Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A method for reduction of color space dimensionality by moment-preserving thresholding and its application in edge detection for color images is proposed. An input color image is partitioned into n x n non-overlapping blocks. A moment-preserving thresholding technique is then applied individually to each color plane of each image block. Two sets of (R, G, B) tristimulus values are obtained from the thresholding results to form two representative color vectors for each block. The difference vector beten these two representative color vectors is used as an axis onto which all the data in the block are projected to reduce the color space to one dimension. A single-spectral image block is so obtained. Due to the use of analytic formulas in the thresholding step, the proposed dimensionality reduction method is found faster than the KL expansion or vector median approaches which are also applicable for dimensionality reduction. An (n + 1)X (n+ 1) circular window is selected to sample the resulting single-spectral image, which in turn includes the n × n square block. Some mass moments of the window data are computed and used for edge detection in the circular window. Due to the use of the larger detection window which results in smaller overlapping detection areas, the computation time for the edge detection step is reduced, compared with other similar approaches using overlapping detection windows. Experimental results show that the proposed approach is effective in reduction of color space dimensionality and edge detection in color images.
['Chen-Kuei Yang', 'Wen-Hsiang Tsai']
I am writing a research paper on a idea Reduction of color space dimensionality by moment-preserving thresholding and its application for edge detection in color images
[{"from": "human", "value": "I am writing a research paper on a idea Reduction of color space dimensionality by moment-preserving thresholding and its application for edge detection in color images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A method for reduction of color space dimensionality by moment-preserving thresholding and its application in edge detection for color images is proposed. An input color image is partitioned into n x n non-overlapping blocks. A moment-preserving thresholding technique is then applied individually to each color plane of each image block. Two sets of (R, G, B) tristimulus values are obtained from the thresholding results to form two representative color vectors for each block. The difference vector beten these two representative color vectors is used as an axis onto which all the data in the block are projected to reduce the color space to one dimension. A single-spectral image block is so obtained. Due to the use of analytic formulas in the thresholding step, the proposed dimensionality reduction method is found faster than the KL expansion or vector median approaches which are also applicable for dimensionality reduction. An (n + 1)X (n+ 1) circular window is selected to sample the resulting single-spectral image, which in turn includes the n \u00d7 n square block. Some mass moments of the window data are computed and used for edge detection in the circular window. Due to the use of the larger detection window which results in smaller overlapping detection areas, the computation time for the edge detection step is reduced, compared with other similar approaches using overlapping detection windows. Experimental results show that the proposed approach is effective in reduction of color space dimensionality and edge detection in color images."}]