Abstract: Determining GPS coordinates of some image point(s) positions in at least two images using a processor configured by program instructions. Receiving position information of some of the positions where an image capture device captured an image. Determining geometry by triangulating various registration objects in the images. Determining GPS coordinates of the image point(s) positions in at least one of the images. Saving GPS coordinates to memory. This system and method may be used to determine GPS coordinates of objects in an image.

Abstract: Detecting a pattern in an image by receiving the image of a pattern and storing the image in a memory, where the pattern is composed of shapes that have geometrical properties that are invariant under near projective transforms. In some embodiments the process detects shapes in the image using the geometrical properties of the shapes, determines the alignment of the various shapes, and, corresponds or matches the shapes in the image with the shapes in the pattern. This pattern detection process may be used for calibration or distortion correction in optical devices.

Abstract: Information of different scans of physical objects may require comparison, for example to determine if the scans are of the same object or if an object has changed, or better information for a three dimensional model may be desired. Different scans of physical objects may be compared by determining lines or planes tangent to a surface at a discrete number of points, registering three dimensional information provided by the scans using the tangent lines or planes, and determining a measure of discrepancy between the surfaces. Three dimensional information of different scans of the same object may also be merged after determining lines or planes tangent to a surface at a discrete number of points and performing registration and merging.

Abstract: Detecting a pattern in an image by receiving the image of a pattern and storing the image in a memory, where the pattern is composed of shapes that have geometrical properties that are invariant under near projective transforms. In some embodiments the process detects shapes in the image using the geometrical properties of the shapes, determines the alignment of the various shapes, and, corresponds or matches the shapes in the image with the shapes in the pattern. This pattern detection process may be used for calibration or distortion correction in optical devices.

Abstract: Lossless video data compression is performed in real time at the data rate of incoming real time video data in a process employing a minimum number of computational steps for each video pixel. Each pixel 8-bit byte is converted to a difference byte representing the difference between the pixel and its immediate predecessor in a serialized stream of the pixel bytes. This reduces the dynamic range of the data. Any carry bits generated in the subtraction process of two's complement arithmetic are discarded. This reduces the data by a factor of two. Finally, the 8-bit difference pixel bytes thus produced are subject to a maximum entropy encoding process. Such a maximum entropy encoding process may be referred to as a minimum length encoding process.

Abstract: An image analysis technology that enables real-time fully 3-dimensionally integrated reconstruction of the live video-data derived from CCTV system's multiple camera observations of a spatially connected surveillance scene.

Abstract: Information of different scans of physical objects may require comparison, for example to determine if the scans are of the same object or if an object has changed, or better information for a three dimensional model may be desired. Different scans of physical objects may be compared by determining lines or planes tangent to a surface at a discrete number of points, registering three dimensional information provided by the scans using the tangent lines or planes, and determining a measure of discrepancy between the surfaces. Three dimensional information of different scans of the same object may also be merged after determining lines or planes tangent to a surface at a discrete number of points and performing registration and merging.

Abstract: Determining GPS coordinates of some image point(s) positions in at least two images using a processor configured by program instructions. Receiving position information of some of the positions where an image capture device captured an image. Determining geometry by triangulating various registration objects in the images. Determining GPS coordinates of the image point(s) positions in at least one of the images. Saving GPS coordinates to memory. This system and method may be used to determine GPS coordinates of objects in an image.

Abstract: Detecting a pattern in an image by receiving the image of a pattern and storing the image in a memory, where the pattern is composed of shapes that have geometrical properties that are invariant under near projective transforms. In some embodiments the process detects shapes in the image using the geometrical properties of the shapes, determines the alignment of the various shapes, and, corresponds or matches the shapes in the image with the shapes in the pattern. This pattern detection process may be used for calibration or distortion correction in optical devices.

Abstract: Determining GPS coordinates of some image point(s) positions in at least two images using a processor configured by program instructions. Receiving position information of some of the positions where an image capture device captured an image. Determining geometry by triangulating various registration objects in the images. Determining GPS coordinates of the image point(s) positions in at least one of the images. Saving GPS coordinates to memory. This system and method may be used to determine GPS coordinates of objects in an image.

Abstract: Indexing regions of changed pixels (ROCHs) in a collection of images by receiving a collection of images. Estimate and/or validate a background among the collection of images. Detect the changes between images in the collection. Associate the detected changes between the images and classifying the associated changes. This image processing method and system may be used for image indexing and object classification.

Abstract: Information of different scans of physical objects may require comparison, for example to determine if the scans are of the same object or if an object has changed, or better information for a three dimensional model may be desired. Different scans of physical objects may be compared by determining lines or planes tangent to a surface at a discrete number of points, registering three dimensional information provided by the scans using the tangent lines or planes, and determining a measure of discrepancy between the surfaces. Three dimensional information of different scans of the same object may also be merged after determining lines or planes tangent to a surface at a discrete number of points and performing registration and merging.

Abstract: Lossless video data compression is performed in real time at the data rate of incoming real time video data in a process employing a minimum number of computational steps for each video pixel. Each pixel 8-bit byte is converted to a difference byte representing the difference between the pixel and its immediate predecessor in a serialized stream of the pixel bytes. This reduces the dynamic range of the data. Any carry bits generated in the subtraction process of two's complement arithmetic are discarded. This reduces the data by a factor of two. Finally, the 8-bit difference pixel bytes thus produced are subject to a maximum entropy encoding process. Such a maximum entropy encoding process may be referred to as a minimum length encoding process.

Abstract: Lossless video data compression is performed in real time at the data rate of incoming real time video data in a process employing a minimum number of computational steps for each video pixel. A first step is to convert each pixel 8-bit byte to a difference byte representing the difference between the pixel and its immediate predecessor in a serialized stream of the pixel bytes. Thus, each 8-bit pixel byte is subtracted from its predecessor. This step reduces the dynamic range of the data. A next step is to discard any carry bits generated in the subtraction process of two's complement arithmetic. This reduces the data by a factor of two. Finally, the 8-bit difference pixel bytes thus produced are subject to a maximum entropy encoding process. Such a maximum entropy encoding process may be referred to as a minimum length encoding process. One example is Huffman encoding.

Abstract: Methods and systems for grouping video frames. The video frames may be from a multiplexed stream of video frames from a plurality of video sources. In some embodiments video frames are grouped by determining if the video frames share meaningful modes for values representative of features of the video frames.

Abstract: Detecting a pattern in an image by receiving the image of a pattern and storing the image in a memory, where the pattern is composed of shapes that have geometrical properties that are invariant under near projective transforms. In some embodiments the process detects shapes in the image using the geometrical properties of the shapes, determines the alignment of the various shapes, and, corresponds or matches the shapes in the image with the shapes in the pattern. This pattern detection process may be used for calibration or distortion correction in optical devices.

Abstract: A method and apparatus to extract a dense three-dimensional model of an observed scene or object from one or more sequences of images acquired with an imaging device such as a camera or camcorder, or clusters of cameras and/or camcorders. In some embodiments the method includes capturing an image sequence by a camera moving with a translateral motion, for example moving on a straight path with a fixed orientation. The images in each sequence are captured at regularly spaced sites on the straight path. In some embodiments, a device projects structured light on the observed scene. Multiple image sequences can be acquired, on different cameras on straight paths. The captured image sequences are input to a computer. The 3D structure of the scene viewed by each image sequence is computed. Dense reconstruction from EPIs is performed by interpolating the EPIs after having detecting straight segments on the EPIs.

Abstract: Methods and systems for grouping video frames. The video frames may be from a multiplexed stream of video frames from a plurality of video sources. In some embodiments video frames are grouped by determining if the video frames share meaningful modes for values representative of features of the video frames.

Abstract: Methods and systems for performing object directed recognition based on two-dimensional images and three-dimensional models. Transforms are used to map features of an object to an image seen from a vantage point and to map features of images best seen from the vantage points to three-dimensional models. Mapping of features and images to the three-dimensional model and then to image planes of other images in comparison of corresponding features from the mapped images to original images allows for determination of coherence between the mapped images and the original images.

Abstract: Lossless video data compression is performed in real time at the data rate of incoming real time video data in a process employing a minimum number of computational steps for each video pixel. A first step is to convert each pixel 8-bit byte to a difference byte representing the difference between the pixel and its immediate predecessor in a serialized stream of the pixel bytes. Thus, each 8-bit pixel byte is subtracted from its predecessor. This step reduces the dynamic range of the data. A next step is to discard any carry bits generated in the subtraction process of two's complement arithmetic. This reduces the data by a factor of two. Finally, the 8-bit difference pixel bytes thus produced are subject to a maximum entropy encoding process. Such a maximum entropy encoding process may be referred to as a minimum length encoding process. One example is Huffman encoding.