Image processing is a field of great interest for many applications. Nowadays it is very hard to name an
application where image processing is not involved. Digital techniques remains the dominant ones applied
to digital image processing with significant automation approaches that are built in image display, as in
most digital cameras and digital TVs, to name few. Depending on the application, digital image
processing techniques produces satisfactory accurate results. However, digital enhancement techniques
suffer from the main constraint: slow processing speed, an inherited problem associated with any digital
image processing technique. On the other hand optical image enhancement techniques such as the
polarization-based ones produce satisfactory accurate results and at the same time overcome the
processing time constraint associated with their digital counter ones. This paper presents a comparison
between digital and polarization-based enhancement/encoding techniques with respect to their accuracy,
security and processing time in automated pattern recognition applications.
Fingerprint recognition is one of the most commonly used forms of biometrics and has been widely used in daily life
due to its feasibility, distinctiveness, permanence, accuracy, reliability, and acceptability. Besides cost, issues related to
accuracy, security, and processing time in practical biometric recognition systems represent the most critical factors that
makes these systems widely acceptable. Accurate and secure biometric systems often require sophisticated enhancement
and encoding techniques that burdens the overall processing time of the system. In this paper we present a comparison
between common digital and optical enhancementencoding techniques with respect to their accuracy, security and
processing time, when applied to biometric fingerprint systems.
Fingerprint recognition is one of the first techniques used for automatically identifying people and today it is still one of
the most popular and effective biometric techniques. With this increase in fingerprint biometric uses, issues related to
accuracy, security and processing time are major challenges facing the fingerprint recognition systems. Previous work
has shown that polarization enhancementencoding of fingerprint patterns increase the accuracy and security of
fingerprint systems without burdening the processing time. This is mainly due to the fact that polarization
enhancementencoding is inherently a hardware process and does not have detrimental time delay effect on the overall
process. Unpolarized images, however, posses a high visual contrast and when fused (without digital enhancement)
properly with polarized ones, is shown to increase the recognition accuracy and security of the biometric system without
any significant processing time delay.
Pattern recognition deals with the detection and identification of a specific target in an unknown input scene. Target
features such as shape, color, surface dynamics, and material characteristics are common target attributes used for
identification and detection purposes. Pattern recognition using multispectral (MS), hyperspectral (HS), and polarization-based
spectral (PS) imaging can be effectively exploited to highlight one or more of these attributes for more efficient
target identification and detection. In general, pattern recognition involves two steps: gathering target information from
sensor data and identifying and detecting the desired target from sensor data in the presence of noise, clutter, and other
artifacts. Multispectral and hyperspectral imaging (MSI/HSI) provide both spectral and spatial information about the
target. As the reflection or emission spectral signatures depend on the elemental composition of objects residing within
the scene, the polarization state of radiation is sensitive to the surface features such as relative smoothness or roughness,
surface material, shapes and edges, etc. Therefore, polarization information imparted by surface reflections of the target
yields unique and discriminatory signatures which could be used to augment spectral target detection techniques, through
the fusion of sensor data. Sensor data fusion is currently being used to effectively recognize and detect one or more of
the target attributes. However, variations between sensors and temporal changes within sensors can introduce noise in the
measurements, contributing to additional target variability that hinders the detection process. This paper provides a quick
overview of target identification and detection using MSI/HSI, highlighting the advantages and disadvantages of each. It
then discusses the effectiveness of using polarization-based imaging in highlighting some of the target attributes at single
and multiple spectral bands using polarization spectral imaging (PSI), known as spectropolarimetry imaging.
Efficient recognition and clearance of subsurface land mine patterns has been one of the challenging humanitarian
and military tasks. Among the several subsurface land mine patterns recognition techniques available, passive
imaging techniques are more convenient, safer with good probability of recognition. There exist extensive
applications where the joint-transform correlation algorithms have been used for efficient pattern recognition.
However, among the several pattern recognition algorithms exist for subsurface land mines, the joint-transform
correlation ones has been underrepresented. This paper presents the application of an efficient wavelet-filter joint
transform correlation (WFJTC) algorithm for the recognition of passive imagery of subsurface land mines in highly
cluttered scenarios, using intensity and polarization-based imagery. We further improve the recognition efficiency of
the WFJTC proposing a combined optical-digital enhancement approach. Improvements will be justified using
correlation performance metrics.
A practical challenge that designers of hyperspectral (HS) target detection algorithms must confront is the variety of
spectral sampling properties exhibited by various HS imaging sensors. Examples of these variations include different
spectral resolutions and the possibility of regular or irregular sampling. To confront this problem, we propose
construction of a spectral synthetic discriminant signature (SSDS). The SSDS is constructed from q spectral training
signatures which are obtained by sampling the original target signature. Since the SSDS is formulated offline, it does not
impose any burden on the processing speed of the recognition process. Results on our HS scenery show that use of the
SSDS in conjunction with the spectral fringe-adjusted joint transform correlation (SFJTC) algorithm provides spectrallyinvariant
target detection, yielding area under ROC curve (AUROC) values above 0.993.
Fingerprints recognition systems have been widely used by financial institutions, law enforcement, border control, visa
issuing, just to mention few. Biometric identifiers can be counterfeited, but considered more reliable and secure
compared to traditional ID cards or personal passwords methods. Fingerprint pattern fusion improves the performance of
a fingerprint recognition system in terms of accuracy and security. This paper presents digital enhancement and fusion
approaches that improve the biometric of the fingerprint recognition system. It is a two-step approach. In the first step
raw fingerprint images are enhanced using high-frequency-emphasis filtering (HFEF). The second step is a simple linear
fusion process between the raw images and the HFEF ones. It is shown that the proposed approach increases the
verification and identification of the fingerprint biometric recognition system, where any improvement is justified using
the correlation performance metrics of the matching algorithm.
Recently, the use of imaging polarimetry has received considerable attention for use in automatic target recognition
(ATR) applications. In military remote sensing applications, there is a great demand for sensors that are capable of
discriminating between real targets and decoys. Accurate discrimination of decoys from real targets is a challenging
task and often requires the fusion of various sensor modalities that operate simultaneously. In this paper, we use a
simple linear fusion technique known as the high-boost fusion method for effective discrimination of real targets in the
presence of multiple decoys. The HBF assigns more weight to the polarization-based imagery in forming the final
fused image that is used for detection. We have captured both intensity and polarization-based imagery from an
experimental laboratory arrangement containing a mixture of sand/dirt, rocks, vegetation, and other objects for the
purpose of simulating scenery that would be acquired in a remote sensing military application. A target object and
three decoys that are identical in physical appearance (shape, surface structure and color) and different in material
composition have also been placed in the scene. We use the wavelet-filter joint transform correlation (WFJTC)
technique to perform detection between input scenery and the target object. Our results show that use of the HBF
method increases the correlation performance metrics associated with the WFJTC-based detection process when
compared to using either the traditional intensity or polarization-based images.
Detection and clearance of subsurface land mines has been one of the challenging humanitarian and military tasks.
Passive polarization-based imagery has played important role achieving this task. This paper presents new fusion
technique where polarization-based imagery is fused with traditional intensity imagery using high-boost approach.
The main idea of the high-boost approach used in this paper is to give the polarization imagery obtained from the
Stokes vector imagery more weight in forming the final fused image. It is shown that the proposed technique
improves the recognition of surface land mines. This improvement is shown using correlation performance metrics
derived from wavelet-filter joint-transform correlation algorithm used for pattern recognition.
Optical coherence tomography (OCT) is an interferometric, noninvasive and non contact imaging technique that
generates images of biological tissues at micrometer scale resolution. Images obtained from the OCT process are
often noisy and of low visual contrast level. This work focuses on improving the visual contrast of OCT images
using digital enhancement and fusion techniques. Since OCT images are often corrupted with noise, our first
approach is to use the most effective noise reduction algorithm. This process is followed by a series of digital
enhancement techniques that are suitable to enhance the visual contrast of the OCT images. We also investigate any
gain in visual contrast if combined enhancement is employed. In the image fusion methods, images taken at different
depths are fused together using discrete wavelet transform (DWT) and logical fusion algorithms. We answer the
question of it is more efficient to enhance images before or after fusion. This work concludes by suggesting future
work needed to complement the current one.
Fingerprint recognition applications as means of identity authentication that deals with accuracy and security are
becoming more acceptable in areas such as financial transactions, access to secured buildings, commercial driver license
and identity check at entry borders, to mention a few. This paper presented a new approach of using two patterns of the
same person, intelligently fused, to form a new unique pattern of the same person. The Laplacian pyramid (LP) level 7
image fusion approach and the logical "OR" and logical "AND" operators for the decision fusion approach were tested
with respect to their performance in accuracy, security and processing speed of the recognition system. The concept of
receiver operator characteristic (ROC) curve to indicate any improvement in accuracy and security of the process was
used.
Finally, an overall comparison and analysis of performance between traditional systems that used a single pattern
and our proposed system that used two fused fingerprint patterns in the biometric system was presented.
Several approaches have been presented to enhance the recognition capabilities of certain patterns. This is
particularly important in applications involve recognizing the identity of some individual accurately, especially in
critical applications such as border entry, access to secured buildings, and in financial transactions, among other
things. In this paper, we present an approach to improve the accuracy of a biometric recognition system using two
fused patterns (two fingers) of same classifier (individual). In this method, two or more fingers of the same classifier
are fused together to form a unique fingerprint pattern to be used in the recognition process. Techniques related to
pattern and decision fusions are tested. In particular, the logical AND operator used in the decision fusion algorithm
has resulted in a higher level of accuracy of recognition.
Efficient surface and subsurface land mine detection is still one of the several important tasks in the
civilian and military areas. Often information derived from multiple sensors are fused together to lead a
net gain in probability of detection of these land mines. Some drawbacks of this approach are the different
sensor output formats and the overall processing efficiency of the system. To overcome some of the
aforementioned concerns we propose a simple and efficient image fusion using logical operators. The
proposed technique uses logical operators to fuse multiple images derived from a single sensor. The
logical fusion operation is applied to the output of the sensor to lead a logically fused image. This image
serves as an input to the detection algorithm. The proposed technique is shown to improve the probability
of detection of land mines. This improvement is shown using the ROC curve approach.
KEYWORDS: Land mines, Image fusion, Sensors, Mining, Polarization, Polarimetry, General packet radio service, Detection and tracking algorithms, Infrared radiation, Electromagnetism
Detection and clearance of subsurface land mines has been one of the challenging humanitarian and
military tasks. Among the several surface land mines detection techniques available, passive polarimetric
in the visible range holds a high promise to lead a high probability of detection. In this paper we show
how a single sensor producing multiple polarized and unpolarized imagery can be used to improve the
probability of detection of subsurface land mines. This improvement is shown using the Receiver
Operation Characteristic (ROC) curve approach.
The use of Fisher ratio (FR) algorithm to predict a pattern in an input seen has been applied in several
applications in the literature with different success rate, depending on how close is the similarity of
the statistical parameters between the background and the patterns. We propose a modification to the
FR ratio algorithm that is dependent on the probability density function (PDF). The modified PDF-FR
algorithm provides good improvements over that of the PDF used alone. We further enhance the
performance of the PDF-FR using polarization-enhanced imagery.
An efficient multiple target recognition technique which combines the inherited enhancement of the optical polarization field with the feature enhancement of the wavelet filter is proposed in this paper. The wavelet filter is superimposed on the joint power spectrum before the correlation output is produced. It is shown that the proposed technique yields cumulative target discrimination capabilities for automatic single/multiple target recognition applications.
Important aspects of automatic pattern recognition systems are their ability to efficiently discriminate and detect proper targets with low false alarms. In this paper we extend the applications of passive imaging polarimetry to effectively discriminate and detect different color targets of identical shapes using color-blind imaging sensor. For this case of study we demonstrate that traditional color-blind polarization-insensitive imaging sensors that rely only on the spatial distribution of targets suffer from high false detection rates, especially in scenarios where multiple identical shape targets are present. On the other hand we show that color-blind polarization-sensitive imaging sensors can successfully and efficiently discriminate and detect true targets based on their color only. We highlight the main advantages of using our proposed polarization-encoded imaging sensor.
We present a novel synthetic discriminant function (SDF) formulated from the Laplacian-enhanced (L) training images for the rotation and scale invariant target detection. It is shown that the proposed LSDF yields significantly improved correlation performance compared to the traditional SDF. Since the LSDF is formulated off line, it does not have any burden on the processing speed of the system.
We have previously shown that polarization enhancement of fingerprint images during the enrolment process improves the performance of the verification and identification processes. In this paper, we present a design and analysis of a new synthetic discriminant function (SDF) for rotation/scale invariant polarization-enhanced fingerprint system. Performance comparison between the proposed SDF and an SDF obtained for traditional fingerprint systems is included.
In previous work we showed that polarization-enhancement/encoding imagery can effectively be used to improve the detection and discrimination of targets. In this paper, we present a design and analysis of a new synthetic discriminant function for rotation/scale invariant for this polarization imagery system. Performance comparison between the proposed SDF and SDF obtained in traditional methods is included.
The parallel processing capability and adaptive filtering features of dynamic neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. The most important aspect of the fingerprint enhancement is the extraction of relevant details with respect to distributed complex features. For this purpose, an efficient dynamic neural filtering technique has been proposed in this paper. After the enhancement process, fingerprint identification is/has been achieved using joint transform correlation (JTC) algorithm. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it in this study. The identification test results are presented to verify the effectiveness of the proposed enhancement and identification algorithms.
There have been renewed interests in utilizing imaging polarimetry in target detection and discrimination. A greater demand is the need for a sensor capable of discriminating between real military targets and decoys in the battlefield deployments. This paper demonstrates the potential application of passive visible imaging polarimetry in discriminating real targets from identical (same paint type, surface structure, and color) decoys, based on their composite materials. Target material made of steel is compared to three different decoy materials (wood, ceramic, and cardboard) are considered in this study.
One of the most important challenges of fingerprint identification is the extraction of relevant details against distributed complex features. The parallel processing capability and learnable filtering features of cellular neural networks offer highly efficient feature extraction and enhancement capability for fingerprint images. In this paper, we propose to utilize the Widrow learning algorithm with a cellular neural network to efficiently enhance fingerprint details during the enrollment part. To evaluate the performance of the verification-identification part, enhanced fingerprint images are introduced into the fringe-adjusted joint transform correlator architecture for verification of an unknown fingerprint from a database. Comparison between the original and enhanced fingerprint identification and verification results is provided through computer simulation.
An important step in the fingerprint identification system is the extraction of relevant details against distributed complex features. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial intelligence based feature extraction techniques are attractive due to their adaptive learning properties. In this paper, we propose a cellular neural network (CNN) based filtering technique due to its ability of parallel processing and generating learnable filtering features. CNN offers high efficient feature extraction and enhancement possibility for fingerprint images. The enhanced fingerprint images are then introduced to joint transform correlator (JTC) architecture to identify unknown fingerprint from the database. Since the fringe-adjusted JTC algorithm has been found to yield significantly better correlation output compared to alternate JTCs, we used it for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.
A novel polarization-encoded fingerprint verification system for high-security applications is presented. During the enrolment part the fingerprint pattern is encoded with certain parameters of polarization ellipse. We show through experimental and simulation results that polarization encoding of fingerprint images can play significant role in fingerprint verification especially in application where high security is of a great concern.
We develop new techniques for polarization-encoded fingerprint images that correspond to Stokes vector imagery. We show how the polarization-encoded images can effectively be used to increase the discrimination capabilities of fingerprint systems for security applications in defense, law enforcement and civilian areas. Computer simulation supported by optical experiments is also presented to further support the validity of the proposed technique.
We propose a novel Stokes vector imagery system that is capable of determining the full Stokes vector of each pixel in an image simultaneously without any movable parts or modulation. The proposed system creates four channels by utilizing a fixed and rugged lenslet array to produce an exact replica of the incident image through each channel. Analysis of the instrument matrix singularities is discussed. Since all Stokes vector images are determined simultaneously, the processing speed of such a system is high, makes it very attractive for several important applications. Furthermore, the proposed system is expected to reduce several errors associated with conventional imaging polarimeters that employ movable parts.
The pattern matching for fingerprints requires a large amount of data and computation time. Practical fingerprint
identification systems require minimal errors and ultrafast processing time to perform real time verification and
identification. By utilizing the two-dimensional processing capability, ultrafast processing speed and noninterfering
communication of optical processing techniques, fingerprint identification systems can be
implemented in real time. Among the various pattern matching systems, the joint transform correlator (JTC) has
been found to be inherently suitable for real time matching applications. Among the various JTCs, the fringeadjusted
JTC has been found to yield significantly better correlation output compared to alternate JTCs. In this
paper, we review the latest trends and advancements in fingerprint identification system based on the fringeadjusted
JTC. Since all pattern matching systems suffer from high sensitivity to distortions, the synthetic
discriminant function concept has been incorporated in fringe-adjusted JTC to ensure distortion-invariant
fingerprint identification. On the other hand a novel polarization-enhanced fingerprint verification system is
described where a polarized coherent light beam is used to record spatially dependent response of the scattering
medium of the fingerprint to provide detailed surface information, which is not accessible to mere intensity
measurement. It is shown that polarization-enhanced database improves the accuracy of the fingerprint
identification or verification system significantly.
Keywords: Fringe-adjust joint transform correlation, finger print identification, polarization, synthetic
discriminant function
We introduce novel optoelectronic target detection technique using polarization enhancement. Images correspond to elements of Stokes vector imagery are introduced as input scenes to the fringe-adjusted joint-transform correlator. Our results show excellent improvement in performance parameters. Both computer simulation and experimental results are presented in support of the proposed technique.
A high-efficiency beamsplitter for the equipartition of infrared input power using combined reflection and transmission is described. The new design does not use a back metal reflector coating, and hence is more efficient than those previously described. The beamsplitter uses a parallel slab of fused silica that is strip coated with a germanium film on the front and back sides to generate four beams of equal powers. A specific design for operation at the 1.55-µm fiber communication wavelength is presented. Power, efficiency, and polarization analysis of fractional beams in the presence of angular, film-thickness, and spectral deviations near the equipartition condition are discussed. The following results are obtained. An angular deviation of ±0.5 deg has no significant effect on power (<0.1%), efficiency (<0.02%), or polarization (ellipsometric) parameters (< 1 deg) of the fractional beams. A film-thickness deviation of ±10 nm results in a small change in power (<2%), efficiency (<0.1%), and polarization (<0.5 deg) of the fractional beams. A spectral variation of ±25 nm also has a small effect on power (<1.5%), efficiency (<0.01%), and polarization (<1 deg).
A novel polarization-based fingerprint identification sensor is proposed in this paper. This sensor consists of an optoelectronic system where the enrollment process is recorded optically and the identification process is carried out digitally using the concept of fringe-adjusted joint transform correlation technique. In the optical part, a polarized coherent light beam is used to record spatially dependent response of the scattering medium of the fingerprint to provide detailed surface information, which is not accessible to mere intensity measurement. Both simulation and experimental results are presented to evaluate the performance of the proposed technique.
Performance of reflection polarizers using bare semiconductor substrates in the visible and UV spectral range is presented. Performance evaluation based on extinction ratio, throughput, and sensitivity to angular and spectral variations of Si and Ge reflection polarizers are considered.
We describe efficient beam splitters for the equi-partition of infrared input power using combined reflection and transmission by a strip-coated all-dielectric slab. Because no metal coating is used, high efficiency is achieved. The beam splitters use a fused silica parallel-slab that is strip coated with germanium on the front and back sides. Specific designs for operation at 1.55, 2.02 and 5.0 µm wavelengths are presented.
Several recent applications in polarimetry, ellipsometry, spectropolarimetry, and multiplexed galvanometric scanners require a single compact beam splitter capable of splitting an input beam of light into four or more components. Of special interest is to design a single beam splitter to produce multiple components of equal powers. We present a specific IR design of a parallel-slab beam splitter that uses a fused silica as a slab material, and it is strip- coated with Germanium thin film on the front and with a uniform silver coat on the back. Equal powers among the first four components can be achieved when the reflectance levels on the first, second, third, and fourth strip is equal to 20 percent, 68 percent, 54 and 18 percent respectively. Specific designs at wavelengths of 1.55, 2 and 5 micrometers are presented. At a wavelength of 5 micrometers , glass shows some absorption and is replaced by another transparent slab material. The choice of varying the otpical material of the slab and metric thickness of each strip provides a great flexibility in the design and operation of the beam splitter over a wide range of applications.
In this paper we describe novel designs of IR versions of the parallel-slab division-of-amplitude photopolarimeter (IR-PS-DOAP) to measure the state of polarization of light as determined by the four Stokes parameters. The IR-PS-DOAP uses no movable parts or modulation and thus fast and simultaneous measurement is obtained. We present two different designs. The first employs a uniform, thin, transparent, film coating on the front surface of the parallel-slab. The second employs strips of thin, transparent, film coating on the front surface of the parallel-slab. A performance analysis comparison between the two will be presented. For wavelengths up to approximately 3.5 micrometers , SiO2, is totally transparent and is selected to be the slab material for the IR-PS-DOAP. For wavelengths beyond 3.5 micrometers , SiO2 becomes absorbent and will be replaced by another transparent material like Irtran2, for example. The instrument matrix of the system is non- singular; hence the state of polarization is completely determined. The IR-PS-DOAP is compact, light-weight, rugged and based on reflective optics, so that predictive theory of instrument performance is applicable.
We present a novel photopolarimeter capable of compete measurement of the state of polarization of light, as determined by the four Stokes parameters. This photopolarimeter uses a diffractive-optical-element as a beam splitter to divide the incident beam into four or more components. It uses no movable parts or modulation and thus fast and simultaneous measurements of the four Stokes parameters are obtained. The instrument matrix of this element photopolarimeter is compact, lightweight, and rugged, hence, it can be easily integrated for number of applications.
In this paper we investigate the polarization properties of uncoated and coated parallel-slab multi-reflection beam splitters. We analyze the ellipsometric parameters and fractional powers for the multi-reflected components generated by this system. Interesting new observations regarding the polarization properties at the Brewster angle of incidence and distribution of powers among the multi- reflected components orders are presented.
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