A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Determination of Filter Scales for Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo vision for planetary rovers: stochastic modeling to near real-time implementation
International Journal of Computer Vision
International Journal of Computer Vision
An adaptive approach to scale selection for line and edge detection
Pattern Recognition Letters
Edges: saliency measures and automatic thresholding
Machine Vision and Applications
Deformable Kernels for Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Adaptive-Scale Filtering and Feature Detection Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
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It is typical in edge detection applications to examine a single scale or to consider some space of scales in the image without knowing which scale is appropriate for each location in the image. However, many images contain a wide variation in the distance to the scene points, and thus objects of the same size can appear at greatly differing scales in the image. We present a method where the scale of the smoothing and edge detection is varied locally according to the distance to the scene point, which we estimate through stereoscopy. The edges that are detected are thus at the same scale in the world, rather than at the same scale in the image. This method has been implemented efficiently by smoothing the image at a discrete set of scales and performing interpolation to estimate the response at the correct scale for each pixel. The application of this technique to an ordnance recognition problem has resulted in a considerable improvement in performance.