3D OBJECTS SEPARATION: UNSUPERVISED SEGMENTATION AND CLASSIFICATION
David Tal, Jacob Fish · International Journal for Multiscale Computational Engineering · 2019
Object segmentation for the purpose of object and pattern recognition has been a long-standing subject of interest in the field of machine vision. Despite the significant attention given to the development of segmentation and recognition methods, the critical challenge of separating merged objects did not share the spotlight. In the work presented in this paper, we propose a simple yet novel approach to overcome this hurdle, by developing a methodology for unsupervised classification and separation of objects in 3D. Lower dimensionality classifiers are joined to provide a powerful higher dimensionality classification. The robustness of this approach is illustrated through its implementation on two case studies of merged objects. Applications of this methodology can further extend from structural classification to general problems of clustering and classification in various fields.