Fast Automated Object Detection Using Signature Parsing

Tim Heaton; Shawn Becker; Kelley Anderson; William Barrett

doi:10.1117/12.969720

1 March 1990 Fast Automated Object Detection Using Signature Parsing

Tim Heaton, Shawn Becker, Kelley Anderson, William Barrett

Proceedings Volume 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques; (1990) https://doi.org/10.1117/12.969720
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

A fast algorithm is described which makes use of image signatures or profiles for automated object detection. Following segmentation, signatures are generated with respect to both axes and transition points are marked to separate the signature into bands. The intersections of the bands define rectangular regions (subimages) which may contain objects or groups of objects. Signature parsing is repeated for each subimage until single band intersections are produced at which point each object is naturally bounded by the band limits. Recursive decomposition of the image in this manner allows fast location of objects and calculation of object parameters while avoiding pixel level processing. The output of the algorithm is a two-dimensional spatial object relationship tree (SORT) which contains a high-level hierarchical description of the spatial interrelationships between objects and groups of objects. The SORT provides a powerful tool for scene matching and can be used to distribute subimages (nodes) to multiple processors. The algorithm has been used for efficient detection of objects and their locations in picking and placing tasks with a (PC) compute time of 1-2 seconds for a typical image.

Citation Download Citation

Tim Heaton, Shawn Becker, Kelley Anderson, and William Barrett "Fast Automated Object Detection Using Signature Parsing", Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); https://doi.org/10.1117/12.969720

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available