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 |
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| 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) |
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| Country/Territory | 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