Title :
Object recognition and localization from scanning beam sensors
Author :
Wallack, Aaron S. ; Canny, John F.
Author_Institution :
Comput. Sci. Div., California Univ., Berkeley, CA, USA
Abstract :
Model based object recognition and object localization are fundamental problems in industrial automation. The authors present techniques which use a scanner composed of binary light beam sensors to quickly recognize objects (as fast as 5 microseconds) and to accurately localize objects (0.025 millimeters), and they describe localization and recognition experiments and present results. Binary sensors only sense whether the part is present or absent at a particular location, and their high performance is due to their simple specification. Fast recognition is achieved by using indexing to solve the correspondence problem, the problem of interpreting the sensed data as model features; indexing involves using the sensed data to directly look up the correspondence information using a precomputed indexing table. Since each experiment only produces a single indexing vector, indexing tables need to be complete; in this paper the authors detail a complete indexing construction method for flat polygonal and polyhedral objects
Keywords :
computer vision; image sensors; object recognition; binary light beam sensors; binary sensors; correspondence problem; flat polygonal objects; industrial automation; model features; object localization; object recognition; polyhedral objects; precomputed indexing table; scanning beam sensor; Computer industry; Computer science; Construction industry; Heart; Indexing; Machine vision; Object recognition; Robotics and automation; Sensor phenomena and characterization; Structural beams;
Conference_Titel :
Robotics and Automation, 1995. Proceedings., 1995 IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-1965-6
DOI :
10.1109/ROBOT.1995.525293