Abstract: An encoding-decoding method and system is provided where the encoding process or system are not a mirror image of the decoding process or system.

Abstract: A method to estimate range to a moving rigid body from a moving platform using a monocular camera. The method does not require the camera platform to maneuver in order to estimate range. The method relies on identification and tracking of certain principal features of the object. The method extracts a silhouette of an object from an obtained image and identifies two principal linear components of the silhouette. A normalized distance between the point of intersection of the two linear components and a centroid of the silhouette is computed, compared to a data set and used to determine a direction of movement of the object.

Abstract: An encoding-decoding method and system is provided where the encoding process or system are not a mirror image of the decoding process or system.

Abstract: An encoding-decoding method and system is provided where the encoding process or system are not a mirror image of the decoding process or system.

Abstract: The present invention provides a method and apparatus of obtaining single and video, high-resolution, panoramic images of a large field-of-view scene with a single viewpoint. The system consists of a segmented cap lens, multiple imaging sensors and imaging lenses. The cap lens is segmented to capture an individual sub-field of the entire panorama. In addition, the cap lens is optimized to relay the optical centers of the component cameras into a single viewpoint therefore reducing parallax errors. A high-resolution panoramic image with reduced artifacts is reconstructed by mosaicking multiple images taken by component cameras. The entire system is based upon refractive lenses to minimize the path length of light rays, enabling a relatively compact design for practical applications.

Abstract: A method to estimate range to a moving rigid body from a moving platform using a monocular camera. The method does not require the camera platform to maneuver in order to estimate range. The method relies on identification and tracking of certain principal features of the object. The method extracts a silhouette of an object from an obtained image and identifies two principal linear components of the silhouette. A normalized distance between the point of intersection of the two linear components and a centroid of the silhouette is computed, compared to a data set and used to determine a direction of movement of the object.

Abstract: A computerized aircraft system, such as an unmanned aircraft system (UAS) is provided with a cloud detection system. The UAS includes a monocular electro-optic or infra-red camera which acquires consecutively, in real time, a plurality of images within a field of view of the camera. The system identifies feature points in each of the consecutive images, and generates macro representations of cloud formations (3D representations of the clouds) based on tracking of the feature points across the plurality of images. A cloud avoidance system takes in nominal own-ship waypoints, compares those waypoints to the 3D cloud representations and outputs modified waypoints to avoid the detected clouds.

Abstract: The communications system comprises an encoder for encoding an input image. The salient features of the image prioritized and encoded accordingly. A low bandwidth media for transmitting the encoded input image is used. A decoder is coupled to the encoder for receiving encoded information. The decoder is non-symmetrical to the encoder for decoding merely a part of the encoded input image with improved subjective/perception quality. Whereby a human operator can reach a determination via visual inspection regarding the input image.

Abstract: A method with related structures and computational components and modules for modeling data, particularly audio and video signals. The modeling method can be applied to different solutions such as 2-dimensional image/video compression, 3-dimensional image/video compression, 2-dimensional image/video understanding, knowledge discovery and mining, 3-dimensional image/video understanding, knowledge discovery and mining, pattern recognition, object meshing/tessellation, audio compression, audio understanding, etc. Data representing audio or video signals is subject to filtration and modeling by a first filter that tessellates data having a lower dynamic range. A second filter then further tessellates, if needed, and analyzes and models the remaining parts of data, not analyzable by first filter, having a higher dynamic range. A third filter collects in a generally lossless manner the overhead or residual data not modeled by the first and second filters.