Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery.

Recognizing targets in infra-red images is an important problem for defense and security applications. A deployed network must not only recognize the known classes, but it must also reject any new or objects without confusing them to be one of the known classes. Our goal is to enhance the ability of existing (or pretrained) classifiers to detect and reject unknown classes.

Detection and Localization of Small Moving Objects in the Presence of Sensor and Platform Movement.

In this paper, we address the challenge of detecting small moving targets in dynamic environments characterized by the concurrent movement of both platform and sensor. In such cases, simple image-based frame registration and optical flow analysis cannot be used to detect moving targets. To tackle this, it is necessary to use sensor and platform meta-data in addition to image analysis for temporal and spatial anomaly detection.

Three-dimensional integral imaging and object detection using long-wave infrared imaging.

We propose a passive three-dimensional (3D) imaging technique based on integral imaging using a long-wave infrared (LWIR) camera. 3D imaging can improve visualization and detection of objects in adverse environments, such as low light levels and the presence of partial occlusions, along with depth estimation by reconstructing the scene at the plane of the object. This is achieved by capturing multiple two-dimensional images, known as elemental images (EI), of a scene with each image having a unique perspective of the 3D objects.

Recent results of medium wave infrared compressive sensing.

The application of compressive sensing (CS) for imaging has been extensively investigated and the underlying mathematical principles are well understood. The theory of CS is motivated by the sparse nature of real-world signals and images, and provides a framework in which high-resolution information can be recovered from low-resolution measurements. This, in turn, enables hardware concepts that require much fewer detectors than a conventional sensor.

Optimizing measurements for feature-specific compressive sensing.

While the theory of compressive sensing has been very well investigated in the literature, comparatively little attention has been given to the issues that arise when compressive measurements are made in hardware. For instance, compressive measurements are always corrupted by detector noise. Further, the number of photons available is the same whether a conventional image is sensed or multiple coded measurements are made in the same interval of time.

3D passive integral imaging using compressive sensing.

Passive 3D sensing using integral imaging techniques has been well studied in the literature. It has been shown that a scene can be reconstructed at various depths using several 2D elemental images. This provides the ability to reconstruct objects in the presence of occlusions, and passively estimate their 3D profile.

Maximum Margin Correlation Filter: a new approach for localization and classification.

Support vector machine (SVM) classifiers are popular in many computer vision tasks. In most of them, the SVM classifier assumes that the object to be classified is centered in the query image, which might not always be valid, e.g.

3D passive photon counting automatic target recognition using advanced correlation filters.

In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.

Expanding the field of view by polarization multiplexing.

We introduce and demonstrate a method for expanding the field of view of a typical imaging system by multiplexing images encoded onto different polarization states and recovering them from a limited number of measurements.

Off-axis sparse aperture imaging using phase optimization techniques for application in wide-area imaging systems.

Sparse apertures find imaging applications in diverse fields such as astronomy and medicine. We are motivated by the design of a wide-area imaging system where sparse apertures can be used to construct novel and efficient optical designs. Specifically, we investigate the use of sparse apertures for off-axis imaging at infrared wavelengths while combating the effects of chromaticity to preserve resolution.

Intraclass and between-class training-image registration for correlation-filter synthesis.

Correlation filters have traditionally been designed without much attention given to the issue of the training images within a class or the relative spatial position between classes. We examine the impact of training-set registration on correlation-filter performance and develop techniques for centering the training images from a class that result in improved performance. We also show that it is beneficial to adjust the spatial position of the classes relative to one another.

Optimal tradeoff circular harmonic function correlation filter methods providing controlled in-plane rotation response.

Correlation methods are becoming increasingly attractive tools for image recognition and location. This renewed interest in correlation methods is spurred by the availability of high-speed image processors and the emergence of correlation filter designs that can optimize relevant figures of merit. In this paper, a new correlation filter design method is presented that allows one to optimally tradeoff among potentially conflicting correlation output performance criteria while achieving desired correlation peak value behavior in response to in-plane rotation of input images.

Automatic target recognition employing signal compression.

Quadratic correlation filters (QCFs) have been used successfully to detect and recognize targets embedded in background clutter. Recently, a QCF called the Rayleigh quotient quadratic correlation filter (RQQCF) was formulated for automatic target recognition (ATR) in IR imagery. Using training images from target and clutter classes, the RQQCF explicitly maximized a class separation metric.

Integrated approach for automatic target recognition using a network of collaborative sensors.

We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted.

Target-adaptive polarimetric synthetic aperture radar target discrimination using maximum average correlation height filters.

We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states.

Quadratic correlation filter design methodology for target detection and surveillance applications.

A novel method is presented for optimization of quadratic correlation filters (QCFs) for shift-invariant target detection in imagery. The QCFs are quadratic classifiers that operate directly on the image data without feature extraction or segmentation. In this sense, the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects.

Biometric verification with correlation filters.

Using biometrics for subject verification can significantly improve security over that of approaches based on passwords and personal identification numbers, both of which people tend to lose or forget. In biometric verification the system tries to match an input biometric (such as a fingerprint, face image, or iris image) to a stored biometric template. Thus correlation filter techniques are attractive candidates for the matching precision needed in biometric verification.

Constrained quadratic correlation filters for target detection.

A method for designing and implementing quadratic correlation filters (QCFs) for shift-invariant target detection in imagery is presented. The QCFs are quadratic classifiers that operate directly on the image data without feature extraction or segmentation. In this sense the QCFs retain the main advantages of conventional linear correlation filters while offering significant improvements in other respects.

Object recognition in subband transform-compressed images by use of correlation filters.

We introduce subband correlation filters (SCFs) as a solution to the problem of object recognition at multiple resolution levels in quantized transformed imagery. The approach synthesizes correlation filters that operate directly on subband coefficients rather than on image data. We explore two techniques to accomplish the reduced-resolution recognition: (1) training the correlation filters to incorporate downsampling tolerance and (2) adaptation of the subband decomposition filters to accommodate the reduced resolutions.

Distance-classifier correlation filters for multiclass target recognition.

We describe a correlation-based distance-classifier scheme for the recognition and the classification of multiple classes. The underlying theory uses shift-invariant filters to compute distances between the input image and ideal references under an optimum transformation. The original distance-classifier correlation filter was developed for a two-class problem.

Statistical inference for automatic target recognition systems.

Traditionally, correlation-based target-recognition systems are evaluated by observation of the number of images successfully recognized and the errors made in the classification process. However, the rigorous use of hypothesis tests and confidence intervals in these evaluations does not appear with any regularity in the optics literature. The optical target-recognition community should adopt these standard methods to evaluate objectively the statistical performance of automatic target recognition systems.

Optimal trade-off synthetic discriminant function filters for arbitrary devices.

A new correlation-filter design methodology is presented for achieving two objectives: synthetic discriminant function filters that can be implemented on arbitrary various criteria of interest. devices and that can provide optimal trade-off among various criteria of interest.