International Journal of Computer Vision
Hierarchical Image Segmentation—Part I: Detection of Regular Curves in a Vector Graph
International Journal of Computer Vision
Design and Use of Linear Models for Image Motion Analysis
International Journal of Computer Vision
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation based on situational DCT descriptors
Pattern Recognition Letters
Motion Feature Detection Using Steerable Flow Fields
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Feature Extraction of Edge by Directional Computation of Gray-Scale Variation
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Image Manifolds which are Isometric to Euclidean Space
Journal of Mathematical Imaging and Vision
Learning appearance and transparency manifolds of occluded objects in layers
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Exploiting fisher and fukunaga-koontz transforms in chernoff dimensionality reduction
ACM Transactions on Knowledge Discovery from Data (TKDD)
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We propose an algorithm to automatically construct feature detectors for arbitrary parametric features. To obtain a high level of robustness we advocate the use of realistic multi-parameter feature models and incorporate optical and sensing effects. Each feature is represented as a densely sampled parametric manifold in a low dimensional subspace of a Hilbert space. During detection, the brightness distribution around each image pixel is projected into the subspace. If the projection lies sufficiently close to the feature manifold, the feature is detected and the location of the closest manifold point yields the feature parameters. The concepts of parameter reduction by normalization, dimension reduction, pattern rejection, and heuristic search are all employed to achieve the required efficiency. By applying the algorithm to appropriate parametric feature models, detectors have been constructed for five features, namely, step edge, roof edge, line, corner, and circular disc. Detailed experiments are reported on the robustness of detection and the accuracy of parameter estimation.