Network Simulator NS2

An image sensor for capturing a color image is disclosed having a two-dimensional array having first and second groups of pixels wherein pixels from the first group of pixels have narrower spectral photoresponses than pixels from the second group of pixels and wherein the first group of pixels has individual pixels that have spectral photoresponses that correspond to a set of at least two colors, with the placement of the first and second groups of pixels defining a pattern that has a minimal repeating unit including at least six pixels with at least some rows or columns of the minimal repeating unit composed only of pixels from the second group of pixels, and including ways to combine similarly positioned pixels from at least two adjacent minimal repeating units.

Image sensor with improved light sensitivity

Techniques for factoring one or more source graphs into a composite graph containing nodes representing analogous elements of the source graphs and a variability graph containing nodes representing differences in the source graphs. The composite graph is made by taking analogous input trees from the source graphs and traversing the trees from top to bottom looking for nodes in each tree at each level that are analogous to the nodes at that level in the other input trees. The sets of analogous nodes are found by first automatically correlating the nodes in the level currently being examined. Correlation may, for example, be based on similar values of a property of the nodes being correlated. Representations of the sets of correlated nodes are then displayed to a user, who indicates which sets of correlated nodes are in fact analogous. The user may also indicate that the nodes in a set of correlated nodes are not analogous or that nodes that were found by the automatic correlation not to be autonomous are in fact. The analogous nodes are allocated to a corresponding node at a corresponding level in the composite graph; the other nodes are allocated to a set of anomalous nodes. One application for the techniques is managing graphs which are models of catalogs of items.

Conceptual factoring and unification of graphs representing semantic models

A stereo vision system that attempts to achieve robustness with respect to scene characteristics, from textured outdoor scenes to environments composed of highly regular man-made objects is presented. It integrates area-based and feature-based primitives. The area-based processing provides a dense disparity map, and the feature-based processing provides an accurate location of discontinuities. An area-based cross correlation, an ordering constraint, and a weak surface smoothness assumption are used to produce an initial disparity map. This disparity map is only a blurred version of the true one because of the smoothing introduced by the cross correlation. The problem can be reduced by introducing edge information. The disparity map is smoothed and the unsupported points removed. This method gives an active role to edgels parallel to the epipolar lines, whereas they are discarded in most feature-based systems. Very good results have been obtained on complex scenes in different domains. >

3-D surface description from binocular stereo

A traffic violation processing method is disclosed, which includes the steps of monitoring a vehicle; determining whether the vehicle is in violation of a traffic violation and recording the image of the vehicle on a camera when the vehicle is determined to have violated a traffic violation; providing a digital image of the vehicle; recording and storing deployment data corresponding to the violation, wherein the deployment data includes an identifier associated with the traffic violation; assigning the identifier to the digital traffic image; storing the digital image of the vehicle, together with the identifier, on a storage medium; matching the deployment data with the stored digital image by correlating or matching the identifier associated with the deployment data with the identifier associated with the stored digital image; and generating a traffic document that includes the stored digital image and the deployment data.

Traffic violation processing system

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 distortion correction

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.

Pattern recognition apparatus utilizing area linking and region growth techniques

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

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.

Craig C. Reinhart

Papers

Automated license plate locator and reader including perspective distortion correction

Pattern recognition apparatus utilizing area linking and region growth techniques

Pixel selective white balancing

Color selection for sparse color image reconstruction

Automatic bad pixel correction in image sensors