Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Deformation Invariant Image Matching
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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What are the primitives of visual perception? The early feature-analysis theory insists on it being a local-to-global process which has acted as the foundation of most computer vision applications for the past 30 years. The early holistic registration theory, however, considers it as a global-to-local process, of which Chen’s theory of topological perceptual organization (TPO) has been strongly supported by psychological and physiological proofs. In this paper, inspired by Chen’s theory, we propose a novel visual organization, termed computational topological perceptual organization (CTPO), which pioneers the early holistic registration in computational vision. Empirical studies on synthetic datasets prove that CTPO is invariant to global transformation such as translation, scaling, rotation and insensitive to topological deformation. We also extend it to other applications by integrating it with local features. Experiments show that our algorithm achieves competitive performance compared with some popular algorithms.