Segmentation of Hybrid Motions via Hybrid Quadratic Surface Analysis

In this paper, we investigate the mathematical problem underlying segmentation of hybrid motions: Given a series of tracked feature correspondences between two (perspective) images, we seek to segment and estimate multiple motions, possibly of different types (e.g., affine, epipolar, and homography). In order to accomplish this task, we cast the problem into a more general mathematical framework of segmenting data samples drawn from a mixture of linear subspaces and quadratic surfaces. The result is a novel algorithm called Hybrid Quadratic Surface Analysis (HQSA). HQSA uses both the derivatives and Hessians of fitting polynomials for the data to separate linear data samples from quadratic data samples. These derivatives and Hessians also lead to important necessary conditions, based on the so-called mutual contraction subspace, to separate data samples on different quadratic surfaces. The algebraic solution we derive is non-iterative and numerically stable. It tolerates moderate nois...

• Type: Conference
• Year: 2005
• Where: ICCV
• Authors: Shankar Rao, Allen Y. Yang, Andrew Wagner, Yi Ma
• Keywords: Computer Vision, Hybrid Motion Segmentation, Hybrid Quadratic Surface, ICCV 2005, Linear Data Samples, Quadratic Data Samples, Separate Data Samples