Abstract
Image segmentation is a crucial topic in image analysis and understanding, and the foundation of target detection and recognition. Image segmentation, essentially, can be considered as classifying the image according to the consistency of the region and the inconsistency between regions, it is widely used in medical and criminal investigation, cultural relic identification, monitoring and so forth. There are two outstanding common problems in the existing segmentation algorithm, one is the lack of accuracy, and the other is that it is not widely applicable. The main contribution of this paper is to present a novel segmentation method based on the information entropy theory and multi-scale transform contour constraint. Firstly, the target contour is initially obtained by means of a multi-scale sample top-hat and bottom-hat transform and an improved watershed method. Subsequently, in terms of this initial contour, the interesting areas can be finely segmented out with an innovative 3D flow entropy method. Finally, the sufficient synthetic and real experiments proved that the proposed algorithm can greatly improve the segmentation effect. In addition, it is widely applicable.
Highlights
Image segmentation is an important technology of image analysis, and its results often determine the effect of image recognition
The maximum entropy method is a kind of statistic-based method that employs maximum information entropy to extract a target edge, and because of the pixel confusion in the noisy concentrated region, this method is likely to mistakenly identify the noise area as the target profile, which leads to the worse accuracy than Otsu
This paper proposes a method of vector cosine distance to check the over segmentation and combines the over segmentation regions
Summary
Image segmentation is an important technology of image analysis, and its results often determine the effect of image recognition. Li et al [7] proposed an improved two-dimensional histogram partitioning threshold segmentation method, which uses two straight lines by threshold vector point and gray level shafts, respectively into the α and β angle to partition a two-dimensional histogram In this way, the threshold segmentation can be used on a wider range of applications, but for low contrast images, the segmentation effect of this method is not ideal. The method uses top-hat and bottom-hat transform with multi-scale structure and the morphological segmentation method by adding the weighted vector cosine distance check to the select threshold in the initial region of interest. The experimental results show that the proposed algorithm can solve the ubiquitous problem in the current information entropy edge detection and energy edge detection, which is inaccurate segmentation of the depressed area, low contrast region and noise region.
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