Fast Computation of Moments on Compressed Grey Images using Block Representation |
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| Affiliation: | 2. University of Technology Sydney, Sydney, NSW, Australia;1. Key Laboratory of Industrial Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, PR China;2. Department of Physics and Electrical Engineering, Ningde Normal University, Ningde 352100, PR China;3. Beijing Institute of Control Engineering, Beijing 100029, PR China;1. Graduate Institute of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan;2. Department of Chinese Medicine, Show Chwan Memorial Hospital, Changhua, 50008, Taiwan;3. Ko Da Pharmaceutical Co. Ltd., Taoyuan, 32459, Taiwan;4. Graduate Institute of Pharmacognosy, Taipei Medical University, Taipei, 11031, Taiwan;5. Management Office for Health Data, China Medical University Hospital, Taichung, 40447, Taiwan;6. College of Medicine, China Medical University, Taichung, 40402, Taiwan;7. Chinese Medicine Research Center, China Medical University, Taichung, 40402, Taiwan;8. Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan;9. Research Center for Chinese Medicine and Acupuncture, China Medical University, Taichung, 40402, Taiwan;10. Department of Chinese Medicine, China Medical University Hospital, Taichung, 40447, Taiwan |
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| Abstract: | In image processing, moments are useful tools for analyzing shapes. Suppose that the input grey image with size N×N has been compressed into the compressed image using the block representation, where the number of blocks used is K, commonly K<N2 due to the compression effect. This paper presents an efficient O (N√K)-time algorithm for computing moments on the compressed image directly. Experimental results reveal a significant computational advantage of the proposed algorithm, while preserving a high accuracy of moments and a good compression ratio. The results of this paper extend the previous results in [7] from the binary image domain to the grey image domain. |
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