International Journal of Computer Vision
Artificial Intelligence - Special volume on computer vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision and Image Understanding
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Moment invariants for recognition under changing viewpoint and illumination
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
Rover visual obstacle avoidance
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Approximation-based keypoints in colour images: a tool for building and searching visual databases
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
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Partially occluded objects are typically detected using local features (also known as interest points, keypoints, etc.). The major problem of the local-feature approach is the scale-invariance. If the objects have to be detected in arbitrary scales, either computationally complex methods of scale-space (multi-scale approach) are used, or the actual scale is estimated using additional mechanisms. The paper proposes a new type of local features (keypoints) that can be used for scale-invariant detection of known objects in analyzed images. Keypoints are defined as locations at which selected moment-based parameters are consistent over a wide radius of circular patches around the keypoint. Although the database of known objects is built using the multi-scale approach, analyzed images are processed using only a single-scale. The paper focuses on the keypoint building and matching only. Higher-level issues of hypotheses building and verification (regarding the presence of known objects) are only briefly mentioned.