An Efficient Color Edge Detection Using the Mahalanobis Distance

Abstract

Abstract-The performance of edge detection often relies on its ability to correctly determine the dissimilarities of connected pixels. For grayscale images, the dissimilarity of two pixels is estimated by a scalar difference of their intensities and for color images, this is done by using the vector difference (color distance) of the three-color components. The Euclidean distance in the RGB color space typically measures a color distance. However, the RGB space is not suitable for edge detection since its color components do not coincide with the information human perception uses to separate objects from backgrounds. In this paper, we propose a novel method for color edge detection by taking advantage of the HSV color space and the Mahalanobis distance. The HSV space models colors in a manner similar to human perception. The Mahalanobis distance independently considers the hue, saturation, and lightness and gives them different degrees of contribution for the measurement of color distances. Therefore, our method is robust against the change of lightness as compared to previous approaches. Furthermore, we will introduce a noise-resistant technique for determining image gradients. Various experiments on simulated and real-world images show that our approach outperforms several existing methods, especially when the images vary in lightness or are corrupted by noise.

Perception Oriented Haze Image Definition Restoration by Basing on Physical Optics Model

“…The convolution model of minimum mean square error is transformed to frequency domain expression in traditional wiener filter. As shown in (24).…”

“…He [17], [18] chooses the top 1 percent of the maximal pixel value in the whole picture as the radiance of atmospheric light A, which leads to a low precision. In this paper, canny edge detection algorithm [24] is employed for edge detection to locate approximate area of the radiance of atmospheric light A. the maximal value of atmospheric light can be identified by binary-tree method. Hence, the radiance of atmospheric light A is obtained precisely.…”

“…In this paper, the edge in haze image F(x, y) is detected by the canny edge detection algorithm [24]. In regard to fog images which contain the sky area, it could be discovered that the color of the scene is comparatively vivid, whereas the sky part are greyish white and homogeneous.…”

“…Hence, the position of sky area s 1 can be roughly judged according to the characteristic of high pixel intensity of atmospheric light. Canny edge detection algorithm [24] takes Gaussian function as smooth filter to process the haze image in the first place, that is, to remove noises in the image with Gaussian operator [25] by image convolution. 16)…”

Compass radius estimation for improved image classification using Edge-SIFT

“…Although compass operator has been widely used [19][20] [21] [22], the parameter to obtain the edge images always remains fixed. In the experiments carried out by Xie et al the value of the compass operator radius is fixed as proposed in literature [17] for all images.…”