Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast classification of discrete shape contours
Pattern Recognition
A unified distance transform algorithm and architecture
Machine Vision and Applications
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Efficiently Locating Objects Using the Hausdorff Distance
International Journal of Computer Vision
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolution analysis for Gradient Direction Matching of object model edges to overhead images
Computer Vision and Image Understanding
A fast algorithm for template matching
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
| Hi-index | 0.14 |
A new search method over (x,y,/spl theta/), called position-orientation masking is introduced. It is applied to vertices that are allowed to be separated into different bands of acuteness. Position-orientation masking yields exactly one /spl theta/ value for each (x,y) that it considers to be the location of a possible occurrence of an object. Detailed matching of edge segments is performed at only these candidate (x,y,/spl theta/) to determine if objects actually do occur there. Template matching is accelerated dramatically since the candidates comprise only a small fraction of all (x,y,/spl theta/). Position-orientation masking eliminates the need for exhaustive search when deriving the candidate (x,y,/spl theta/). Search is guided by correlations between template vertices and distance transforms of image vertices. When a poor correlation is encountered at a particular position and orientation, nearby positions at that orientation and nearby orientations at that position are masked out. Position and orientation traversal are by quadrant and binary decomposition.