Scale and the differential structure of images
Image and Vision Computing - Special issue: information processing in medical imaging 1991
The Gaussian scale-space paradigm and the multiscale local jet
International Journal of Computer Vision
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Classifier Conditional Posterior Probabilities
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Contour-Based Learning for Object Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Maximum Membership Scale Selection
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Scale invariant texture analysis using multi-scale local autocorrelation features
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Scale selection for supervised image segmentation
Image and Vision Computing
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The scale-invariant detection of image structure has been a topic of study within computer vision and image analysis since long. To date, Lindeberg's scale selection method has probably been the most fruitful and successful approach to this problem. It provides a general technique to cope with the detection of structures over scale that can be successfully expressed in terms of Gaussian differential operators. Any detection or segmentation task would potentially benefit from a similar approach to deal with scale. For many of the real-world image structures of interest, however, it will often be impossible to explicitly design or handcraft an operator that is capable of detecting them in a sensitive and specific way. In this paper, we present an approach to the scale-selection problem in which the construction of the detector is driven by supervised learning techniques. The resulting classification method is designed so as to achieve scale-invariance and may be thought of as a supervised version of Lindeberg's classical scheme.