On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
A perceptual grouping hierarchy for appearance-based 3D object recognition
Computer Vision and Image Understanding - Special issue on perceptual organization in computer vision
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Saliency, Scale and Image Description
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Recognizing Planar Objects Using Invariant Image Features
Recognizing Planar Objects Using Invariant Image Features
About Moment Normalization and Complex Moment Descriptors
Proceedings of the 4th International Conference on Pattern Recognition
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Scale-invariant shape features for recognition of object categories
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Relative scale method to locate an object in cluttered environment
Image and Vision Computing
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The same object seen in two different images can be geometrically and photometrically transformed. In this paper, a method of interest point detection and matching is described for the same object in different images. One of the main considerations is the change in the object scale. In this method, a reference scale is assigned to a particular instance of the object, and the change of scale is represented by a relative scale. Then, Harris' relative scale method is used for interest point detection. This method is robust to linear geometric transformations. A heuristic method for threshold selection is also described for robustness to intensity changes in a cluttered environment with partial occlusions. The repeatability rate of interest points for this method is higher then that for the existing methods. For the matching process, a local invariant descriptor is computed in the relative scale for each of the detected interest points. A hashing technique is applied to find the matches efficiently. The matching method enables finding a good number of correct matches for different types of transformations in a cluttered environment and one with partial occlusions. The proposed single scale detection and matching method could be effectively used for many practical applications, where the relative scale of the object can be predicted in advance.