Abstract: A system and a method for real-time detection of a moving object on a travel path in a geographical area is provided. The system and method may also be used to track such a moving object in real-time. The system includes an image capturing device for capturing successive images of a geographical area, a geographical reference map comprising contextual information of the geographical area, and a processor configured to calculate differences between successive images to detect, in real-time, a moving object on the travel path. The method includes capturing successive images of the geographical area using the image capturing device, geo-registering at least some of the successive images relative to the geographical reference map, and calculating differences between the successive images to detect, in real-time, an object.

Abstract: A method and apparatus for processing images. A set of candidate targets is identified in a first image and in a second image that corresponds with the first image. A set of first scores is generated for the set of candidate targets using the first image. A set of second scores is generated for the set of candidate targets using the second image. A set of final scores is computed for the set of candidate targets using the set of first scores and the set of second scores. A determination is made as to which of the set of candidate targets is a target of interest based on the set of final scores.

Abstract: A method and apparatus for performing target detection. A potential object is detected within a candidate chip in an image. The potential object is verified as a candidate object. The candidate object is classified as one of a candidate target or a background in response to the potential object being verified as the candidate object. The candidate target is verified as a target of interest in response to the candidate object being classified as the candidate target.

Abstract: Described is a system for filtering, segmenting and recognizing objects. The system receives a three-dimensional (3D) point cloud having a plurality of data points in 3D space and down-samples the 3D point cloud to generate a down-sampled 3D point cloud with reduced data points in the 3D space. A ground plane is then identified and removed, leaving above-ground data points in the down-sampled 3D point cloud. The above-ground data points are clustered to generate a plurality of 3D blobs, each of the 3D blobs having a cluster size. The 3D blobs are filtered based on cluster size to generate a set of 3D candidate blobs. Features are extracted from each 3D candidate blob. Finally, at least one of the 3D candidate blobs is classified as a pre-defined object class based on the extracted features.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: A method for increasing resolution of an image formed of received light from an illuminated spot includes measuring a y vector for measurement kernels A1 to AM, where M is a number of the measurement kernels, measuring the y vector including programming a programmable N-pixel micromirror or mask located in a return path of a received reflected scene spot with a jth measurement kernel Aj of the measurement kernels A1 to AM, measuring y, wherein y is an inner product of a scene reflectivity f(?,?) with the measurement kernel Aj for each range bin ri, wherein ? and ? are azimuth and elevation angles, respectively, repeating programming the programmable N-pixel micromirror or mask and measuring y for each measurement kernel A1 to AM, and forming a reconstructed image using the measured y vector, wherein forming the reconstructed image includes using compressive sensing or Moore-Penrose reconstruction.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: A motion detector comprising a sensor apparatus and a calculator arranged for: generating first and second successive frames comprising each a 3-D cloud of points, wherein each point represents a position in space of a surface of at least an object in the field of vision of said sensor apparatus; transforming each of the first and second frames by: mapping each cloud of 3-D points into a 2-D grid of cells associated each to a sequence of predetermined volumes; and associating to each cell of the 2-D grid of cells a sequence of the lists of the points contained in corresponding volumes of the sequence of predetermined volumes; comparing the sequences of lists associated to the same cells of the 2-D grids obtained for the first and second frames; and indicating that motion occurred based on the sequence of lists being different in the first and second frames.

Abstract: Described is a low power surveillance camera system for intruder detection. The system observes a scene with a known camera motion to generate images with various viewing angles. Next, a background learning mode is employed to generate a low rank matrix for the background in the images. Background null space projections are then learned, which provide a foreground detection kernel. A new scene with known viewing angles is then obtained. Based on the foreground detection kernel and the new input image frame, low power foreground detection is performed to detect foreground potential regions of interest (ROIs), such as intruders. To filter out minimal foreground activity, the system identifies contiguous ROIs to generate the foreground ROI. Focus measures are then employed on the ROIs using foveated compressed sensing to generate foveated measurements. Based on the foveated measurements, the foreground is reconstructed for presentation to a user.

Abstract: A method and apparatus for performing target detection. A potential object is detected within a candidate chip in an image. The potential object is verified as a candidate object. The candidate object is classified as one of a candidate target or a background in response to the potential object being verified as the candidate object. The candidate target is verified as a target of interest in response to the candidate object being classified as the candidate target.

Abstract: A method and apparatus for processing images. A set of candidate targets is identified in a first image and in a second image that corresponds with the first image. A set of first scores is generated for the set of candidate targets using the first image. A set of second scores is generated for the set of candidate targets using the second image. A set of final scores is computed for the set of candidate targets using the set of first scores and the set of second scores. A determination is made as to which of the set of candidate targets is a target of interest based on the set of final scores.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: Described herein is a method for enhancing image data that includes transforming image data from an intensity domain to a wavelet domain to produce wavelet coefficients. A first set of wavelet coefficients of the wavelet coefficients includes low-frequency wavelet coefficients. The method also includes modifying the first set of wavelet coefficients using a coefficient distribution based filter to produce a modified first set of wavelet coefficients. The method includes transforming the modified first set of wavelet coefficients from the wavelet domain to the intensity domain to produce enhanced image data.

Abstract: Systems and methods of detecting dead pixels of image frames are described including receiving a sequence of image frames, aligning, from the sequence of image frames, pairs of image frames, and for a given pair of image frames, determining differences in intensity of corresponding pixels between the aligned pair of image frames. The method also includes, based on the differences in intensity of corresponding pixels between the aligned pair of image frames, generating mask images indicative of areas in the pairs of image frames having moving objects. The method further includes determining, within the mask images, common pixel locations indicative of areas in the pairs of image frames having moving objects over a portion of the sequence of image frames, and based on a number of the common pixel locations for a given pixel location being above a threshold, identifying the given pixel location as a dead pixel.

Abstract: Within examples, methods and systems for occlusion-robust object fingerprinting using fusion of multiple sub-region signatures are described. An example method includes receiving an indication of an object within a sequence of video frames, selecting from the sequence of video frames a reference image frame indicative of the object and candidate image frames representative of possible portions of the object, dividing the reference image frame and the candidate image frames into multiple cells, defining for the reference image frame and the candidate image frames sub-regions of the multiple cells such that the sub-regions include the same cells for overlapping representations and the sub-regions include multiple sizes, comparing characteristics of sub-regions of the reference image frame to characteristics of sub-regions of the candidate image frames and determining similarity measurements, and based on the similarity measurements, tracking the object within the sequence of video frames.

Abstract: Within examples, methods and systems for occlusion-robust object fingerprinting using fusion of multiple sub-region signatures are described. An example method includes receiving an indication of an object within a sequence of video frames, selecting from the sequence of video frames a reference image frame indicative of the object and candidate image frames representative of possible portions of the object, dividing the reference image frame and the candidate image frames into multiple cells, defining for the reference image frame and the candidate image frames sub-regions of the multiple cells such that the sub-regions include the same cells for overlapping representations and the sub-regions include multiple sizes, comparing characteristics of sub-regions of the reference image frame to characteristics of sub-regions of the candidate image frames and determining similarity measurements, and based on the similarity measurements, tracking the object within the sequence of video frames.

Abstract: The present disclosure generally relates to systems and methods for spectral demixing. An example technique includes obtaining empirical spectroscopic data representing a plurality of frequencies of electromagnetic energy that has interacted with a specimen, accessing a computer readable representation of a hierarchal spectral cluster tree representing a spectral library, demixing, with data on each of a plurality of levels of the hierarchal spectral cluster tree, foveated spectroscopic data derived from the empirical spectroscopic data, identifying at least one node in the hierarchal spectral cluster tree as corresponding to the empirical spectroscopic data, and outputting an endmember abundance assessment of the specimen corresponding to at least the at least one node.

Abstract: The present invention relates to a classifier cascade object detection system. The system operates by inputting an image patch into parallel feature generation modules, each of the feature generation modules operable for extracting features from the image patch. The features are provided to an opportunistic classifier cascade, the opportunistic classifier cascade having a series of classifier stages. The opportunistic classifier cascade is executed by progressively evaluating, in each classifier in the classifier cascade, the features to produce a response, with each response progressively utilized by a decision function to generate a stage response for each classifier stage. If each stage response exceeds a stage threshold then the image patch is classified as a target object, and if the stage response from any of the decision functions does not exceed the stage threshold, then the image patch is classified as a non-target object.

Abstract: Described herein is a method for enhancing image data that includes transforming image data from an intensity domain to a wavelet domain to produce wavelet coefficients. A first set of wavelet coefficients of the wavelet coefficients includes low-frequency wavelet coefficients. The method also includes modifying the first set of wavelet coefficients using a coefficient distribution based filter to produce a modified first set of wavelet coefficients. The method includes transforming the modified first set of wavelet coefficients from the wavelet domain to the intensity domain to produce enhanced image data.

Abstract: A method includes receiving image data at a first tracking system. The image data may represent a region in an image of a sequence of images. The method includes generating a first tracking fingerprint based on the image data. The method includes comparing the first tracking fingerprint and a second tracking fingerprint. The method further includes providing an output from the first tracking system to a second tracking system based on a result of the comparison of the first tracking fingerprint and the second tracking fingerprint.