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: It is found that this CNN chip with a simple 3 × 3 CNN kernel can reliably classify four textures and it is believed that more textures can be separated and adequate texture segmentation (< 1% error) can be achieved.
68 citations
15 Apr 2007
TL;DR: A new feature extraction method, which is robust against rotation and histogram equalization for texture classification, is proposed and the classification accuracy of the proposed method exceeds the ones obtained by other image features.
Abstract: In this paper, we propose a new feature extraction method, which is robust against rotation and histogram equalization for texture classification. To this end, we introduce the concept of advanced local binary patterns (ALBP), which reflects the local dominant structural characteristics of different kinds of textures. In addition, to extract the global spatial distribution feature of the ALBP patterns, we incooperate ALBP with the aura matrix measure as the second layer to analyze texture images. The proposed method has three novel contributions, (a) The proposed ALBP approach captures the most essential local structure characteristics of texture images (i.e. edges, corners); (b) the proposed method extracts global information by using Aura matrix measure based on the spatial distribution information of the dominant patterns produced by ALBP; and (c) the proposed method is robust to rotation and histogram equalization. The proposed approach has been compared with other widely used texture classification techniques and evaluated by applying classification tests to randomly rotated and histogram equalized images in two different texture databases: Brodatz and CUReT. The experimental results show that the classification accuracy of the proposed method exceeds the ones obtained by other image features.
68 citations
TL;DR: The enhanced images, as a result of implementing the proposed approach, are characterized by relatively genuine color, increased contrast and brightness, reduced noise level, and better visibility.
Abstract: Poor visibility due to the effects of light absorption and scattering is challenging for processing underwater images. We propose an approach based on dehazing and color correction algorithms for underwater image enhancement. First, a simple dehazing algorithm is applied to remove the effects of haze in the underwater image. Second, color compensation, histogram equalization, saturation, and intensity stretching are used to improve contrast, brightness, color, and visibility of the underwater image. Furthermore, bilateral filtering is utilized to address the problem of the noise caused by the physical properties of the medium and the histogram equalization algorithm. In order to evaluate the performance of the proposed approach, we compared our results with six existing methods using the subjective technique, objective technique, and color cast tests. The results show that the proposed approach outperforms the six existing methods. The enhanced images, as a result of implementing the proposed approach, are characterized by relatively genuine color, increased contrast and brightness, reduced noise level, and better visibility.
67 citations
TL;DR: Three normalization procedures were evaluated on their ability to remove extraneous error variation, induce homogeneity of intersubject variation, and remove unwanted dependencies, and all worked well at removing the dependency of rCBF on gCBF in count and flow images.
67 citations
TL;DR: The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels which performs better than the other known algorithms in terms of accuracy.
Abstract: This paper focuses on the detection of retinal blood vessels which play a vital role in reducing the proliferative diabetic retinopathy and for preventing the loss of visual capability. The proposed algorithm which takes advantage of the powerful preprocessing techniques such as the contrast enhancement and thresholding offers an automated segmentation procedure for retinal blood vessels. To evaluate the performance of the new algorithm, experiments are conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm performs better than the other known algorithms in terms of accuracy. Furthermore, the proposed algorithm being simple and easy to implement, is best suited for fast processing applications.
67 citations