This PDF file contains the front matter associated with SPIE Proceedings Volume 9845, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.

A novel compressive imaging model is proposed that multiplexes segments of the field of view onto an infrared focal plane array (FPA). Similar to the compound eyes of insects, our imaging model is based on combining pixels from a surface comprising of different parts of the field of view (FOV). We formalize this superposition of pixels in a global multiplexing process reducing the resolution requirements of the FPA. We then apply automated target detection algorithms directed on the measurements of this model in a typical missile seeker scene. Based on quadratic correlation filters, we extend the target training and detection processes directly using these encoded measurements. Preliminary results are promising.

We recently pioneered the concept of light-driven micro-robotics including the new and disruptive 3D-printed micro-tools coined Wave-guided Optical Waveguides that can be real-time optically trapped and "remote-controlled" in a volume with six-degrees-of-freedom. To be exploring the full potential of this new drone-like 3D light robotics approach in challenging microscopic geometries requires a versatile and real-time reconfigurable light coupling that can dynamically track a plurality of "light robots" in 3D to ensure continuous optimal light coupling on the fly. Our latest developments in this new and exciting area will be reviewed in this invited paper.

We present the development of a cross-correlation algorithm for correlating objects in the long wave, mid wave and short wave Infrared sensor arrays. The goal is to align the images in the multisensor suite by correlating multiple key features in the images. Due to the wavelength differences, the object appears very differently in the sensor images even the sensors focus on the same object. In order to perform accurate correlation of the same object in the multi-band images, we perform image processing on the images so that the features of the object become similar to each other. Fourier domain band pass filters are used to enhance the images. Mexican Hat and Gaussian Derivative Wavelets are used to further enhance the features of the object. A Python based QT graphical user interface has been implemented to carry out the process. We show reliable results of the cross-correlation of the objects in multiple band videos.

Sensitivity to the variations in the reference image is a major concern when recognizing target objects. A combinational framework of correlation filters and logarithmic transformation has been previously reported to resolve this issue alongside catering for scale and rotation changes of the object in the presence of distortion and noise. In this paper, we have extended the work to include the influence of different logarithmic bases on the resultant correlation plane. The meaningful changes in correlation parameters along with contraction/expansion in the correlation plane peak have been identified under different scenarios. Based on our research, we propose some specific log bases to be used in logarithmically transformed correlation filters for achieving suitable tolerance to different variations. The study is based upon testing a range of logarithmic bases for different situations and finding an optimal logarithmic base for each particular set of distortions. Our results show improved correlation and target detection accuracies.

Hyperspectral imaging is a powerful tool in the field of remote sensing and has been used for many applications like mineral detection, detection of landmines, target detection etc. Major issues in target detection using HSI are spectral variability, noise, small size of the target, huge data dimensions, high computation cost, complex backgrounds etc. Many of the popular detection algorithms do not work for difficult targets like small, camouflaged etc. and may result in high false alarms. Thus, target/background discrimination is a key issue and therefore analyzing target’s behaviour in realistic environments is crucial for the accurate interpretation of hyperspectral imagery. Use of standard libraries for studying target’s spectral behaviour has limitation that targets are measured in different environmental conditions than application. This study uses the spectral data of the same target which is used during collection of the HSI image. This paper analyze spectrums of targets in a way that each target can be spectrally distinguished from a mixture of spectral data. Artificial neural network (ANN) has been used to identify the spectral range for reducing data and further its efficacy for improving target detection is verified. The results of ANN proposes discriminating band range for targets; these ranges were further used to perform target detection using four popular spectral matching target detection algorithm. Further, the results of algorithms were analyzed using ROC curves to evaluate the effectiveness of the ranges suggested by ANN over full spectrum for detection of desired targets. In addition, comparative assessment of algorithms is also performed using ROC.

A spatial domain optimal trade-off Maximum Average Correlation Height (SPOT-MACH) filter has been previously developed and shown to have advantages over frequency domain implementations in that it can be made locally adaptive to spatial variations in the input image background clutter and normalised for local intensity changes. The main concern for using the SPOT-MACH is its computationally intensive nature. However in the past enhancements techniques were proposed for the SPOT-MACH to make its execution time comparable to its frequency domain counterpart. In this paper a novel approach is discussed which uses VANET parameters coupled with the SPOT-MACH in order to minimise the extensive processing of the large video dataset acquired from the Pakistan motorways surveillance system. The use of VANET parameters gives us an estimation criterion of the flow of traffic on the Pakistan motorway network and acts as a precursor to the training algorithm. The use of VANET in this scenario would contribute heavily towards the computational complexity minimization of the proposed monitoring system.

A scanning electron microscope (SEM) is a type of electron microscope that produces images of a sample by scanning it with a focused beam of electrons. The electrons interact with the sample atoms, producing various signals that are collected by detectors. The gathered signals contain information about the sample’s surface topography and composition. The electron beam is generally scanned in a raster scan pattern, and the beam’s position is combined with the detected signal to produce an image. The most common configuration for an SEM produces a single value per pixel, with the results usually rendered as grayscale images. The captured images may be produced with insufficient brightness, anomalous contrast, jagged edges, and poor quality due to low signal-to-noise ratio, grained topography and poor surface details. The segmentation of the SEM images is a tackling problems in the presence of the previously mentioned distortions. In this paper, we are stressing on the clustering of these type of images. In that sense, we evaluate the performance of the well-known unsupervised clustering and classification techniques such as connectivity based clustering (hierarchical clustering), centroid-based clustering, distribution-based clustering and density-based clustering. Furthermore, we propose a new spatial fuzzy clustering technique that works efficiently on this type of images and compare its results against these regular techniques in terms of clustering validation metrics.

The tracking of winds and atmospheric features has many applications, from predicting and analyzing weather patterns in the upper and lower atmosphere to monitoring air movement from pig and chicken farms. Doppler LIDAR systems exist to quantify the underlying wind speeds, but cost of these systems can sometimes be relatively high, and processing limitations exist. The alternative is using an incoherent LIDAR system to analyze aerosol backscatter. Improving the detection and analysis of wind information from aerosol backscatter LIDAR systems will allow for the adoption of these relatively low cost instruments in environments where the size, complexity, and cost of other options are prohibitive. Using data from a simple aerosol backscatter LIDAR system, we attempt to extend the processing capabilities by calculating wind vectors through image correlation techniques to improve the detection of wind features.

Challenges in object tracking such as object deformation, occlusion, and background variations require a robust tracker to ensure accurate object location estimation. To address these issues, we present a Pyramidal Rotation Invariant Features (PRIF) that integrates Gaussian Ringlet Intensity Distribution (GRID) and Fourier Magnitude of Histogram of Oriented Gradients (FMHOG) methods for tracking objects from videos in challenging environments. In this model, we initially partition a reference object region into increasingly fine rectangular grid regions to construct a pyramid. Histograms of local features are then extracted for each level of pyramid. This allows the appearance of a local patch to be captured at multiple levels of detail to make the algorithm insensitive to partial occlusion. Then GRID and magnitude of discrete Fourier transform of the oriented gradient are utilized to achieve a robust rotation invariant feature. The GRID feature creates a weighting scheme to emphasize the object center. In the tracking stage, a Kalman filter is employed to estimate the center of the object search regions in successive frames. Within the search regions, we use a sliding window technique to extract the PRIF of candidate objects, and then Earth Mover’s Distance (EMD) is used to classify the best matched candidate features with respect to the reference. Our PRIF object tracking algorithm is tested on two challenging Wide Area Motion Imagery (WAMI) datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness. Experimental results show that the proposed PRIF approach yields superior results compared to state-of-the-art feature based object trackers.

In this paper, an all-automatic optimized JTC based swimmer tracking system is proposed and evaluated on real video database outcome from national and international swimming competitions (French National Championship, Limoges 2015, FINA World Championships, Barcelona 2013 and Kazan 2015). First, we proposed to calibrate the swimming pool using the DLT algorithm (Direct Linear Transformation). DLT calculates the homography matrix given a sufficient set of correspondence points between pixels and metric coordinates: i.e. DLT takes into account the dimensions of the swimming pool and the type of the swim. Once the swimming pool is calibrated, we extract the lane. Then we apply a motion detection approach to detect globally the swimmer in this lane. Next, we apply our optimized Scaled Composite JTC which consists of creating an adapted input plane that contains the predicted region and the head reference image. This latter is generated using a composite filter of fin images chosen from the database. The dimension of this reference will be scaled according to the ratio between the head's dimension and the width of the swimming lane. Finally, applying the proposed approach improves the performances of our previous tracking method by adding a detection module in order to achieve an all-automatic swimmer tracking system.

This paper presents an efficient phase-encoded and 4-phase shift keying (PSK)-based fringe-adjusted joint transform correlation (FJTC) technique for face recognition applications. The proposed technique uses phase encoding and a 4- channel phase shifting method on the reference image which can be pre-calculated without affecting the system processing speed. The 4-channel PSK step eliminates the unwanted zero-order term, autocorrelation among multiple similar input scene objects while yield enhanced cross-correlation output. For each channel, discrete wavelet decomposition preprocessing has been used to accommodate the impact of various 3D facial expressions, effects of noise, and illumination variations. The performance of the proposed technique has been tested using various image datasets such as Yale, and extended Yale B under different environments such as illumination variation and 3D changes in facial expressions. The test results show that the proposed technique yields significantly better performance when compared to existing JTC-based face recognition techniques.

We propose a Fourier domain asymmetric cryptosystem for multimodal biometric security. One modality of biometrics (such as face) is used as the plaintext, which is encrypted by another modality of biometrics (such as fingerprint). A private key is synthesized from the encrypted biometric signature by complex spatial Fourier processing. The encrypted biometric signature is further encrypted by other biometric modalities, and the corresponding private keys are synthesized. The resulting biometric signature is privacy protected since the encryption keys are provided by the human, and hence those are private keys. Moreover, the decryption keys are synthesized using those private encryption keys. The encrypted signatures are decrypted using the synthesized private keys and inverse complex spatial Fourier processing. Computer simulations demonstrate the feasibility of the technique proposed.

Here, we report a brief review on the recent developments of correlation algorithms. Several implementation schemes and specific applications proposed in recent years are also given to illustrate powerful applications of these methods. Following a discussion and comparison of the implementation of these schemes, we believe that all-numerical implementation is the most practical choice for application of the correlation method because the advantages of optical processing cannot compensate the technical and/or financial cost needed for an optical implementation platform. We also present a simple iterative algorithm to optimize the training images of composite correlation filters. By making use of three or four iterations, the peak-to-correlation energy (PCE) value of correlation plane can be significantly enhanced. A simulation test using the Pointing Head Pose Image Database (PHPID) illustrates the effectiveness of this statement. Our method can be applied in many composite filters based on linear composition of training images as an optimization means.

An improved shifted phase-encoded fringe-adjusted joint transform correlation technique is proposed in this paper for face recognition which can accommodate the detrimental effects of noise, illumination, and other 3D distortions such as expression and rotation variations. This technique utilizes a third order local derivative pattern operator (LDP3) followed by a shifted phase-encoded fringe-adjusted joint transform correlation (SPFJTC) operation. The local derivative pattern operator ensures better facial feature extraction in a variable environment while the SPFJTC yields robust correlation output for the desired signals. The performance of the proposed method is determined by using the Yale Face Database, Yale Face Database B, and Georgia Institute of Technology Face Database. This technique has been found to yield better face recognition rate compared to alternate JTC based techniques.

Existing face recognition systems are susceptible to spoofing attacks. So, Face liveness detection is a pivotal part for reliable face recognition, which has recently acknowledged vast attention. In this paper we propose a wavelet decomposition based face liveness recognition system using an energy calculation technique. Live faces contain high energy components compared to fake or printed image. In this paper, we calculate energy components of live face as well as fake face using discrete wavelet decomposition method. We analyze percentage of energy at different levels as well as for different wavelet basis function. We also analyze percentage of energy at different RGB bands and efficient face liveness detection method has been proposed. Discrete wavelet representation has been used to calculate decomposed energy components. Moreover, it provides differentiation of several spatial orientations as well as average and detailed information which are missing in the fake faces. This technique provides excellent discrimination capability when compared to the previously reported works based on the discrete Fourier transform and n-dimensional Fourier transform operations. To verify the proposed approach, we tested the performance using various face antispoofing datasets such as university of south Alabama (UFAD), and MSU face antispoofing dataset which incorporates different types of attacks. The test results obtained using the proposed technique shows better performance compared to existing techniques.

Previously we have examined the similarities of the quantum Fourier transform to the classical coherent optical implementation of the Fourier transform (R. Young et al, Proc SPIE Vol 87480, 874806-1, -11). In this paper, we further consider how superposition states can be generated on coherent optical wave fronts, potentially allowing coherent optical processing hardware architectures to be extended into the quantum computing regime. In particular, we propose placing the pixels of a Spatial Light Modulator (SLM) individually in a binary superposition state and illuminating them with a coherent wave front from a conventional (but low intensity) laser source in order to make a so-called ‘interaction free’ measurement. In this way, the quantum object, i.e. the individual pixels of the SLM in their superposition states, and the illuminating wavefront would become entangled. We show that if this were possible, it would allow the extension of coherent processing architectures into the quantum computing regime and we give an example of such a processor configured to recover one of a known set of images encrypted using the well-known coherent optical processing technique of employing a random Fourier plane phase encryption mask which classically requires knowledge of the corresponding phase conjugate key to decrypt the image. A quantum optical computer would allow interrogation of all possible phase masks in parallel and so immediate decryption.

The l1-norm reconstruction techniques have enabled exact data reconstruction with high probability from 'k-sparse' data. This paper presents an added technique to press this reconstruction by truncating the data in its decomposed state. The truncation utilizes a transformation of the eigen-vectors of the covariance matrix and prioritizes the vectors equally without regard to their energy levels associated to the eigenvalues of the vectors. This method presents two primary advantages in data representation: first, the data is naturally represented in only a few terms, components of each of the vectors, and second, the complete set of features is represented, albeit, the fidelity of the representation may have changed. This investigation provides a means of dealing with issues associated with high-energy fading of small-signal data features. One may think of the current technique as a method to inject sparsity into the data that is methodical with consideration of key features represented in eigen-vectors of the covariance matrix of the data.

We are developing an innovative Tunable Laser Spectrometer (TLS) that is compact, broad tuning range (> 200 nm) enabled by an innovative chip-scale (a waveguide based architecture), non-mechanical (voltage- controlled tuning), Waveguide External-cavity Semiconductor Laser (WECSL). This WECSL based TLS, with broad tuning range, will enable the simultaneous measurement of multiple gases abundances in Martian and other planetary atmospheres, adsorbed to soil; and bound to rocks. This monolithic, robust, integrated-optic Tunable Laser Absorption Spectrometer (TLS) will operate in the near infrared and infrared spectral bands. The system architecture, principles of operation and applications of the TLS will be reported in this paper.

This paper presents a simple but effective algorithm for scene sketch generation from input images. The proposed algorithm combines the edge magnitudes of directional Prewitt differential gradient kernels with Kirsch kernels at each pixel position, and then encodes them into an eight bit binary code which encompasses local edge and texture information. In this binary encoding step, relative variance is employed to determine the object shape in each local region. Using relative variance enables object sketch extraction totally adaptive to any shape structure. On the other hand, the proposed technique does not require any parameter to adjust output and it is robust to edge density and noise. Two standard databases are used to show the effectiveness of the proposed framework.

Powerful image editing tools are very common and easy to use these days. This situation may cause some forgeries by adding or removing some information on the digital images. In order to detect these types of forgeries such as region duplication, we present an effective algorithm based on fixed-size block computation and discrete wavelet transform (DWT). In this approach, the original image is divided into fixed-size blocks, and then wavelet transform is applied for dimension reduction. Each block is processed by Fourier Transform and represented by circle regions. Four features are extracted from each block. Finally, the feature vectors are lexicographically sorted, and duplicated image blocks are detected according to comparison metric results. The experimental results show that the proposed algorithm presents computational efficiency due to fixed-size circle block architecture.

Two-dimensional data matrices are used in many different areas that provide quick and automatic data entry to the computer system. Their most common usage is to automatically read labeled products (books, medicines, food, etc.) and recognize them. In Turkey, alcohol beverages and tobacco products are labeled and tracked with the invisible data matrices for public safety and tax purposes. In this application, since data matrixes are printed on a special paper with a pigmented ink, it cannot be seen under daylight. When red LEDs are utilized for illumination and reflected light is filtered, invisible data matrices become visible and decoded by special barcode readers. Owing to their physical dimensions, price and requirement of special training to use; cheap, small sized and easily carried domestic mobile invisible data matrix reader systems are required to be delivered to every inspector in the law enforcement units.

In this paper, we first developed an apparatus attached to the smartphone including a red LED light and a high pass filter. Then, we promoted an algorithm to process captured images by smartphones and to decode all information stored in the invisible data matrix images. The proposed algorithm mainly involves four stages. In the first step, data matrix code is processed by Hough transform processing to find “L” shaped pattern. In the second step, borders of the data matrix are found by using the convex hull and corner detection methods. Afterwards, distortion of invisible data matrix corrected by geometric correction technique and the size of every module is fixed in rectangular shape. Finally, the invisible data matrix is scanned line by line in the horizontal axis to decode it. Based on the results obtained from the real test images of invisible data matrix captured with a smartphone, the proposed algorithm indicates high accuracy and low error rate.

In this work, brief theoretical modeling, analysis, and novel numerical verification of a photorefractive polymer based four wave mixing (FWM) setup for defect detection has been developed. The numerical simulation helps to validate our earlier experimental results to perform defect detection in periodic amplitude and phase objects using FWM. Specifically, we develop the theory behind the detection of isolated defects, and random defects in amplitude, and phase periodic patterns. In accordance with the developed theory, the results show that this technique successfully detects the slightest defects through band-pass intensity filtering and requires minimal additional post image processing contrast enhancement. This optical defect detection technique can be applied to the detection of production line defects, e.g., scratch enhancement, defect cluster enhancement, and periodic pattern dislocation enhancement. This technique is very useful in quality control systems, production line defect inspection, and computer vision.

Traditionally, homodyne and heterodyne detection is the mixing of two signals with different frequencies, followed by a low-pass filter. Mixing two signals of frequencies f₁ and f₂, generates signals of frequencies f₁ + f₂ and f₁- f₂ and their integer multiples. Both multiplicative heterodyne and phase sensitive detection has been demonstrated optically, by using photorefractive four-wave mixing (FWM). The multiplicative characteristic of FWM is used for mixing, and the response time of the photorefractive medium is used for low-pass filtering. If one of the input beams is both spatially and temporally modulated using a controlled oscillating membrane, depending on which mode is heterodyned, one can generates orthogonal sets of Bessel band pass filters. This scheme can be integrated part within parallel data acquisition systems for applications involved nondestructive testing.

An approach is presented that automatically discovers different cluster shapes that are hard to discover by traditional clustering methods (e.g., non-spherical shapes). This allows discover useful knowledge by dividing the datasets into sub clusters; in which each one have similar objects. The approach does not compute the distance between objects but instead the similarity information between objects is computed as needed while using the topological relations as a new similarity measure. An efficient tool was developed to support the approach and is applied to a multiple synthetic and real datasets. The results are evaluated and compared against different clustering methods using different comparison measures such as accuracy, number of parameters, and time complexity. The tool performs better than error-prone distance clustering methods in both the time complexity and the accuracy of the results.

The presented paper deals to PC modeling of scaled and rotated images recognition based on different types of distortion invariant correlation filters. There was used a database of different images (both false and true class) that are under geometrical distortions and there were calculated correlation outputs for images recognition with the help of MACE, MINACE, GMACE, DCCF and polynomial filters and it's combinations for two-stage recognition. The results provide a possibility for successful usage of implemented algorithms.

Recently, the one dimensional (1D) spectral fringe-adjusted joint transform correlation (SFJTC) technique has been proposed as an effective means for performing deterministic target detection in hyperspectral imagery. In addition, different types of sensor data have been proposed in the literature to characterize and detect objects or targets. Some of the recently proposed sensors are accelerometers and laser vibrometers. In this work, we investigate how a laser vibrometer data signature is correlated to any accelerometer based data signature using SFJTC. Toward this end, we applied SFJTC to several data sets of laser velocity vibrometer data and different accelerometer data recorded from a vehicle under different challenging conditions. It was found that all laser vibrometer data correlated equally well with all accelerometer data regardless of the position of the accelerometer under all conditions. This result suggests that laser vibrometric data may be used instead of accelerometric data to uniquely characterize desired objects such as vehicles.

For the gyrator transform-based image encryption, besides the random operations, the rotation angles used in the gyrator transforms are also taken as the secret keys, which makes such cryptosystems to be more secure. To analyze the security of such cryptosystems, one may start from analyzing the security of a single gyrator transform. In this paper, the security of the gyrator transform-based image encryption by chosen-plaintext attack was discussed in theory. By using the impulse functions as the chosen-plaintext, it was concluded that: (1) For a single gyrator transform, by choosing a plaintext, the rotation angle can be obtained very easily and efficiently; (2) For image encryption with a single random phase encoding and a single gyrator transform, it is hard to find the rotation angle directly with a chosen-plaintext attack. However, assuming the value of one of the elements in the random phase mask is known, the rotation angle can be obtained very easily with a chosen-plaintext attack, and the random phase mask can also be recovered. Furthermore, by exhaustively searching the value of one of the elements in the random phase mask, the rotation angle as well as the random phase mask may be recovered. By obtaining the relationship between the rotation angle and the random phase mask for image encryption with a single random phase encoding and a single gyrator transform, it may be useful for further study on the security of the iterative random operations in the gyrator transform domains.