Maximum segmented-scene spatial entropy thresholding

Chi Kin Leung; F. K. Lam

Maximum segmented-scene spatial entropy thresholding

Chi Kin Leung, F. K. Lam

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

The Segmented-scene Spatial Entropy (SSE) is defined as the amount of information contained in the spatial structure of a segmented scene resulted from segmenting an image. An automatic, non-parametric, unsupervised thresholding algorithm that maximizes the SSE of an image is described, and this algorithm is known as the Maximum Segmented-scene Spatial Entropy (MSSE) thresholding algorithm. It is shown that the MSSE-thresholded image contains the maximum amount of information about the original scene and hence good thresholding results are warranted. Simulation and practical results are presented to illustrate the improvement in performance as compared to some other histogram-based thresholding algorithms.

Original language	English
Title of host publication	IEEE International Conference on Image Processing
Publisher	IEEE
Pages	963-964
Number of pages	2
Publication status	Published - 1 Dec 1996
Event	Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switzerland Duration: 16 Sep 1996 → 19 Sep 1996

Conference

Conference	Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
Country	Switzerland
City	Lausanne
Period	16/09/96 → 19/09/96

ASJC Scopus subject areas

Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Cite this

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title = "Maximum segmented-scene spatial entropy thresholding",

abstract = "The Segmented-scene Spatial Entropy (SSE) is defined as the amount of information contained in the spatial structure of a segmented scene resulted from segmenting an image. An automatic, non-parametric, unsupervised thresholding algorithm that maximizes the SSE of an image is described, and this algorithm is known as the Maximum Segmented-scene Spatial Entropy (MSSE) thresholding algorithm. It is shown that the MSSE-thresholded image contains the maximum amount of information about the original scene and hence good thresholding results are warranted. Simulation and practical results are presented to illustrate the improvement in performance as compared to some other histogram-based thresholding algorithms.",

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AU - Leung, Chi Kin

AU - Lam, F. K.

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N2 - The Segmented-scene Spatial Entropy (SSE) is defined as the amount of information contained in the spatial structure of a segmented scene resulted from segmenting an image. An automatic, non-parametric, unsupervised thresholding algorithm that maximizes the SSE of an image is described, and this algorithm is known as the Maximum Segmented-scene Spatial Entropy (MSSE) thresholding algorithm. It is shown that the MSSE-thresholded image contains the maximum amount of information about the original scene and hence good thresholding results are warranted. Simulation and practical results are presented to illustrate the improvement in performance as compared to some other histogram-based thresholding algorithms.

AB - The Segmented-scene Spatial Entropy (SSE) is defined as the amount of information contained in the spatial structure of a segmented scene resulted from segmenting an image. An automatic, non-parametric, unsupervised thresholding algorithm that maximizes the SSE of an image is described, and this algorithm is known as the Maximum Segmented-scene Spatial Entropy (MSSE) thresholding algorithm. It is shown that the MSSE-thresholded image contains the maximum amount of information about the original scene and hence good thresholding results are warranted. Simulation and practical results are presented to illustrate the improvement in performance as compared to some other histogram-based thresholding algorithms.

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M3 - Conference article published in proceeding or book

SP - 963

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BT - IEEE International Conference on Image Processing

PB - IEEE

T2 - Proceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)

Y2 - 16 September 1996 through 19 September 1996

ER -

Maximum segmented-scene spatial entropy thresholding

Abstract

Conference

ASJC Scopus subject areas

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