Abstract
Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.
Highlights
Image segmentation technology plays a crucial role in the application of image processing, and the effect after segmentation will directly affect the work in the later stage of computer vision. e original segmentation technology is to convert the image into grayscale image and use various algorithms to segment it [1]
E experiment is mainly divided into three parts. e first part is to compare the running time of different calculation methods of circular maximum entropy thresholding under the condition of circular histogram divided into two parts: brute force method, brute force method considering symmetry, and the proposed recursive algorithm
In order to prove that the proposed recursive algorithm is efficient, the running time of the circular Otsu thresholding model in the literature [9] and recursive algorithm of maximum entropy thresholding for circular histogram linearization are tested. en, the segmentation results of various algorithms will be compared in the second part
Summary
Image segmentation technology plays a crucial role in the application of image processing, and the effect after segmentation will directly affect the work in the later stage of computer vision. e original segmentation technology is to convert the image into grayscale image and use various algorithms to segment it [1]. Image segmentation technology plays a crucial role in the application of image processing, and the effect after segmentation will directly affect the work in the later stage of computer vision. E original segmentation technology is to convert the image into grayscale image and use various algorithms to segment it [1]. Erefore, many researchers began to turn their attention to color image segmentation technology [2]. E color image provides more information than the gray image. To segment the color image, researchers should select the appropriate color space and adopt the segmentation algorithm suitable for this space. RGB is only suitable for the display system, but not for image segmentation and analysis. The HSI (i.e., hue saturation intensity) color space model [4] overcomes the defect of the coupling of the two in the universal RGB color model by representing the color attribute and the light intensity, respectively, by the three mutually independent components hue (H), saturation (S), and intensity (I)
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