Abstract: An image of a visual scene, comprising a plurality of pixels. is acquired and an associated range map is either determined therefrom or separately acquired. Elements of the range map comprise distances from the camera for each pixel of the image. In one aspect, either the image or the range map is processed with a connected-components sieve filter that locates clusters of pixels or elements that are connected to one another along either adjacent rows, columns or diagonally. In another aspect, a cross-range value of range-map element is compared with a down-range-responsive cross-range threshold of a boundary of a collision-possible space and the pixel or element is nulled or ignored if associated with a location that is not in the collision-possible space. The collision-possible space is responsive to an operating condition of a vehicle from which the image is acquired.

Abstract: An image of a visual scene, comprising a plurality of pixels. is acquired and an associated range map is either determined therefrom or separately acquired. Elements of the range map comprise distances from the camera for each pixel of the image. In one aspect, either the image or the range map is processed with a connected-components sieve filter that locates clusters of pixels or elements that are connected to one another along either adjacent rows, columns or diagonally. In another aspect, a cross-range value of range-map element is compared with a down-range-responsive cross-range threshold of a boundary of a collision-possible space and the pixel or element is nulled or ignored if associated with a location that is not in the collision-possible space. The collision-possible space is responsive to an operating condition of a vehicle from which the image is acquired.

Abstract: A range map of a visual scene generated by a stereo vision and associate image processing system, and is filtered to remove objects beyond a region of interest and for which a collision is not possible, and to remove an associated road surface. Objects clustered in range bins are separated by segmentation. A composite range map is generated using principale components analysis and processed with a connected-components sieve filter. Objects are identified using one or more of a harmonic profile and other features using an object recognition processor using a combination of inclusive, exclusive and harmonic networks to generate a classification metric.

Abstract: An image processing system adapted to image an area around a vehicle includes an image processor adapted to receive information indicative of an image of an area proximate to the vehicle automatically captured by an image capturing device. The image processor includes logic to analyze the information indicative of the image proximate to the vehicle and automatically determine whether the information indicative of the image of an area proximate to the vehicle is indicative of at least one predetermined environment. This image processing system can be utilized for crash prevention and crash recording. This image processing system can be utilized to identify an increased likelihood of crash and/or the occurrence of a crash and/or to implement crash avoidance and/or crash safety devices.

Abstract: A range map of a visual scene generated by a stereo vision and associate image processing system, and is filtered to remove objects beyond a region of interest and for which a collision is not possible, and to remove an associated road surface. Objects clustered in range bins are separated by segmentation. A composite range map is generated using principle components analysis and processed with a connected components sieve filter. Objects are identified using one or more of a harmonic profile and other features using an object recognition processor using a combination of inclusive, exclusive and harmonic networks to generate a classification metric.

Abstract: A camera (32) captures successive images of light stripes (22) projected onto an object by a light curtain (18) positioned by a light source positioner (28). A background image is subtracted (616) therefrom, and the resulting image is boosted by binning (618), binarized with a thresholding algorithm (620), skeletonized (622), interpolated (624) and stored (626). Interpolated images are acquired for a plurality of light stripes (22). A processor (30) generates (1604) a 3-D surface model from Cartesian coordinates computed for non-zero camera pixels. A volumetric representation is determined (1610) from the offset of the object surface model relative to a model of a proximate surface, e.g. a seating surface (24). The object is classified (1614), e.g. by a trainable pattern recognitions system, responsive to 3-D shape descriptors (1606) of the 3-D surface model and to the volumetric representation (1610) or portions (1612) thereof.

Abstract: An eye-safe light curtain is generated by a plurality of LED's illuminating a cylindrical lens having a corrugated cylindrical surface. A camera generates a first image of an avoidance zone boundary of an air bag inflator with the light curtain off, and a second image of with the light curtain on along the avoidance zone boundary. The first image is subtracted from the second image, and the resulting difference image is binned to enhance signal strength. A penetration of the avoidance zone is classified from features of the leading edge of the binned image, and the air bag inflator is disabled if the avoidance zone is penetrated by an occupant at risk of injury from the deployment thereof. An image of the light curtain may be recovered for high ambient lighting conditions by adapting the exposure time of the camera responsive to the percentage of pixels in a mask region of the first image having values exceeding a threshold.