EdgeFlow: a technique for boundary detection and image segmentation
A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flo...
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| Veröffentlicht in: | IEEE transactions on image processing 2000-08, Vol.9 (8), p.1375-1388 |
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| container_title | IEEE transactions on image processing |
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| creator | Wei-Ying Ma Manjunath, B.S. |
| description | A novel boundary detection scheme based on "edge flow" is proposed in this paper. This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. Segmentation results on a large and diverse collections of natural images are provided, demonstrating the usefulness of this method to content based image retrieval. |
| doi_str_mv | 10.1109/83.855433 |
| format | Article |
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This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. 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This scheme utilizes a predictive coding model to identify the direction of change in color and texture at each image location at a given scale, and constructs an edge flow vector. By propagating the edge flow vectors, the boundaries can be detected at image locations which encounter two opposite directions of flow in the stable state. A user defined image scale is the only significant control parameter that is needed by the algorithm. The scheme facilitates integration of color and texture into a single framework for boundary detection. 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Computational geometry</topic><topic>Physics</topic><topic>Position (location)</topic><topic>Predictive coding</topic><topic>Predictive models</topic><topic>Surface layer</topic><topic>Texture</topic><topic>Vectors (mathematics)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wei-Ying Ma</creatorcontrib><creatorcontrib>Manjunath, B.S.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>MEDLINE - Academic</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on image processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wei-Ying Ma</au><au>Manjunath, B.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>EdgeFlow: a technique for boundary detection and image segmentation</atitle><jtitle>IEEE transactions on image processing</jtitle><stitle>TIP</stitle><addtitle>IEEE Trans Image Process</addtitle><date>2000-08-01</date><risdate>2000</risdate><volume>9</volume><issue>8</issue><spage>1375</spage><epage>1388</epage><pages>1375-1388</pages><issn>1057-7149</issn><eissn>1941-0042</eissn><coden>IIPRE4</coden><abstract>A novel boundary detection scheme based on "edge flow" is proposed in this paper. 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| ispartof | IEEE transactions on image processing, 2000-08, Vol.9 (8), p.1375-1388 |
| issn | 1057-7149 1941-0042 |
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| subjects | Application software Applied sciences Artificial intelligence Boundaries Computer science control theory systems Computer vision Computers in experimental physics Content based retrieval Exact sciences and technology Filtering Gabor filters Image edge detection Image processing Image retrieval Image segmentation Instruments, apparatus, components and techniques common to several branches of physics and astronomy Mathematical analysis Mathematical models Pattern recognition. Digital image processing. Computational geometry Physics Position (location) Predictive coding Predictive models Surface layer Texture Vectors (mathematics) |
| title | EdgeFlow: a technique for boundary detection and image segmentation |
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