This PDF file contains the front matter associated with SPIE Proceedings Volume 9094, including the Title Page, Copyright Information, Table of Contents, and the Conference Committee listing.

We have designed optical processing architecture and algorithms utilizing the DMD as the input and filter Spatial Light Modulators (SLM). Detailed system analysis will be depicted. Experimental demonstration, for the first time, showing that a complex-valued spatial filtered can be successfully written on the DMDSLM using a Computer Generated Hologram (CGH) [1] encoding technique will also be provided. The high-resolution, high-bandwidth provided by the DMD and its potential low cost due to mass production will enable its vast defense and civil application.

We describe the design and evaluate the performance of a compressive imaging system comprised of a 256x320 detector array sensitive to mid-wave infrared, DMD, objective and relay lenses. The irradiance of each detector element is characterized that allows a system of measurements to be made separable from other detectors. The FOV is divided into smaller areas based on the support of each detector, allowing for tractable high throughput reconstructions. Cross-talk is considered in the sensor modeling that corrects for the noise in the boundaries of the image patches. Based on our previous work, we apply optimal codes subject to device constraints and give favorable results.

Advanced correlation filters (CFs) were introduced over three decades ago to offer distortion-tolerant object recognition and are used in applications such as automatic target recognition (ATR) and biometric recognition. Some of the advances in CF design include minimum average correlation energy (MACE) filters that produce sharp correlations and offer excellent discrimination, optimal tradeoff synthetic discriminant function (OTSDF) filters that allow the filter designer to control the tradeoff between peak sharpness and noise tolerance, maximum average correlation height (MACH) filter that removes correlation peak constraints to reduce filter design complexity and quadratic correlation filters (QCFs) that extend the linear CFs to include second-order nonlinearity. In this paper, we summarize two recent major advances in CF design. First is the introduction of maximum margin correlation filters (MMCFs) that combine the excellent localization properties of CFs with the very good generalization abilities of support vector machines (SVMs). Second is the introduction of zero-aliasing correlation filters (ZACFs) that eliminate the aliasing in CF design due to the circular correlation caused by the use of discrete Fourier transforms (DFTs).

The photorefractive joint transform correlator (JTC) combines two features. The first is embedded semi-adaptive optimality which weighs the correlation against clutter and noise in the input and the second is the intrinsic dynamic range compression nonlinearity which improves several metrics simultaneously without metric tradeoff. The performance of this two-beam coupling joint transform correlator scheme is evaluated against several other well-known correlation filters that have been developed during the last three decades. The result shows that the two-beam coupling joint transform scheme is a very robust correlator with respect to standard evaluation metrics for different sets of data.

In this paper, we present a method for reducing the computation time of Automated Target Recognition (ATR) algorithms through the utilization of the parallel computation on Graphics Processing Units (GPUs). A selected multistage ATR algorithm is refounded to encourage efficient execution on the GPU. Such refounding includes parallel reimplementations of optical correlation, Feature Extraction, Classification and Correlation using NVIDIA's CUDA programming model. This method is shown to significantly reduce computation time of the selected ATR algorithms allowing the potential for further complexity and real-time applications.

The face recognition tasks can be divided into two categories: verification (i.e. compare two images in order to know if they represent the same person) and identification (i.e. find the identity of a person into the database). Several powerful face recognition methods exist, in literature, for controlled environments: constrained illumination, frontal pose, neutral expression... However, there are few reliable methods for the uncontrolled case. Optical correlation has shown its interest through relevant architectures for controlled and uncontrolled environments. Based on this architecture, we propose a novel method for verification and identification tasks under illumination variation conditions. More specifically, we optimize the performances of a correlation method against illumination changes by using and adapting the Local Binary Patterns (LBP) description. This later is widely used in the literature to describe the texture of an image using 8 bits words. For both, target image and reference image, we begin by using a specific-Gaussian function as first step of LBP-VLC correlator. This function filters the considered image with a band-pass filter in order to extract the edges. Then we applied the adapted LBP-VLC method. To validate our new approach, we used a simple POF filter (others correlation filters can be used). The simulations are done using the YaleB and YaleB Extended databases that contain respectively 10 and 38 identities with 64 illuminations. The results obtained reach more than 94% and 92% for the verification and 93% and 90% for the identification case. These results show the good performances of our approach of LBP-correlation methods against illumination changes.

The performance of a novel joint transform correlator (JTC) based on photorefractive (PR) two-beam coupling (TBC) is analyzed by determining the dependence of relevant figures of merit such as the discrimination ratio, the peak-to-correlation plane energy ratio, and the peak-to-noise ratio on the PR gain coefficient and pump-probe beam ratio for a variety of reference and signal images. In this scheme, spatially separated reference and signal images constitute the pump, which transfers energy to a weak probe in a novel image processing setup where the PR polymer serves as the spatial filter in the Fourier plane.

Swimmer tracking has specific difficulties compared to the other tracking systems due to some complex problems such as occlusion (by another person, a wall or splashing), variability of the target (in appearance, lighting or behavior). For the sake of conceiving a robust swimmer tracking system we started by developing mono-tracking systems based on some well-known pattern recognition techniques such as optical correlation (Non-linear JTC (Joint Transform Correlator)) and histogram based approaches (Color histogram, LBP (Local Binary Patterns) and HOG (Histogram of Oriented Gradients)). As an enhancement to these systems, we introduce the aspect of multi-tracking. Its basic idea is to track several potential targets which will give us several tracks then we choose the best track and relaunch the multi-tracking process. Forasmuch each technique has its own periods of good tracking and dropouts (loss of the object to be tracked), we propose a novel heterogeneous multi-tracking system by taking advantage of different tracking techniques. Each track represents an independent mono-tracking system. The use of the proposed heterogeneous multi-tracking system has improved significantly the tracking results from 81.34% to 94%.

Automatic target detection and tracking requires efficient recognition of the target pattern in variable environmental conditions. Optical joint transform correlation (JTC) method has been proven to be efficient in recognizing a target without requiring complex optical set up. However, the classical JTC suffers from poor correlation performance, which can be improved through the use of different and modified designs. A very successful scheme is developed by employing phase-shifted and phase-encoded fringe-adjusted JTC (SPFJTC), which provides with a high discrimination between a target and non-target objects in a given scene and better utilization of the space-bandwidth resource. Further enhancement of the target detection performance can be achieved by incorporating log-polar transform in the SPFJTC technique. We applied the SPFJTC technique to the log-polar transformation of both the reference image and the input scene that makes the pattern recognition invariant to rotation and scale variations. Peak-to-side lobe ratio is measured and a threshold operation is employed to detect and track a target in an unknown input scene.

Various matched filter based architectures have been proposed over the last two decades to optimize the target detection and recognition performance. While these techniques provide excellent performance with respect to one or more parameters, a unified and synergistic approach to evaluate the performance of these techniques under the same constraints is yet to be done. Consequently, in this paper, we used a set of generalized performance metrics for comparing the performance of the recently reported matched filter based techniques using various types of infrared and SAR datasets. Test results obtained using the aforementioned datasets and performance metrics provide excellent information with respect to the suitability of existing filter based techniques for various target detection and tracking practical applications.

LPED method, or Local Polar Edge Detection method, is a novel method the author discovered and implemented in many image processing schemes in the last 3 years with 3 papers published in this and other SPIE national conferences. It uses a special real-time boundary extraction method applied to some binary images taken by an uncooled IR camera on some high temperature objects embedded in a cold environment background in the far field. The unique boundary shape of each high temperature object can then be used to construct a 36D analog vector (a 36 − “digit” number U, with each “digit” being a positive analog number of any magnitude). This 36D analog vector U then represents the ID code to identify this object possessing this particular boundary shape. Therefore, U may be used for tracking and targeting on this particular object when this object is moving very fast in a 2D space and criss-crossing with other fast moving objects embedded in the same field of view. The current paper will report a preliminary optical bench design of the optical system that will use the above developed soft-ware to construct a real-time, instant-detect, instant track, and automatic targeting high power laser gun system, for shooting down any spontaneously launched enemy surface-to-air-missiles from the near-by battle ground. It uses the total reflection phenomenon in the Wollastron beam combiner and real-time monitor screen auto-targeting and firing system to implement this “instant-detect, instant-kill, SAM killer system”.

In this paper, we present a method to optimize Multi-Channel Free Space Optical Communication for statically aligned transmitter-receiver pairs. Pattern recognition algorithms are employed to minimize crosstalk between pixels, reducing the need for channel redundancy. Digitization is accomplished through comparison with several look up tables which are generated during alignment. Mathematical modeling has been performed to simulate the optical misalignment. A multistage automated alignment system can be developed based on the models. Simulation of the in plane and out-of-plane translation and rotation shows that this method builds a foundation of an effective self-healing precision optical alignment system.

A new rotation-invariant pattern recognition technique, based on spectral fringe-adjusted joint transform correlator (SFJTC) and histogram representation, is proposed. Synthetic discriminant function (SDF) based joint transform correlation (JTC) techniques have shown attractive performance in rotation-invariant pattern recognition applications. However, when the targets present in a complex scene, SDF-based JTC techniques may produce false detections due to inaccurate estimation of rotation angle of the object. Therefore, we herein propose an efficient rotation-invariant JTC scheme which does not require a priori rotation training of the reference image. In the proposed technique, a Vectorized Gaussian Ringlet Intensity Distribution (VGRID) descriptor is also proposed to obtain rotation-invariant features from the reference image. In this step, we divide the reference image into multiple Gaussian ringlets and extract histogram distribution of each ringlet, and then concatenate them into a vector as a target signature. Similarly, an unknown input scene is also represented by the VGRID which produces a multidimensional input image. Finally, the concept of the SFJTC is incorporated and utilized for target detection in the input scene. The classical SFJTC was proposed for detecting very small objects involving only few pixels in hyperspectral imagery. However, in our proposed algorithm, the SFJTC is applied for a two-dimensional image without limitation of the size of objects and most importantly it achieves rotation-invariant target discriminability. Simulation results verify that the proposed scheme performs satisfactorily in detecting targets in the input scene irrespective of rotation of the object.

An illumination-robust face recognition system using Local Directional Pattern (LDP) descriptors in Phase Congruency (PC) space is proposed in this paper. The proposed Directional Pattern of Phase Congruency (DPPC) is an oriented and multi-scale local descriptor that is able to encode various patterns of face images under different lighting conditions. It is constructed by applying LDP on the oriented PC images. A LDP feature is obtained by computing the edge response values in eight directions at each pixel position and encoding them into an eight bit binary code using the relative strength magnitude of these edge responses. Phase congruency and local directional pattern have been independently used in the field of face and facial expression recognition, since they are robust to illumination changes. When the PC extracts the discontinuities in the image such as edges and corners, the LDP computes the edge response values in different directions and uses these to encode the image texture. The local directional pattern descriptor on the phase congruency image is subjected to principal component analysis (PCA) for dimensionality reduction for fast and effective face recognition application. The performance evaluation of the proposed DPPC algorithm is conducted on several publicly available databases and observed promising recognition rates. Better classification accuracy shows the superiority of the LDP descriptor against other appearance-based feature descriptors such as Local Binary Pattern (LBP). In other words, our result shows that by using the LDP descriptor the Euclidean distance between reference image and testing images in the same class is much less than that between reference image and testing images from the other classes.

Structured Light Illumination is a widely used 3D shape measurement technique in non-contact surface scanning. Multi-pattern based Structured Light Illumination methods are the most accurate measuring techniques, but are sensitive to object motion during the pattern projection. To reduce this sensitivity, Composite Pattern was introduced as a single pattern Structured Light Illumination technique. Composite Pattern technique spatially modulates several Phase Measuring Profilometry patterns into a single pattern but demonstrated sensitivity to surface contrast and an object’s Spatial Modulation Transfer Function in the form of banding error. The Modified Composite Pattern was developed based on Composite Pattern, but used an imbedded binary gray code to minimize sensitivity to an object’s Spatial Modulation Transfer Function and banding at the expense of lateral resolution. We present a novel method utilizing an MCP pattern for non-ambiguous phase followed by a single sinusoidal pattern. The surface phase modulates the single sinusoidal pattern which is demodulated using a quadrature demodulation technique and then unwrapped by the MCP phase result. A single sinusoidal pattern reconstruction inherently has banding error. So the final step is to use an existing de-banding algorithm. The mathematical implementation is given in detail for the two pattern algorithm and experimental results are presented.

A graph-based approach for modeling and solving the LiDAR filtering problem in urban areas is established. Our method consists of three steps. In the first step we construct a graph-based representation of the LiDAR data, Delaunay triangulation or the KNN graph can be used in this step. An algorithm is introduced to label the edges of this graph. In this second step, we defined criteria to eliminate some of the graph edges, then we used a connected components algorithm to separate the graph representation into different components. Finally, these components are classified into terrain or objects. Different datasets with different characteristics have been used to analyze the performance of our method.

Recently, substantial efforts have been made to find an alternative approach to the Shannon sampling theorem with a method that can deal with large data sets, something for which the Shannon theorem is not easily applicable. If applied, the above approach would have to surmount difficult computational problems resulting from large data. In order to deal with the large data sets, we avoid a universal image acquisition and use wavelet matrices based on tree structures. The proposed approach allows a calculation reduction that yields a better control over the compressed image quality. The suggested technique also advocates a selective approach over the non-adaptive, random functions favored by the Shannon sampling theorem.

Minimum Noise And Correlation Energy (MINACE) filters application provides good ability to recognize in case of grayscale input images of an object with background noises. For fast correlation matching MINACE filters can be used in 4-f correlators as a computer generated hologram (holographic filters). In this paper different variants of holographic filters realization were discussed. The results of correlation recognition with holographic MINACE filters are presented.

There are presented results of scaled image recognition modeling with the help of optical correlator with invariant correlation filters like MACE, GMACE, MINACE and DCCF. The image database for testing include images of true and false classes. There are presented qualitative and quantitative characteristics of output correlation peaks. Also there is provided an analysis of positive and negative side of each filter's type as for single class only scaled images recognition so for multiclass one. Also there are shown results for modeling of images with more complex distortions recognition using all mentioned types of filters.