基于自适应SLIC的人体标准姿势图像分割

为了提高在复杂背景下人体图像分割的精度，提出了一种新的人体图像分割算法.该算法针对简单线性迭代算法（SLIC）在进行超像素块分割时需指定像素块个数的问题，借鉴CV能量模型，通过将图片极小化为多个区域进行水平集迭代分割，从而构造出自适应的超像素块，使得分割后的每个超像素块更贴合图像中的单个色块.然后结合人体平均模板，在图片上标记出感兴趣的人体标准姿势区域，提高了算法对复杂背景的抗干扰能力.最后利用k-means聚类算法将每个超像素块作为节点进行聚类，实现标准人体图像分割.在不同环境下采集多组图片进行实验，结果表明：该算法在保证了图像分割效率的情况下，提高了人体标准姿势的分割精度，对色度丰富的复杂背景抗干扰能力强.

Human Body Standard Pose Image Segmentation Based on Adaptive SLIC

In order to improve the accuracy of human body image segmentation under complex background, a new human body image segmentation algorithm is proposed. This algorithm solves the problem of specifying the number of pixel blocks in the super-pixel block segmentation for the simple linear iterative algorithm (SLIC). By referring to the CV energy model, it is constructed by minimizing the image into multiple regions for horizontal set iterative segmentation. The adaptive super-pixel block is made such that each super-pixel block after the segmentation fits a single color block in the image. Then combined with the human body average template, the human body standard posture area of interest is marked on the picture, which improves the anti-interference ability of the algorithm against the complex background. Finally, each super-pixel block is clustered as a node by k-means clustering algorithm to realize standard human body image segmentation. The experiment is carried out by collecting multiple sets of pictures in different environments. The results show that the proposed algorithm improves the segmentation accuracy of the human body''s standard posture and ensures strong anti-interference ability for complex backgrounds with rich chroma.