reconstruct the noisy blurred image by blending different light exposed images in order to reduce the photon shot noise, also we filter the fixed pattern noise and we sharpen the image. Then we have the color processing blocks: white balancing, color correction, gamma correction, and conversion from XYZ color space to RGB color space. For the reproduction of color we use an OLED (organic light emitting diode) monitor. The analysis can be useful to assist students and engineers in image quality evaluation and imaging system design. Many other configurations of blocks can be used in our analysis.

Texture analysis has become an important task in image processing because it is used as a preprocessing stage in different research areas including medical image analysis, industrial inspection, segmentation of remote sensed imaginary, multimedia indexing and retrieval. In order to extract visual texture features a texture image analysis technique is presented based on the Hermite transform. Psychovisual evidence suggests that the Gaussian derivatives fit the receptive field profiles of mammalian visual systems. The Hermite transform describes locally basic texture features in terms of Gaussian derivatives. Multiresolution combined with several analysis orders provides detection of patterns that characterizes every texture class. The analysis of the local maximum energy direction and steering of the transformation coefficients increase the method robustness against the texture orientation. This method presents an advantage over classical filter bank design because in the latter a fixed number of orientations for the analysis has to be selected. During the training stage, a subset of the Hermite analysis filters is chosen in order to improve the inter-class separability, reduce dimensionality of the feature vectors and computational cost during the classification stage. We exhaustively evaluated the correct classification rate of real randomly selected training and testing texture subsets using several kinds of common used texture features. A comparison between different distance measurements is also presented. Results of the unsupervised real texture segmentation using this approach and comparison with previous approaches showed the benefits of our proposal.

The analysis of latent fingerprint patterns generally requires clearly recognizable friction ridge patterns. Currently, overlapping latent fingerprints pose a major problem for traditional crime scene investigation. This is due to the fact that these fingerprints usually have very similar optical properties. Consequently, the distinction of two or more overlapping fingerprints from each other is not trivially possible. While it is possible to employ chemical imaging to separate overlapping fingerprints, the corresponding methods require sophisticated fingerprint acquisition methods and are not compatible with conventional forensic fingerprint data. A separation technique that is purely based on the local orientation of the ridge patterns of overlapping fingerprints is proposed by Chen et al. and quantitatively evaluated using off-the-shelf fingerprint matching software with mostly artificially composed overlapping fingerprint samples, which is motivated by the scarce availability of authentic test samples. The work described in this paper adapts the approach presented by Chen et al. for its application on authentic high resolution fingerprint samples acquired by a contactless measurement device based on a Chromatic White Light (CWL) sensor. An evaluation of the work is also given, with the analysis of all adapted parameters. Additionally, the separability requirement proposed by Chen et al. is also evaluated for practical feasibility. Our results show promising tendencies for the application of this approach on high-resolution data, yet the separability requirement still poses a further challenge.

The behaviour of a construction safety net and its supporting structure was monitored with a high speed camera and image processing techniques. A 75 kg cylinder was used to simulate a falling human body from a higher location in a sloped surface of a building under construction. The cylinder rolled down over a ramp until it reaches the net. The behaviour of the net and its supporting structure was analysed through the movement of the cylinder once it reaches the net. The impact was captured from a lateral side with a high speed camera working at 512 frames per second. In order to obtain the cylinder position each frame of the sequence was binarized. Through morphological image processing the contour of the cylinder was isolated from the background and with a Hough transform the presence of the circle was detected. With this, forces and accelerations applying on the net and the supporting structure have been described, together with the trajectory of the cylinder. All the experiment has been done in a real structure in outdoors location. Difficulties found in the preparation on the experiment and in extracting the final cylinder contour are described and some recommendations are giving for future implementations.

In the field of latent fingerprint detection in crime scene forensics the classification of surfaces has importance. A new method for the scientific analysis of image based information for forensic science was investigated in the last years. Our image acquisition based on a sensor using Chromatic White Light (CWL) with a lateral resolution up to 2 μm. The used FRT-MicroProf 200 CWL 600 measurement device is able to capture high-resolution intensity and topography images in an optical and contact-less way. In prior work, we have suggested to use 2D surface texture parameters to classify various materials, which was a novel approach in the field of criminalistic forensic using knowledge from surface appearance and a chromatic white light sensor. A meaningful and useful classification of different crime scene specific surfaces is not existent. In this work, we want to extend such considerations by the usage of fourteen 3D surface parameters, called 'Birmingham 14'. In our experiment we define these surface texture parameters and use them to classify ten different materials in this test set-up and create specific material classes. Further it is shown in first experiments, that some surface texture parameters are sensitive to separate fingerprints from carrier surfaces. So far, the use of surface roughness is mainly known within the framework of material quality control. The analysis and classification of the captured 3D-topography images from crime scenes is important for the adaptive preprocessing depending on the surface texture. The adaptive preprocessing in dependency of surface classification is necessary for precise detection because of the wide variety of surface textures. We perform a preliminary study in usage of these 3D surface texture parameters as feature for the fingerprint detection. In combination with a reference sample we show that surface texture parameters can be an indication for a fingerprint and can be a feature in latent fingerprint detection.

This paper presents a novel system that makes effective use of High Dynamic Range (HDR) image data to improve and maintain the best viewing quality of video broadcast on current mobile display devices. The proposed approach combines bilateral filtering with an adaptive tone mapping method used to enable the enhancement of the perceptual quality of the video frames at the display device. The bilateral filter separates the frame into large-scale and detail layers. The large-scale layer is divided into bright, mid-tone and dark regions, which are each processed by an appropriate tone mapping function. Ambient and backlight sensors at the display device provide information about current illumination conditions, which are used to intelligently and dynamically vary the levels and thresholds of post-processing applied at the decoder, thereby maintaining a constant level of perceived quality.

This paper describes a segmentation method for time series of 3D cardiac images based on deformable models. The goal of this work is to extend active shape models (ASM) of tree-dimensional objects to the problem of 4D (3D + time) cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that encodes the spatio-temporal variability of a training set, i.e., the principal modes of variation of the temporal shapes are computed using some statistical parameters. An active search is used in the segmentation process where an initial approximation of the spatio-temporal shape is given and the gray level information in the neighborhood of the landmarks is analyzed. The starting shape is able to deform so as to better fit the data, but in the range allowed by the point distribution model. Several time series consisting of eleven 3D images of cardiac CT are employed for the method validation. Results are compared with manual segmentation made by an expert. The proposed application can be used for clinical evaluation of the left ventricle mechanical function. Likewise, the results can be taken as the first step of processing for optic flow estimation algorithms.

In this paper, we propose a detection method of low contrast structures in medical ultrasound images. Since noise speckle makes difficult the analysis of ultrasound images, two approaches based on the wavelet and Hermite-transforms for enhancement and noise reduction are compared. These techniques assume that speckle pattern is a random signal characterized by a Rayleigh distribution and affects the image as a multiplicative noise. For the wavelet-based approach, a Bayesian estimator at subband level for pixel classification is used. All the estimation parameters are calculated using an adjustment method derived from the first and second order statistical moments. The Hermite method computes a mask to find those pixels that are corrupted by speckle. In this work, we consider a statistical detection model that depends on the variable size and contrast of the image speckle. The algorithms have been evaluated using several real and synthetic ultrasound images. Combinations of the implemented methods can be helpful for automatic detection applications of tumors in mammographic ultrasound images. The employed filtering techniques are quantitatively and qualitatively compared with other previously published methods applied on ultrasound medical images.

This paper applies the Mach-Zehnder interferometer with an objective lens and an offset lens to magnify the wavefront of an object image, while compensating for the quadratic phase caused by the objective lens and achieving digital holographic microscopy. 1) The single-exposure method and simple arbitrary micro phase step (AMPS) approach are applied to suppress the zero-order and conjugate image interferences caused by holograph reconstruction. Through this process the best conditions for conjugate imaging suppression can be identified via the relative light intensity distribution and noise suppression of the numerically reconstructed object wavefront.

Modern textile industry seeks to produce textiles as little defective as possible since the presence of defects can decrease the final price of products from 45% to 65%. Automated visual inspection (AVI) systems, based on image analysis, have become an important alternative for replacing traditional inspections methods that involve human tasks. An AVI system gives the advantage of repeatability when implemented within defined constrains, offering more objective and reliable results for particular tasks than human inspection. Costs of automated inspection systems development can be reduced using modular solutions with embedded systems, in which an important advantage is the low energy consumption. Among the possibilities for developing embedded systems, the ARM processor has been explored for acquisition, monitoring and simple signal processing tasks. In a recent approach we have explored the use of the ARM processor for defects detection by implementing the wavelet transform. However, the computation speed of the preprocessing was not yet sufficient for real time applications. In this approach we significantly improve the preprocessing speed of the algorithm, by optimizing matrix operations, such that it is adequate for a real time application. The system was tested for defect detection using different defect types. The paper is focused in giving a detailed description of the basis of the algorithm implementation, such that other algorithms may use of the ARM operations for fast implementations.

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.