A Shape Detection Framework for Deformation Objects Using Clustering Algorithms

This paper uses clustering algorithms to introduce a shape framework for deformable objects. Until now, the shape detection of the deformable objects has faced several challenges: 1) unable to form a unified framework for multiple shapes; 2) the calculation burden as a large number of calculations; 3) the inability to solve the 3D point-cloud case. A novel shape detection framework for deformable objects is presented in this paper, which only uses the input 2D-pixel data of the objects without any artificial markers. The proposed detection approach runs in a highly real-time manner. For the definitions of the shapes of the deformable objects, three shape configurations are used to describe the outlines of the objects, i.e., centerline, contour, and surface. In addition, for the obtaining of the 3D shape, Different from the traditional 3D point cloud processing method, this article uses a one-to-one mapping method between 2D-pixel points and 3D shape points. Therefore, this guarantees a one-to-one correspondence between 2D and 3D shape points. Hence, the proposed approach can enhance the autonomous capability to detect the shape of deformable objects. Detailed experimental results are conducted within the centerline configuration to evaluate the effectiveness of the proposed shape detection framework.