The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Steerable-scalable kernels for edge detection and junction analysis
Image and Vision Computing - Special issue: 2nd European Conference on Computer Vision
Recognizing corners by fitting parametric models
International Journal of Computer Vision
Junction classification by multiple orientation detection
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Steerable filters and invariance theory
VIP '94 The international conference on volume image processing on Volume image processing
A framework for low level feature extraction
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
A lie group approach to steerable filters
Pattern Recognition Letters
On the Precision in Estimating the Location of Edges and Corners
Journal of Mathematical Imaging and Vision
Steerable wedge filters for local orientation analysis
IEEE Transactions on Image Processing
An improved representation of junctions through asymmetric tensor diffusion
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
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The computational cost of conventional filter methods for junction characterization is very high. This burden can be attenuated by using steerable filters. However, in order to achieve a high orientational selectivity to characterize complex junctions a large number of basis filters is necessary. From this results a yet too high computational effort for steerable filters. In this paper we present a new method for characterizing junctions which keeps the high orientational resolution and is computationally efficient. It is based on applying rotated copies of a wedge averaging filter and estimating the derivative with respect to the polar angle. The new method is compared with the steerable wedge filter method [13] in experiments with real images. We show the superiority of our method as well as its adaptability to scale changes and robustness against nozse.