Abstract
Image segmentation is an important preprocessing operation in image recognition and computer vision. This paper proposes an adaptive K-means image segmentation method, which generates accurate segmentation results with simple operation and avoids the interactive input of K value. This method transforms the color space of images into LAB color space firstly. And the value of luminance components is set to a particular value, in order to reduce the effect of light on image segmentation. Then, the equivalent relation between K values and the number of connected domains after setting threshold is used to segment the image adaptively. After morphological processing, maximum connected domain extraction and matching with the original image, the final segmentation results are obtained. Experiments proof that the method proposed in this paper is not only simple but also accurate and effective.
Highlights
Image segmentation refers to the decomposition of an image into a number of non-overlapping meaningful areas with the same attributes
Image segmentation is a key technology in digital image processing, and the accuracy of segmentation directly affects the effectiveness of the follow-up tasks
Clustering analysis algorithm divides the data sets into different groups according to a certain standard, so it has a wide application in the field of image segmentation
Summary
Image segmentation refers to the decomposition of an image into a number of non-overlapping meaningful areas with the same attributes. Image segmentation is a key technology in digital image processing, and the accuracy of segmentation directly affects the effectiveness of the follow-up tasks. Considering its complexity and difficulty, the existing segmentation algorithm has achieved certain success to varying degrees, but the research on this aspect still faces many challenges. Clustering analysis algorithm divides the data sets into different groups according to a certain standard, so it has a wide application in the field of image segmentation. Image segmentation as one of the key technology of digital image processing, combined with relevant professional knowledge, is widely used in machine vision, face recognition, fingerprint recognition, traffic control systems, satellite image positioning objects (roads, forests, etc.), pedestrian detection, medical imaging, and many other fields, and it is worthy of in-depth study to explore. The luminance component l in LAB color space is fixed to filter the influence
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