A high-speed algorithm for extracting line segment features in an image is described. The extracted line segments are used as the geometric features for identifying and locating objects of interest, specifically in a Model Based Vision system. The algorithm is divided into two parts with each part being performed using separate computer hardware. The first part of the algorithm involves locating pixels in an image which correspond to edges. This part of the algorithm was implemented on the Datacube MaxVideo-20 pipelined image processing hardware executing in real time. The second part of the algorithm involves the extraction of the edge pixels in a connected manner so that line segments can be identified. This part of the algorithm was implemented in software on a Sun Sparc 2 workstation using a run-length encoded image and a chain-code mapped image generated by the Datacube MaxVideo-20 hardware.
[1]
Sunanda Mitra,et al.
Fast Implementation Of A Laplacian Of Gaussian Edge Detector
,
1990,
Optics & Photonics.
[2]
Gérard G. Medioni,et al.
Refining edges detected by a LoG operator
,
1988,
Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.
[3]
Michael Magee,et al.
Real-time model-based vision for industrial domains
,
1993,
Other Conferences.
[4]
Paul Wintz,et al.
Instructor's manual for digital image processing
,
1987
.
[5]
David G. Lowe,et al.
Three-Dimensional Object Recognition from Single Two-Dimensional Images
,
1987,
Artif. Intell..
[6]
D Marr,et al.
Theory of edge detection
,
1979,
Proceedings of the Royal Society of London. Series B. Biological Sciences.