A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of edge detectors: a methodology and initial study
Computer Vision and Image Understanding
Object Tracking Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Formulation for Hausdorff Matching
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
Two-dimensional object alignment based on the robust oriented Hausdorff similarity measure
IEEE Transactions on Image Processing
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
Texture classification using Gabor wavelets based rotation invariant features
Pattern Recognition Letters
Image copy detection using a robust gabor texture descriptor
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
Computers and Electronics in Agriculture
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This paper describes a new and efficient circular Gabor filter-based method for object matching by using a version of weighted modified Hausdorff distance. An improved Gabor odd filter-based edge detector is performed to get edge maps. A rotation invariant circular Gabor-based filter, which is different from conventional Gabor filter, is used to extract rotation invariant features. The Hausdorff distance (HD) has been shown an effective measure for determining the degree of resemblance between binary images. A version of weighted modified Hausdorff distance (WMHD) in the circular Gabor feature space is introduced to determine which position can be possible object model location, which we call 'coarse' location, and at the same time we get correspondence pairs of edge pixels for both object model and input test image. Then we introduce the geometric shape information derived from the above correspondence pairs of edge pixels to find the 'fine' location. The experimental results given in this paper show the proposed algorithm is robust to rotation, scale, occlusion, and noise etc.