IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition and localization via pose clustering
Computer Vision, Graphics, and Image Processing
Object recognition by computer: the role of geometric constraints
Object recognition by computer: the role of geometric constraints
GENITOR II.: a distributed genetic algorithm
Journal of Experimental & Theoretical Artificial Intelligence
Recognizing solid objects by alignment with an image
International Journal of Computer Vision
Geometric invariance in computer vision
Geometric invariance in computer vision
Local search algorithms for geometric object recognition: optimal correspondence and pose
Local search algorithms for geometric object recognition: optimal correspondence and pose
How Easy is Matching 2D Line Models Using Local Search?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
A Generalized Hough-like Transformation for Shape Recognition
A Generalized Hough-like Transformation for Shape Recognition
Comparing random starts local search with key feature matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
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A Messy Genetic Algorithm is customized to find optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which chromosome length can vary. Consequently, population dynamics can be made to drive a relatively efficient and robust search for larger and better matches. Run-times for the Messy GA are as much as an order of magnitude smaller than for random starts local search. When compared to a faster Key-Feature Algorithm, the Messy Genetic Algorithm more reliably finds optimal matches. Empirical results are presented for both controlled synthetic and real world line matching problems.