During routine use of flatbed scanners for grading lentils, unexpected short-term variability in scanner performance was noticed. This variability was detected as scan-to-scan differences, or repeatability. This study aims at developing an objective measure of scanner repeatability to facilitate the selection of scanners by establishing performance criteria for a scanner-based vision system. Four measures of scanner repeatability were compared, using seven scanners for two types of target objects. Scanner selection for grain grading applications is discussed. For grain grading, scanner repeatability is best characterized by color variations as measured by Eq. (4), which performed better than the other three measures of repeatability. The selection of the method of determining scanner repeatability, and the tolerance thereof, are dependent upon the constraints of the imaging targets.

Time delay and integration (TDI) is a technology used in line-scan cameras to improve moving image quality. As an image sweeps over the sensor array, the pixels collect charge; at certain intervals the charge in the wells in each of the rows is moved to the adjacent rows, in the same direction and velocity as the moving image. TDI sensors help provide high quality and contrast images even under low illumination providing the image speed is same as the speed of the charge movement. In this paper we model the TDI process by treating it as a discrete time sampler and, using this model, we develop several simple algorithms that are able to self-synchronize the TDI row charge movement based solely on the output of the TDI sensor itself rather than on an external encoder. The algorithms are simple enough to be implemented on a small size FPGA.

This work has been motivated by the rapid growth in the use of digital color imaging technology, together with the ever-increasing interchange of images over the internet. Users of these systems frequently have to make assumptions about the viewing conditions under which such images will be used, as well as the properties of the devices on which the images will be displayed. In this work, we focus on the way in which the displayed colors on a cathode ray tube (CRT) monitor are influenced by the gamma of the display. We have used a simple gain-offset-gain-type model of the performance of the CRT, with gain and offset values K₁ = 1.15, K₂ = –0.15, together with a set of standard assumptions regarding the display primaries and the white point setting, to enable us to simulate the influence of monitor gamma on the colors displayed. Color differences have been computed in the CIELAB color space. A total of 76 numerically defined color samples were used, of which 66 were chromatic samples and ten achromatic. They were subjected to the influence of eight different gamma values. A reference gamma of 2.2 was assumed in making colorimetric comparisons of the displayed colors for the different "test" gammas against the colors displayed under the reference gamma. The comparisons have been expressed as CIELAB color differences, and they show that the color shifts can be significant (up to 3.58 CIELAB units for a single simulated color sample with a gamma "discrepancy" of 0.1, and up to 9.90 CIELAB units when the gamma discrepancy is 0.3). These results are of concern in the context of the "correct" display of color reproductions—for example, in applications where an accurate impression or understanding of the displayed color is important.

In the conventional color reproduction based on the colorimetric match for a standard observer, color mismatch can be perceived if the color matching functions of the observer deviate from those of the standard observer; this phenomenon is known as observer metamerism. Recently, multi-primary display, using more than three-primary colors, has attracted attention as a color reproduction media because of its expanded gamut and its possibility to reduce the color mismatch caused by observer metamerism. In this paper, a new color conversion method for multi-primary display that reduces the observer metamerism is proposed. The proposed method gives the multi-dimensional control value of a display device to minimize the spectral approximation error under the constraints of tristimulus match. Reproduced spectrum by a seven-primary display is simulated and evaluated by the color matching functions of Stiles's 20 observers. The results confirmed that the proposed method reduces the color reproduction error caused by observer variability compared to the other seven-primary reproduction and conventional three-primary reproduction. The preliminary visual evaluation results with a seven-primary display using light-emitting diodes are also introduced.

Current display algorithms employ a wide variety of intensity mapping schemes to convert the 10–14 bit digital sensor data to 6–8 bits for display. These include histogram equalization, local area processing, and region of interest processing. Manual display mapping (MDM) differs from these algorithms because it allows the user to manipulate the displayed intensity of different regions of the sensor output by nonlinear mapping to pixel values. The user can thus allocate or "tune" pixel intensities (gray shades) to output regions expected to contain targets. Two experiments compare the identification (ID) performance of observers viewing low-resolution images and images blurred to simulate multiple ranges. In each experiment, images were processed with an automatic algorithm and MDM. Results indicate the expected MDM range performance improvement under typical infrared imaging conditions.

A new fixed-valued impulsive noise removal filter, entitled modified two-step iterative-median filter (MIMF), is proposed in this paper. The MIMF offers to use a statistical method in order to analyze the impulsive behavior of pixels. The experimental results show that the MIMF achieves superior restoration performance compared to the other filters mentioned in this paper with regard to noise suppression and detail preservation, especially when the noise density is very high.

We introduce the homotopic alternating sequential filter as a new method for smoothing two-dimensional (2D) and three-dimensional (3D) objects in binary images. Unlike existing methods, our method offers a strict guarantee of topology preservation. This property is ensured by the exclusive use of homotopic transformations defined in the framework of digital topology. Smoothness is obtained by the use of morphological openings and closings by metric disks or balls of increasing radius, in the manner of an alternating sequential filter. The homotopic alternating sequential filter operates both on the object and on the background, in an equilibrated way. It takes an original image X and a control image C as input, and smooths X "as much as possible" while respecting the topology of X and geometrical constraints implicitly represented by C. Based on this filter, we introduce a general smoothing procedure with a single parameter which allows to control the degree of smoothing. Furthermore, the result of this procedure presents small variations in response to small variations of the parameter value. We also propose a method with no parameter for smoothing zoomed binary images in 2D or 3D while preserving topology.

In this paper we present a new stochastic model for pixels in an edge-enhanced image. The model is robust because it allows for the possibilities of false and multiple edges, and may be efficiently estimated using an expectation-maximization technique with a minimum description length metric. The direct applicability of the model for the sequential edge linking algorithm is investigated and shown to improve edge detection for low signal-to-noise ratio cases.

Adaptive processes often attempt to minimize the mean square error (MSE) to filter a partially observed digital signal. While mathematically tractable, the MSE criterion often causes oversmoothing of the filtered signal. In this paper, we propose using maximum entropy (ME) as the optimization criterion to avoid the oversmoothing of signals. This criterion is motivated by the fact that ME methods make no assumptions regarding the unobserved data, aside from explicitly stated ones. The maximum entropy Kalman filter presented in this paper employs ME as its optimization criterion to explicitly identify the appropriate parameters of the standard Kalman filter, for the purpose of image compression and reconstruction.

The work described in this paper concerns directional structures detection for particular aspects of inspection, such as scratches and marbling defect detection in leather images, or for particular medical imaging problems, such as mammography analysis. Because of the very specific geometry of these structures, we apply a multiscale and orientation-shiftable method. Scratches and marbling have various shapes and sizes. In mammograms, stellate masses have an irregular appearance and are frequently surrounded by a radiating pattern of linear spicules. Multiscale approaches using oriented filters have proved to be efficient to detect both types of curvilinear patterns. The detection is based on steerable filters, which can be steered to any orientation fixed by the user, and are synthesized using a limited number of basic filters. These filters are used in a recursive multiscale transform: the steerable pyramid. Then, the curvilinear structures are extracted from the directional images at different scales.

Synthesizing new views from existing ones is an emerging field with various applications. Approaches for view synthesis rely upon dense or sparse matching between existing views. In all cases, some parts of the original images are inevitably unmatched and, therefore, the synthesis of new views of the corresponding regions need to rely upon sparse sets of matched points. Triangulation based upon sparsely matched points constitutes a possible solution for the handling of unmatched regions. However, such a triangulation should respect the underlying geometry of the corresponding scene. In this paper, a novel approach is proposed for a physically valid joint-triangulation of sparsely matched points. The proposed approach is based upon the maximization of a physical validity criterion which is supported by textured regions in the images. The produced triangulation is such that each triangle corresponds approximately to a planar surface in the scene. Given an arbitrary initial triangulation, the proposed approach refines it by modifying links between vertices so as to locally increase the physical validity measure. Furthermore, since missing or incorrect matched points may impede the correctness of the current triangulation, an iterative split-and-merge phase adds or removes matching points in low-score triangles, by means of a correctness evaluation based upon a large evaluation support. The paper presents results of the proposed approach with synthetic views—which allow quantitative performance evaluation—and natural images.

In this paper we present a genetic-algorithm-based fuzzy-logic approach for computer-aided diagnosis scheme in medical imaging. The scheme is applied to discriminate myocardial heart disease from echocardiographic images and to detect and classify clustered microcalcifications from mammograms. Unlike the conventional types of membership functions such as trapezoid, triangle, S curve, and singleton used in fuzzy reasoning, Gaussian-distributed fuzzy membership functions (GDMFs) are employed in the present study. The GDMFs are initially generated using various texture-based features obtained from reference images. Subsequently the shapes of GDMFs are optimized by a genetic-algorithm learning process. After optimization, the classifier is used for disease discrimination. The results of our experiments are very promising. We achieve an average accuracy of 96% for myocardial heart disease and accuracy of 88.5% at 100% sensitivity level for microcalcification on mammograms. The results demonstrated that our proposed genetic-algorithm-based fuzzy-logic approach is an effective method for computer-aided diagnosis in disease classification.

This paper proposes a method for estimating the three-dimensional (3D) rigid motion parameters from an image sequence of a moving object. The 3D surface measurement is achieved using an active stereovision system composed of a camera and a light projector, which illuminates the objects to be analyzed by a pyramid-shaped laser beam. By associating the laser rays with the spots in the two-dimensional image, the 3D points corresponding to these spots are reconstructed. Each image of the sequence provides a set of 3D points, which is modeled by a B-spline surface. Therefore, estimating the 3D motion between two images of the sequence boils down to matching two B-spline surfaces. We consider the matching environment as an optimization problem and find an optimal solution using genetic algorithms. A chromosome is encoded by concatenating seven binary coded parameters, the angle, and the three components of the rotation vector axis, and the three translation vector components. We have defined an original fitness function for calculating the similarity measure between two surfaces. Experimental results with real and synthetic image sequences are presented to show the effectiveness and the robustness of the method.

We propose a method for automatic target detection and tracking in forward-looking infrared (FLIR) image sequences. We use morphological connected operators to extract and track targets of interest and remove undesirable clutter. The design of these operators is based on general size, connectivity, and motion criteria, using spatial intraframe and temporal interframe information. In a first step, an image sequence is filtered on a frame-by-frame basis to remove background and residual clutter and to enhance the presence of targets. Detections extracted from the first step are passed to a second step for motion-based analysis. This step exploits the spatiotemporal correlation of the data, stated in terms of a connectivity criterion along the time dimension. The proposed method is suitable for piplined implementation or time progressive coding/transmission, since only a few frames are considered at a time. Experimental results, obtained with real FLIR image sequences, illustrating a wide variety of target and clutter variability, demonstrate the effectiveness and robustness of the proposed method.

This paper describes a method of numerical reconstruction for digital holography with changed viewing angles within the Fresnel domain. The proposed algorithm could not only reconstruct the real image of the original object with different focal lengths but also with changed viewing angles, which could be selected as any angle in space. Some representative simulations are presented to validate the proposed algorithm.

Dual beam electronic speckle interferometers provide raw data in the form of maps of wrapped relative phase or fringe patterns. Interpretation of such fringe patterns is complicated by aliased and random speckle noise. This noise can result in misidentification of the phase at a given point in the image. Automated determination of the loci of fringe extrema, useful for quantitative evaluation, are particularly affected. A nonlinear image filtering technique referred to as mean curvature diffusion is applied to overcome this difficulty. This technique essentially smooths the image without a substantial reduction in the magnitude of the underlying trends that here represent the fringes. Mean curvature diffusion uses calculations analogous to those for the diffusion of heat with the difference that the diffusion coefficient, reminiscent of thermal diffusivity, varies spatially within the image with a value given by the reciprocal of the local surface gradient. At a given point in the image, the rate of surface diffusion depends only on the average value of the normal curvature in any two orthogonal directions and not on its magnitude; this allows the lower frequency underlying components of the image structure to be retained. The method is tested on both calculated and real speckle interferograms to highlight the effectiveness of this smoothing technique relative to more standard smoothing algorithms.

We present a multiresolutional algorithm that segments a compound document and uses the results of the segmentation for document enhancement in copier applications. The document is initially segmented into halftone and nonhalftone areas. Based on this segmentation the location of the edges due to text, graphics, and images (and not due to halftone dots) are detected on halftone as well as on nonhalftone portions. We further detect constant-tone regions within nonhalftone areas for subsequent bleed-through removal applications. Edge enhancement on detected edges and descreening on detected halftones are carried out. The algorithm can detect general halftones over regions of arbitrary sizes and shapes, and it can be straightforwardly adjusted for operation at various dpi resolutions. We obtain high detection probabilities on compound multilingual documents containing halftones and fine text. The proposed enhancement stage is tolerant of segmentation errors providing robust performance for the remaining problem cases. Our main contribution is the accomplishment of these tasks with a single pass algorithm that is computationally very simple and that requires less than 1% of full page memory, with active memory requirements less than 0.02% of full page memory. The operation of the algorithm can be imagined as a very thin line (of thickness the size of a "full-stop" in 11 pt text) that rapidly scans an input page while simultaneously producing an output page.

Popular press and congressional record report a belief by the intelligence community that Al Qaeda members communicate through messages embedded invisibly in images shared via the Internet. This is certainly plausible as steganography has a rich history of military and civilian use. Current signature-based approaches for detecting the presence of hidden messages rely on discerning "footprints" of steganographic tools. Of greater recent concern is detecting the use of novel tools for which no signature has been established. This research addresses this concern by using a method for detecting anomalies in seemingly innocuous images, applying a genetic algorithm within a computational immune system to leverage powerful image processing through wavelet analysis. The sensors developed with this system demonstrated a surprising level of capability to detect the use of steganographic tools for which the system had no previous exposure, including one tool designed to be statistically stealthy.

In this paper, a novel image compression scheme based on the moment preserving block truncation coding (MPBTC) scheme is proposed. In this scheme, three techniques are employed to cut down the bit rate of moment preserving block truncation coding. They are the two-dimensional prediction technique, the bit map omission technique, and the bit map coding using edge patterns. According to the experimental results, the proposed scheme not only achieves good image quality at low bit rate, but also requires little computational cost in the encoding/decoding procedures. In other words, the proposed scheme indeed improves the performance of the MPBTC scheme and it is quite suitable for those multimedia applications with low computational cost requirement.

A predictive tree structure is presented for classifying the wavelet coefficients, and a new scheme is proposed to construct the trees based on rate distortion function, including both the optimal hierarchical quadtree construction and the predictive spatial orientation tree development. The full search quadtree optimization is applied first to the highest level of high frequency subbands, exploiting the intrasubband correlation of wavelet coefficients. The generated optimal quadtree serves as a predictor to construct the trees for other lower level subbands in which only the leaf nodes are to be analyzed in terms of the associated Lagrange costs for further expansion, taking advantage of the self-similarity across subbands. Constraining the full search quadtree optimization within the highest level subband reduces the computational complexity significantly. Simulation results indicate the proposed scheme is efficient and the performance of the system is comparable to some of the popular image compression techniques.

This paper presents a new wavelet-based video codec for very low bit rate coding, where each frame of the test sequence is transformed using discrete wavelet transformation. The hierarchical edge based motion estimation is applied between adjacent transformed frames. The aim is to reduce the computational complexity of a motion estimation algorithm and to preserve coding efficiency. The edge detection is incorporated into the motion estimation process in order to determine the moving regions. The edge is assumed as a point between two different moving areas in the proposed motion estimation algorithm. All transformed frames are quantized and prediction errors are coded using quantization and probability models designed with fuzzy logic. This proposed method is comparable with the H.26L video coding standard in the rate distortion sense, and has lower computational complexity than the H.26L standard.

JPEG2000 is the newest international standard to be produced by the Joint Photographic Experts Group (JPEG) and it has recently been published as an International Standards Organization/IEC standard and as an ITU-T recommendation. It complements most of the currently used standards including the original JPEG, usually with superior compression for the same image quality, or alternatively with superior image quality for the same bit rate. The algorithm uses the wavelet transform followed by an embedded block coder incorporating the MQ arithmetic coder. In this article we give a novel hardware architecture for the block coder and MQ coder as used in the JPEG2000 standard. Together with appropriate software, this design enables a high performance system-on-chip solution for JPEG2000 image compression. We have included sufficient introductory material to interest readers unfamiliar with either JPEG2000 or hardware design.

This PDF file contains the editorial “Color Space and Its Divisions: Color Order from Antiquity to the Present” for JEI Vol. 13 Issue 04