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Spatial and temporal grouping in the interpretation of image motion
Abstract
Interpretation of the three-dimensional (3D) world from two-dimensional (2D) images is a primary goal of vision. Image motion is an important source of 3D information. The 3D motion (relative to a camera), and the 3D structure of the environment manifest themselves as image motion. The primary focus of this thesis is the reliable derivation of scene structure through an interpretation of motion in monocular images over extended time intervals. An approach is presented for the integration of spatial models of the 3D world with temporal models of 3D motion in order to robustly derive 3D structure and perform tracking and segmentation. Two specific problems are addressed to illustrate this approach. First, a model of a class of 3D objects is combined with smooth 3D motion to track, identify and reconstruct shallow structures in the scene. Second, a specific model of 3D motion along with general spatial constraints is employed for 3D reconstruction of motion trajectories. Both parts rely fundamentally upon the quantitative modeling of common fate of a structure in motion. In many man-made environments, obstacles in the path of a mobile robot can be characterized as shallow, i.e. they have relatively small extent in depth compared to the distance from the camera. Shallowness can be quantified as affine describability. This is embedded in a tracking system to discriminate between shallow and non-shallow structures based on their affine trackability. The temporal evolution of a structure, derived using affine constraints, is used for verifying its identity as a shallow structure and for its 3D reconstruction. Spatio-temporal analysis and integration is further demonstrated through a two-stage technique for the reconstruction of 3D structure and motion of a scene undergoing a rotational motion with respect to the camera. First, the spatio-temporal grouping of discrete point correspondences as a set of conic trajectories in the image plane is obtained, by exploiting their common motion. This leads to a description that is reliable when compared to the independent fitting of trajectories. The second stage uses a new closed-form solution for computing the 3D trajectories from the computed image trajectories under perspective projection.
Subject Area
Computer science|Electrical engineering
Recommended Citation
Sawhney, Harpreet S, "Spatial and temporal grouping in the interpretation of image motion" (1992). Doctoral Dissertations Available from Proquest. AAI9219492.
https://scholarworks.umass.edu/dissertations/AAI9219492