Biomorphic Vlsi, Inc.

Hughes Aircraft Company

Rockwell International Corporation

Hughes Missile Systems Company

Craig Charles Reinhart

David Lawrence Standley

James F Alves

Thomas Eugene Hubina

H Taichi Wang

Bimal P Mathur

Jerry A Burman

Shih-Chii Liu

Walter W Tackett

Michele K Daniels

Victoria Gor

Manjunath S Bhat

Hung Yu Chou

Brian J Birdsall

Bruce A Berger

James F Frazier

A method and system for converting a patterned color image in a first color space to a second color space, prior to image compression is provided. The process includes, dividing the image into plural tiles; converting the plural tiles from the first color space to the second color space; retaining certain pixel values in the second color space; discarding certain pixel values in the second color space; and creating a pattern in the second color space, similar to the patterned color image in the first color space. Also, a method for decoding a compressed image pattern from a first color space to second color space is provided. The method includes, assembling pixels from a first pattern based on the first color space; transforming triplets form the first color space to the second color space; and determining pixel values for a dominant color.

Method and system for color space conversion of patterned color images

A method and system for automatic bad pixel correction in image sensors is provided. The process includes identifying outlier pixels, identifying bad pixels, and performing bad pixel correction. Bad pixels are identified by comparing pixels in a single row or more than one row. A bad pixel value is replaced by a pixel value that depends on the pixel value of non-bad pixels located next to the bad pixel. The system includes means for identifying outlier pixels, means for identifying bad pixels, and means for performing bad pixel correction.

Automatic bad pixel correction in image sensors

A system and method for processing data representative of a color image is disclosed. Image data represents an intensity of photoexposure of an imaging array at specific locations in the imaging array and in distinct spectral regions corresponding to color channels. A process identifies 'white' regions in the color image by comparing the intensities of photoexposure of groups of associated pixels which are responsive to photon energy in different spectral regions. If the intensities of photoexposure of the pixels in the group of associated pixels are proportionally equivalent, these pixels are determined to be in a white region of the image. White balancing gain coefficients are then based upon the pixel intensity values at pixel locations in the white regions of the image.

Pixel selective white balancing

Described herein are a system and method for reconstructing a color image from sparse image data collected at an imaging sensor which provides intensity data for exactly one color for each pixel location. A pattern for color selection preferably defines an assignment of color information to each pixel location from neighboring pixels. Thus, the color selection pattern provides an assignment of multicolor image data for each pixel location including color information collected at the pixel location in the imaging sensor and color information selected from neighboring pixels according to the color selection pattern. The color selection pattern may be formulated to minimize the effect of color artifacts in the resulting reconstructed image.

Color selection for sparse color image reconstruction

An automated license plate locator and reader (10) which provides novel methods for 1) correcting for perspective distortion (16), 2) locating the license plate (20), 3) reading the license plate (22) and 4) improving the confidence rating of the output signal (228). The novel method for accommodating a wide range of viewing geometries involves warping the image to eliminate perspective distortion of the plate resulting from roadside camera placement. The novel method for locating plates involves correlating an 'idealized' edge template of a license plate with an edge-enhanced version of the image. Edge enhancement and shadow reduction is achieved by applying a Laplacian operator over the input image. Performance is further improved by filtering out distracting edges within the resulting correlation surface and then locating the highest peaks within this filtered correlation surface. The location of the highest peaks provides the location of the plate. The novel method for reading the license plates involves performing a modified binary correlation of an image of the plate with reference characters to provide output signals indicative of the symbols on the plate. This output is correlated with valid license plate signals to improve the confidence rating thereof.

Automated license plate locator and reader including perspective

A vehicular traffic monitoring system incorporates an array of photosensors and a nonlinear resistive network for identifying, locating, and processing outliers in sensor images of a highway or intersection. The camera system can be mounted on a pole or overpass to provide an image of the roadway or intersection. Areas of the outlier network ('video loops') are designated to correspond to selected areas of the roadway. Images are received by the outlier detection network with all data path switches closed between sensor elements and their corresponding network nodes. The system detects the presence of objects in the image by comparing the brightness or intensity of each pixel with that of the background. If the intensity of a pixel is significantly different from the background level, the data path switch corresponding to that pixel is opened. A readout of the state of all the switches in the network yields a map of outlier points for each video frame. The outlier map is connected to a data processing system to identify and locate outlier points in the image. The detection of a threshold number of outliers in a video loop indicates the presence of a vehicle at the corresponding area of the roadway. The processor, having a greatly reduced computational load without extensive image processing, simply measures and transmits traffic data such as the number and speed of vehicles passing through the video loops.

Vehicular traffic monitoring system

Image data is processed by a low level feature detection processor that extracts low level features from an image. This is accomplished by converting a matrix of image data into a matrix of orthogonal icons that symbolically represent the image scene using a predetermined set of attributes. The orthogonal icons serve as the basis of processing by means of a high level graph matching processor which employs symbolic scene segmentation, description, and recognition processing that is performed subsequent to the low level feature detection. This processing generates attribute graphs representative of target objects present in the image scene. High level graph matching compares predetermined attributed reference graphs to the sensed graphs to produce a best common subgraph between the two based on the degree of similarity between the two graphs. The high level graph matching generates a recognition decision based on the value of the degree of similarity and a predetermined threshold. The output of the high level graph matching provides data from which a target aimpoint is determined, and this aimpoint is coupled as an input to a missile guidance system that tracks identified targets.

Growing community of inventors

Moorpark, CA, United States of America

Craig Charles Reinhart