An Efficient Parameterless Quadrilateral-Based Image Segmentation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Tracking by Classification in a Tiny Discrete Pose Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special section: Best papers from the 2007 biometrics: Theory, applications, and systems (BTAS 07) conference
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In this paper, we consider the problem of matching 2D planar object curves from a database, and tracking moving object curves through an image sequence. The first part of the paper describes a curve data compression method using B-spline curve approximation. We present a new constrained active B-spline curve model based on the minimum mean square error (MMSE) criterion, and an iterative algorithm for selecting the “best” segment border points for each B-spline curve. The second part of the paper describes a method for simultaneous object tracking and affine parameter estimation using the approximate curves and profiles. We propose a novel B-spline point assignment algorithm which incorporates the significant corners for interpolating corresponding points on the two curves to be compared. A gradient-based algorithm is presented for simultaneously tracking object curves, and estimating the associated translation, rotation and scaling parameters. The performance of each proposed method is evaluated using still images and image sequences containing simple objects