Abstract:
A new method for segmenting images using abrupt changes in intensity (edge points) to separate regions of smoothly varying intensity is discussed. Region segmentation usi...Show MoreMetadata
Abstract:
A new method for segmenting images using abrupt changes in intensity (edge points) to separate regions of smoothly varying intensity is discussed. Region segmentation using edge points has not been very successful in the past because small gaps would allow merging of dissimilar regions. The present method uses an expansion-contraction technique in which the edge regions are first expanded to close gaps and then contracted after the separate uniform regions have been identified. In order to preserve small uniform regions, the process is performed iteratively from small to large expansions with no expansion for edge regions that separate different uniform regions. The final result is a set of uniform intensity regions (usually less than 100) and a set of edge boundary regions. The program has successfully segmented scenes with industrial parts, landscapes, and integrated circuit chips.
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: PAMI-2, Issue: 1, January 1980)
Citations are not available for this document.
Cites in Patents (1)Patent Links Provided by 1790 Analytics
1.
Gewaltig, Marc-Oliver; Korner, Edgar; Korner, Ursula, "PREPARATION OF A DIGITAL IMAGE WITH SUBSEQUENT EDGE DETECTION"
Inventors:
Gewaltig, Marc-Oliver; Korner, Edgar; Korner, Ursula
Abstract:
For object recognition, an image is segmented into areas of similar homogeneity at a coarse scale, which are then interpreted as surfaces. Information from different spatial scales and different image features is simultaneously evaluated by exploiting statistical dependencies on their joint appearance. Thereby, the local standard deviation of specific gray levels in the close environment of an observed pixel serves as a measure for local image homogeneity that is used to get an estimate of dominant global object contours. This information is then used to mask the original image. Thus, a fine-detailed edge detection is only applied to those parts of an image where global contours exist. After that, said edges are subject to an orientation detection. Moreover, noise and small details can be suppressed, thereby contributing to the robustness of object recognition.
Assignee:
HONDA RESEARCH INSTITUTE EUROPE GMBH
Filing Date:
05 June 2003
Grant Date:
08 April 2008
Patent Classes:
Current U.S. Class:
382199000, 382264000, 382266000, 382274000
Current International Class:
G06K0094800