Gm Global Technolgoy Operations, Inc.

Hrl Laboratories, LLC

Zhichao Chen

Deepak Khosla

Kyungnam Kim

Yang Chen

David W Payton

Ryan M Uhlenbrock

Heiko Hoffmann

Swarup S Medasani

Jiejun Xu

Leandro G Barajas

Rashmi N Sundareswara

Jason Meltzer

Darrel J VanBuer

Li Yang Ku

Described is system for real-time automated exposure adjustment of a camera using contrast entropy. Multiple images are captured using an image sensor, where each of the images is captured at a distinct exposure value. A contrast entropy is determined for each of the multiple images. An image having the largest contrast entropy is selected among the images and output.

Real-time auto exposure adjustment of camera using contrast entropy

A method for localizing and estimating a pose of a known object in a field of view of a vision system is described, and includes developing a processor-based model of the known object, capturing a bitmap image file including an image of the field of view including the known object, extracting features from the bitmap image file, matching the extracted features with features associated with the model of the known object, localizing an object in the bitmap image file based upon the extracted features, clustering the extracted features of the localized object, merging the clustered extracted features, detecting the known object in the field of view based upon a comparison of the merged clustered extracted features and the processor-based model of the known object, and estimating a pose of the detected known object in the field of view based upon the detecting of the known object.

Method for object localization and pose estimation for an object of interest

A machine vision system for a controllable robotic device proximal to a workspace includes an image acquisition sensor arranged to periodically capture vision signal inputs each including an image of a field of view including the workspace. A controller operatively couples to the robotic device and includes a non-transitory memory component including an executable vision perception routine. The vision perception routine includes a focus loop control routine operative to dynamically track a focus object in the workspace and a background loop control routine operative to monitor a background of the workspace. The focus loop control routine executes simultaneously asynchronously in parallel with the background loop control routine to determine a combined resultant including the focus object and the background based upon the periodically captured vision signal inputs. The controller is operative to control the robotic device to manipulate the focus object based upon the focus loop control routine.

Robotic device including machine vision

A method for recognizing a known object in a field of view of a three-dimensional (3D) vision system includes capturing a bitmap image file of the field of view and executing a first feature extraction to generate appearance features including a color histogram and an edge direction histogram. A reduced search space is identified based upon the appearance features including the color histogram and the edge direction histogram. A second feature extraction of the bitmap image file is executed to identify geometric features including a viewpoint feature histogram and an ensemble of shape functions. The appearance features and the geometric features are fused with contents of the reduced search space. The fused appearance features, geometric features and contents of the reduced search space are compared with a plurality of predetermined partial views of a plurality of known objects to recognize one of the known objects.

Method and device for recognizing a known object in a field of view of a three-dimensional machine vision system

Described is system for image de-hazing for real-time video processing. A video comprising image frames is received as input. In determining an atmospheric veil value to use, the system leverages temporal correlations or specific scene constraints to determine whether to use a new atmospheric veil value or a previously determined atmospheric veil value. A global atmospheric light value is estimated as an average of a set of the brightest pixels in an upper region of an image frame. A de-hazed image is generated from the current image frame using either the previously determined atmospheric veil value or the new atmospheric veil value and the global atmospheric light value.

Fast digital image de-hazing methods for real-time video processing

Described is a system for localizing and controlling a tool tip with a robotic arm. The system receives three-dimensional (3D) visual information of a scene having a tool with a tool tip proximate a target. A depth disparity map is generated based on the 3D visual information. The depth disparity map is filtered by depth continuity to generate boundaries of the tool. The boundaries of the tool are initially loose to ensure that the tool is contained within the boundaries. Thereafter, using level set segmentation, the boundaries of the tool are shrunk and extracted. Ends of the tool tip are identified based on the extracted boundaries, which are then used to generate a pose of the tool tip.

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Woodland Hills, CA, United States of America

Zhichao Chen