Computation of Surface Orientation and Structure of Objects Using Grid Coding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
3-D structures from 2-D images
Advances in Machine Vision
Computational Approaches to Image Understanding
ACM Computing Surveys (CSUR)
Vehicle Segmentation and Classification Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Adaptive-Focus Deformable Model Using Statistical and Geometric Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and Nonrigid Motion Estimation Through Physics-Based Synthesis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elastically Adaptive Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locating object contours in complex background using improved snakes
Computer Vision and Image Understanding
Advances in Engineering Software
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The authors introduce a technique for 3D surface reconstruction using elastic deformable-models. The model used is an imaginary elastic grid, which is made of membranous, thin-plate-type material. The elastic grid can bent, twisted, compressed, and stretched into any desired 3D shape, which is specified by the shape constraints derived automatically from images of a real 3D object. Shape reconstruction is guided by a set of imaginary springs that enforce the consistency in the position, orientation, and/or curvature measurements of the elastic grid and the desired shape. The dynamics of a surface reconstruction process is regulated by Hamilton's principle or the principle of the least action. Furthermore, a 1D deformable template that borders the elastic grid may be used. This companion boundary template is attracted/repelled by image forces to conform with the silhouette of the imaged object. Implementation results using simple analytic shapes and images of real objects are presented.