Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
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TL;DR: Overall security analysis and experimental results show that the proposed Chaos-based color multiple image encryption technique has achieved confidentiality and have resistance against classical attacks.
70 citations
Journal Article•
22 Feb 2009-World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
TL;DR: This paper includes how to determine the segmentation points in the histogram and the proposed algorithm has been tested with more than 100 images having various contrasts in the images and the results are compared to the conventional approaches to show its superiority.
Abstract: In order to enhance the contrast in the regions where the pixels have similar intensities, this paper presents a new histogram equalization scheme. Conventional global equalization schemes over-equalizes these regions so that too bright or dark pixels are resulted and local equalization schemes produce unexpected discontinuities at the boundaries of the blocks. The proposed algorithm segments the original histogram into sub-histograms with reference to brightness level and equalizes each sub-histogram with the limited extents of equalization considering its mean and variance. The final image is determined as the weighted sum of the equalized images obtained by using the sub-histogram equalizations. By limiting the maximum and minimum ranges of equalization operations on individual sub-histograms, the over-equalization effect is eliminated. Also the result image does not miss feature information in low density histogram region since the remaining these area is applied separating equalization. This paper includes how to determine the segmentation points in the histogram. The proposed algorithm has been tested with more than 100 images having various contrasts in the images and the results are compared to the conventional approaches to show its superiority.
70 citations
10 Dec 2002
TL;DR: It is demonstrated that the synergy resulting from the combination of structure, color andtexture is superior than using just color and texture.
Abstract: In this paper we combine structure, color and texture for efficient image retrieval. Structure is extracted by the application of perceptual grouping principles. Color analysis is performed by mapping all pixels in an image into a fixed color palette that uses linguistic tags to describe color content. Texture analysis is done using a bank of even-symmetric Gabor filters. A methodology for performance evaluation of these analyses is presented on a database of color images. The database has been partitioned into various classes and subclasses for quantifying the success of image query and classification. It is demonstrated that the synergy resulting from the combination of structure, color and texture is superior than using just color and texture.
70 citations
Proceedings Article•
01 Jan 2001TL;DR: This paper describes an approach to increase the noise robustness of automatic speech recognition systems by, transforming the signal after Mel scaled filtering, to make the cumulative density functions of the signal’s values in recognition match the ones that where estimated on the training data.
Abstract: This paper describes an approach to increase the noise robustness of automatic speech recognition systems by, transforming the signal after Mel scaled filtering, to make the cumulative density functions of the signal’s values in recognition match the ones that where estimated on the training data. The cumulative density functions are approximated using a small number of quantiles. Recognition tests on several databases showed significant reductions of the word error rates. On a real life database recorded in driving cars with a large mismatch between the training and testing conditions the relative reductions of the word error rates where over 60%.
70 citations
TL;DR: A new method of adaptive-neighborhood histogram equalization that is effective in enhancing these types of images when the image contains relatively small but variable-sized regions in which there are objects or features of interest with low visual contrast is proposed.
69 citations