Segmenting Images on the Tensor Manifold

In this note, we propose a method to perform segmentation on the tensor manifold, that is, the space of positive definite matrices of given dimension. In this work, we explicitly use the Riemannian structure of the tensor space in designing our algorithm. This structure has already been utilized in several approaches based on active contour models which separate the mean and/or variance inside and outside the evolving contour. We generalize these methods by proposing a new technique for performing segmentation by separating the entire probability distributions of the regions inside and outside the contour using the Bhattacharyya metric. In particular, this allows for segmenting objects with multimodal probability distributions (on the space of tensors). We demonstrate the effectiveness of our algorithm by segmenting various textured images using the structure tensor. A level set based scheme is proposed to implement the curve flow evolution equation.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Yogesh Rathi, Allen Tannenbaum, Oleg V. Michailovich
• Keywords: Active Contour Models, Computer Vision, CVPR 2007, Entire Probability Distributions, Multimodal Probability Distributions, Structure Tensor, Tensor Space