Abstract
Spatial correlation information between pixels is considered to be very important in thresholding methods. However, it is often ignored and thus unsatisfied segmentation results maybe obtained. To overcome this shortcoming, we propose a new image segmentation approach by taking not only pixels’ spatial information but also pixels’s gray level into account. First, a non-local mean filter is imposed on the image. Then the filtered image and the original image together are adopted to build a two dimensional histogram, it is called non-local mean two dimensional histogram. Finally, a minimum relative entropy criteria is used to select the ideal thresholding vector. Since the non-local mean filter process is performed in a neighborhood of current pixel, it carries out the spatial information of current pixel. Segmentation results on several images illustrate the effectiveness of the proposed thresholding method, whose segmentation accuracy are greatly improved compared to most existing thresholding methods.
Highlights
In the area of computer vision, image segmentation is a primary pre-processing step
The proposed method is compared with other methods including Otsu method, Kapur method, Minimum cross entropy (MCE) method, 2D histogram-based minimum cross entropy
The misclassification error (ME) is adopted as objective criteria to evaluate the performance of the referenced methods
Summary
In the area of computer vision, image segmentation is a primary pre-processing step. The primary goal of image segmentation is to partition the image into several regions. Image segmentation techniques had widely been adopted by different practical application task such as cell segmentation [1], object detection in SAR image [2], defect detection [3,4], etc. To deal with image segmentation, many approaches and strategies had been developed. Turbopixel/superpixel segmentation methods [5,6], watershed segmentation methods [7,8], active contour models [9,10], clustering based methods [11,12], deep learning-based methods [13,14], thresholding methods [15,16],and so on.
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