Image Segmentation: Difference between revisions
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(Created page with "{{DISPLAYTITLE:Image Segmentation (Image Segmentation)}} == Description == Image segmentation is the division of an image into different regions, each having certain properties. It is the first step of image analysis which aims at either a description of an image or a classification of the image if a class label is meaningful. An example of the former is the description of an office scene. An example of the latter is the classification of the image of a cancerous cell....") |
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| [[Recursive Region Splitting ( Image Segmentation)|Recursive Region Splitting]] || 1978 || $O(n^{2})$ || || || Deterministic || | | [[Recursive Region Splitting ( Image Segmentation)|Recursive Region Splitting]] || 1978 || $O(n^{2})$ || || || Deterministic || | ||
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| [[Barghout; Lauren Visual Taxometric approach ( Image Segmentation)|Barghout; Lauren Visual Taxometric approach]] || 2014 || $O( | | [[Barghout; Lauren Visual Taxometric approach ( Image Segmentation)|Barghout; Lauren Visual Taxometric approach]] || 2014 || $O(n \log n)$ || || || Deterministic || | ||
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| [[Dual clustering - Guberman ( Image Segmentation)|Dual clustering - Guberman]] || 2012 || $O( | | [[Dual clustering - Guberman ( Image Segmentation)|Dual clustering - Guberman]] || 2012 || $O(n \log n)$ || || || Deterministic || | ||
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| [[R. Nock and F. Nielsen Statistical Region Merging ( Image Segmentation)|R. Nock and F. Nielsen Statistical Region Merging]] || 2004 || $O(n^{2})$ || || || Deterministic || | | [[R. Nock and F. Nielsen Statistical Region Merging ( Image Segmentation)|R. Nock and F. Nielsen Statistical Region Merging]] || 2004 || $O(n^{2})$ || || || Deterministic || | ||
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| [[Kass; Witkin and Terzopoulos ( Image Segmentation)|Kass; Witkin and Terzopoulos]] || 1987 || $O(n^{2})$ || || || Deterministic || | | [[Kass; Witkin and Terzopoulos ( Image Segmentation)|Kass; Witkin and Terzopoulos]] || 1987 || $O(n^{2})$ || || || Deterministic || | ||
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| [[Chen's lambda-connected segmentation ( Image Segmentation)|Chen's lambda-connected segmentation]] || 1991 || $O( | | [[Chen's lambda-connected segmentation ( Image Segmentation)|Chen's lambda-connected segmentation]] || 1991 || $O(n \log n)$ || || || Deterministic || | ||
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| [[S.L. Horowitz and T. Pavlidis - directed split and merge ( Image Segmentation)|S.L. Horowitz and T. Pavlidis - directed split and merge]] || 1974 || $O(n^{2})$ || || || Deterministic || | | [[S.L. Horowitz and T. Pavlidis - directed split and merge ( Image Segmentation)|S.L. Horowitz and T. Pavlidis - directed split and merge]] || 1974 || $O(n^{2})$ || || || Deterministic || | ||
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| [[Multiple Resolution segmentation - J. Liu and Y. H. Yang (1994) ( Image Segmentation)|Multiple Resolution segmentation - J. Liu and Y. H. Yang ()]] || 1994 || $O(n^{2})$ || || || Deterministic || | | [[Multiple Resolution segmentation - J. Liu and Y. H. Yang (1994) ( Image Segmentation)|Multiple Resolution segmentation - J. Liu and Y. H. Yang ()]] || 1994 || $O(n^{2})$ || || || Deterministic || | ||
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| [[Quasi-linear Topological watershed ( Image Segmentation)|Quasi-linear Topological watershed]] || 2005 || $O( | | [[Quasi-linear Topological watershed ( Image Segmentation)|Quasi-linear Topological watershed]] || 2005 || $O(n \log n)$ || || || Deterministic || | ||
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| [[Isometric graph partitioning - Leo Grady and Eric L. Schwartz (2006) ( Image Segmentation)|Isometric graph partitioning - Leo Grady and Eric L. Schwartz ()]] || 2006 || $O(n^{2})$ || || || Deterministic || | | [[Isometric graph partitioning - Leo Grady and Eric L. Schwartz (2006) ( Image Segmentation)|Isometric graph partitioning - Leo Grady and Eric L. Schwartz ()]] || 2006 || $O(n^{2})$ || || || Deterministic || | ||
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Latest revision as of 08:24, 10 April 2023
Description
Image segmentation is the division of an image into different regions, each having certain properties. It is the first step of image analysis which aims at either a description of an image or a classification of the image if a class label is meaningful. An example of the former is the description of an office scene. An example of the latter is the classification of the image of a cancerous cell. Image segmentation is a critical component of an image recognition system because errors in segmentation might propagate to feature extraction and classification.
Parameters
No parameters found.