Paper Info
Title
Spatio-Temporal Segmentation with Edge Relaxation and Optimization Using Fully Parallel Methods
Abstract
In this paper, we outline a fully parallel and locally connected computation model for the spatio-temporal segmentation of motion events in video sequences. We are searching for a new algorithm, which can be easily implemented in one-pixel/one-processor cell-array VLSI architectures at high-speed. Our proposed algorithm starts from an oversegmented image, then the segments are merged by applying the information coming from the spatial and temporal auxiliary data: motion fields and motion history, which is calculated from consecutive image frames. This grouping process is defined through a similarity measure of neighboring segments, which is based on the values of intensity, speed and the time-depth of motion history. As for checking the merging process there is a feedback implemented, by that we can accept or refuse the cancellation of a segment-border. Our parallel approach is independent of the number of segments and objects, since instead of graph representation and serial processing of these components, image features are defined on the pixel-level. We use simple functions, easily realizable in VLSI, like arithmetic and logical operators, local memory transfers and convolution.
Year
DOI
Venue
2000
10.1109/ICPR.2000.903043
ICPR
Keywords
Field
DocType
motion field,edge relaxation,oversegmented image,motion event,vlsi architecture,image feature,parallel approach,new algorithm,grouping process,consecutive image frame,motion history,parallel methods,spatio-temporal segmentation,history,optimization,computer model,convolution,parallel algorithms,merging,image features,very large scale integration,image segmentation,graph representation,group process,concurrent computing,computational modeling,computer architecture,serial processing,feedback,vlsi
Computer vision,Similarity measure,Feature (computer vision),Serial memory processing,Computer science,Parallel algorithm,Convolution,Segmentation,Image segmentation,Artificial intelligence,Graph (abstract data type)
Conference
ISSN
Citations 
PageRank 
1051-4651
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Sziranyi, T.139544.76
László Czuni26813.41