The Gaussian scale-space paradigm and the multiscale local jet
International Journal of Computer Vision
Shock Graphs and Shape Matching
International Journal of Computer Vision
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Combined Top-Down/Bottom-Up Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Rule-Based Interpretation of Aerial Imagery
IEEE Transactions on Pattern Analysis and Machine Intelligence
A boundary-fragment-model for object detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
From a Non-Local Ambrosio-Tortorelli Phase Field to a Randomized Part Hierarchy Tree
Journal of Mathematical Imaging and Vision
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Early vision is dominated by image patches or features derived from them; high-level vision is dominated by shape representation and recognition. However there is almost no work between these two levels, which creates a problem when trying to recognize complex categories such as "airports" for which natural feature clusters are ineffective. We argue that an intermediate-level representation is necessary and that it should incorporate certain high-level notions of distance and geometric arrangement into a form derivable from images. We propose an algorithm based on a reaction-diffusion equation that meets these criteria; we prove that it reveals (global) aspects of the distance map locally; and illustrate its performance on airport and other imagery, including visual illusions.