Research On Occlusion Boundary Detection Approach Of Depth Image Using Unsupervised Thought

Occlusion boundary detection is a major problem to be solved in the field of computer vision.Because of its universality,it has gradually become the research focus for many scientists.With the increasing application of depth images in the field of vision,the problem of occlusion in depth images has attracted the attention of many scholars.Based on the analysis of occlusion and its characteristics in the depth image,this paper treats the problem of occlusion boundary detection as the classification of all the pixels in the depth image with the popular machine learning algorithm.The specific research contents are as follows:Firstly,according to the related knowledge of depth image,the causes of occlusion happened and the definition of occlusion boundaries are expounded,and then the related knowledge of machine learning theory involved in this method is introduced briefly.Secondly,a method to detect the occlusion boundary in a depth image using spectral clustering is proposed.First,a new occlusion-related feature called effective standard deviation feature is defined.Second,some pixels are extracted by using mean chi-square set distance and the similarity matrix is constructed based on the occlusion-related feature.At the end,the laplacian matrix and the decomposed eigenvectors of all the pixels are approximated by Nystrom approximation method based on the similarity matrix.And then,the decomposed eigenvectors are clustered to divide into two categories named occlusion boundary points and non-occlusion boundary points.Thirdly,a method of detecting the occlusion boundary in a depth image using principal component analysis is proposed.First,the laplacian operator is used to extract the boundary points of the visual target in the depth image.Second,the standard Euclidean distance and cosine similarity are respectively used to calculate similarities between the boundary points,and the two similarities are normalized and merged.Third,the similarity matrix is linearly reduced to obtain new occlusion-related features in the low-dimensional feature space.At the end,using the unsupervised thought,the new occlusion-related feature is combined with the existing features to build feature matrix which is clustered toobtain the occlusion boundary of the visual target in the depth image.Finally,the effectiveness and feasibility of the proposed method are verified by experiments,and the experimental results are compared with the existing methods.