Abstract: An apparatus for directing uniform intensity light onto a surface where the light is provided at an acute angle with respect to the surface including a light source that generates initial light rays that are essentially perpendicular to the surface and a guidance member positioned between the source and the surface which receives the initial rays and redirects the rays such that redirected rays are parallel and form acute angles with respect to the surface, the source and guidance member juxtaposed with respect to the surface so that the redirected rays subtend the surface.

Abstract: A method and apparatus for classification and tracking objects in three-dimensional space is described. A machine vision system acquires images from roadway scenes and processes the images by analyzing the intensities of edge elements within the image. The system then applies fuzzy set theory to the location and angles of each pixel after the pixel intensities have been characterized by vectors. A neural network interprets the data created by the fuzzy set operators and classifies objects within the roadway scene. The system can also track objects within the roadway scene, such as vehicles, by forecasting potential track regions and then calculating match scores for each potential track region based on how well the edge elements from the target track regions match those from the source region as weighted by the extent the edge elements have moved.

Abstract: A method and apparatus for producing a background image from a plurality of images of a scene and for subtracting a background image from an input image are described. A background image is produced by dividing an image into subimages, acquiring reference subimages for each subimage location and comparing subsequent subimages with the reference subimage to determine if any objects have passed between the reference subimage and the video camera that acquired images. When objects have passed between the reference subimage and the video camera, the reference subimage is designated as background and stored in a background image. Background portions of an input image can be removed or their intensity diminished with a background image. Foreground weights can be determined by comparing the difference between a background image and an input image. To the extent that corresponding pixels are the same, the pixel is given a low foreground weight, indicating that the pixel is a background weight.

Abstract: A calibration apparatus for calibrating three-dimensional space for a machine vision system and a method for using the calibration apparatus for calibrating the machine vision system are disclosed. The calibration apparatus comprises pairs of indicators, such as lights, mounted on a frame, each pair of lights separated by a known horizontal distance. The calibration apparatus may be mounted on the back of a vehicle that moves the apparatus through a roadway scene at a known speed. A machine vision system tracks the calibration apparatus, specifically the pairs of lights mounted on the frame, and maps the pixel space of the machine vision system to three-dimensional space.

Abstract: A calibration apparatus for calibrating three-dimensional space for a machine vision system and a method for using the calibration apparatus for calibrating the machine vision system are disclosed. The calibration apparatus comprises pairs of indicators, such as lights, mounted on a frame, each pair of lights separated by a known horizontal distance. The calibration apparatus may be mounted on the back of a vehicle that moves the apparatus through a roadway scene at a known speed. A machine vision system tracks the calibration apparatus, specifically the pairs of lights mounted on the frame, and maps the pixel space of the machine vision system to three-dimensional space.

Abstract: A method and apparatus for classification and tracking objects in three-dimensional space is described. A machine vision system acquires images from roadway scenes and processes the images by analyzing the intensities of edge elements within the image. The system then applies fuzzy set theory to the location and angles of each pixel after the pixel intensities have been characterized by vectors. A neural network interprets the data created by the fuzzy set operators and classifies objects within the roadway scene. The system can also track objects within the roadway scene, such as vehicle, by forecasting potential track regions and then calculating match scores for each potential track region based on how well the edge elements from the target track regions match those from the source region as weighted by the extent the edge elements have moved.