Representation of local geometry in the visual system
Biological Cybernetics
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching affine-distorted images
Matching affine-distorted images
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Geometry-Driven Diffusion in Computer Vision
Geometry-Driven Diffusion in Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Object Recognition Using Multidimensional Receptive Field Histograms
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
An Affine Invariant Interest Point Detector
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Object indexing using an iconic sparse distributed memory
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
COSMOS-a representation scheme for free-form surfaces
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Visual feature learning
On multi-scale differential features and their representations for image retrieval and recognition
On multi-scale differential features and their representations for image retrieval and recognition
Journal of Cognitive Neuroscience
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
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The Gaussian kernel has played a central role in multi-scale methods for feature extraction and matching. In this paper, a method for shaping the filter using the local image structure is presented. We propose an optimization formulation that densely estimates the filter's affine parameters by minimizing an objective constructed from differential feature responses and seeks iterative, approximate solutions. A consequence of shaping the filters is affine invariance of the differential feature vector and it is shown that the shaped responses improve recognition performance.