Abstract: A vision system for a vehicle that identifies and classifies objects (targets) located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate depth maps of the scene proximate a vehicle. The depth maps are processed and compared to pre-rendered templates of target objects that could appear proximate the vehicle. A target list is produced by matching the pre-rendered templates to the depth map imagery. The system processes the target list to produce target size and classification estimates. The target is then tracked as it moves near a vehicle and the target position, classification and velocity are determined. This information can be used in a number of ways. For example, the target information may be displayed to the driver, the information may be used for an obstacle avoidance system that adjusts the trajectory or other parameters of the vehicle to safely avoid the obstacle.

Abstract: A vision system for a vehicle that identifies and classifies objects (targets) located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate depth maps of the scene proximate a vehicle. The depth maps are processed and compared to pre-rendered templates of target objects that could appear proximate the vehicle. A target list is produced by matching the pre-rendered templates to the depth map imagery. The system processes the target list to produce target size and classification estimates. The target is then tracked as it moves near a vehicle and the target position, classification and velocity are determined. This information can be used in a number of ways. For example, the target information may be displayed to the driver, the information may be used for an obstacle avoidance system that adjusts the trajectory or other parameters of the vehicle to safely avoid the obstacle.

Abstract: A vision system for a vehicle that identifies and classifies objects (targets) located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate depth maps of the scene proximate a vehicle. The depth maps are processed and compared to pre-rendered templates of target objects that could appear proximate the vehicle. A target list is produced by matching the pre-rendered templates to the depth map imagery. The system processes the target list to produce target size and classification estimates. The target is then tracked as it moves near a vehicle and the target position, classification and velocity are determined. This information can be used in a number of ways. For example, the target information may be displayed to the driver, the information may be used for an obstacle avoidance system that adjusts the trajectory or other parameters of the vehicle to safely avoid the obstacle.

Abstract: A method and apparatus for detecting a target in an image is disclosed. A plurality of depth images is provided. A plurality of target templates is compared to at least one of the plurality of depth images. A scores image is generated based on the plurality of target templates and the at least one depth image.

Abstract: A vision system for a vehicle that identifies and classifies objects (targets) located proximate a vehicle. The system comprises a sensor array that produces imagery that is processed to generate depth maps of the scene proximate a vehicle. The depth maps are processed and compared to pre-rendered templates of target objects that could appear proximate the vehicle. A target list is produced by matching the pre-rendered templates to the depth map imagery. The system processes the target list to produce target size and classification estimates. The target is then tracked as it moves near a vehicle and the target position, classification and velocity are determined. This information can be used in a number of ways. For example, the target information may be displayed to the driver, the information may be used for an obstacle avoidance system that adjusts the trajectory or other parameters of the vehicle to safely avoid the obstacle.

Abstract: A panoramic visualization system has multiple cameras with overlapping fields of view. A pointing device supplies view port direction information to a processing system, which blends the fields of view to produce panoramic view data that represents the panoramic view imaged by the cameras. The processing system also produces view port data along the view port direction. The processing system uses a vision processing board. A display device images the view port data to show an image area. The processing system beneficially corrects for the relative positions of the cameras, for the lens distortions of the individual cameras, and for roll, pitch, and yaw. The system may include an auto-track assembly that automatically moves the view port to track a moving object, while the processing system may enable multiple users to view multiple view ports. The cameras are beneficially mounted on a vehicle.

Abstract: The invention is an apparatus for providing X-server interface to a Unix server using any television from any location that has a connection between them. Providing flexible and economical computer access to the users. This connection can be achieved using standard telephone lines, local area network, Wireless connection, an Internet Service Provider or any other communication system that can get the connection in operational status.

Abstract: A method for fixating a camera, mounted on a motorized mount, on a target point. The method includes receiving a plurality of images representative of a scene; selecting, within the plurality of images, a first display reference image containing the target point at a first coordinate location; estimating, in a current image within the plurality of images, a transformation that maps the current image to the first display reference image; estimating a second coordinate location of the target point in the current image using the transformation; computing an image slip between the target point in the current image and the target point in the first display reference image; converting the image slip into an angular correction for fixating the camera; modifying the angular correction by using closed-loop control with the motorized mount; and warping the current image using the transformation to align the current image with the first display reference image.

Abstract: A system that estimates both the ego-motion of a camera through a scene and the structure of the scene by analyzing a batch of images of the scene obtained by the camera employs a correlation-based, iterative, multi-resolution algorithm. The system defines a global ego-motion constraint to refine estimates of inter-frame camera rotation and translation. It also uses local window-based correlation to refine the current estimate of scene structure. The batch of images is divided into a reference image and a group of inspection images. Each inspection image in the batch of images is aligned to the reference image by a warping transformation. The correlation is determined by analyzing respective Gaussian/Laplacian decompositions of the reference image and warped inspection images. The ego-motion constraint includes both rotation and translation parameters. These parameters are determined by globally correlating surfaces in the respective inspection images to the reference image.