Paper Info
Title
Stereo and image matching on fixed size linear arrays
Abstract
The authors present parallel techniques to implement two vision tasks; stereo matching and image matching using linear features as primitives. The implementations are performed on a fixed size linear array and achieve processor-time optimal performance. For stereo matching, they propose an O(Nn3/P) time algorithm on a P-processor linear array, where N is the number of line segments in one image, n is the number of line segments in a window determined by the object size, and P⩽n. The sequential algorithm takes O(Nn3) time. They also propose a partitioned parallel implementation of image matching with an O((nm/P+P)nm) time performance achieved on a P-processor linear array, where n is the number of line segments in the image, m is the number of line segments in the model and P⩽nm. This leads to a processor-time optimal solution for P⩽√nm. Previously known approaches to the image matching problem take O(n3m3) time
Year
DOI
Venue
1993
10.1109/IPPS.1993.262833
Newport, CA
Keywords
Field
DocType
line segments,navigation,machine vision,robots,computational complexity,object recognition,image recognition,image segmentation,parallel processing,data mining,photometry,parallel algorithms
Template matching,Line segment,Computer vision,Machine vision,Parallel algorithm,Computer science,Image segmentation,Artificial intelligence,Robot,Computational complexity theory,Cognitive neuroscience of visual object recognition
Conference
ISBN
Citations 
PageRank 
0-8186-3442-1
0
0.34
References 
Authors
9
2
Name
Order
Citations
PageRank
Ashfaq A. Khokhar11008108.60
Wei-Ming Lin211.70