A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation of local geometry in the visual system
Biological Cybernetics
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Edge Location Error for Local Maximum and Zero-Crossing Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
High-Order Differentiation Filters that Work
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logical/Linear Operators for Image Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Design of a low-pass filter by multi-scale even order Gaussian derivatives
Signal Processing - Special section: Multimodal human-computer interfaces
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We are proposing a biologically inspired multi-scale derivative filter in which the higher order derivatives are expressed as a linear combination of a smoothing function at various scales. One of the functions in the summation has been approximated to a Dirac-delta function to finally yield the new filter. This modification has some support from the point of view of authentic edge detection as well as from neurophysiological and psychophysical experiments at the retinal level. Besides, it improves the quality of the filter in a number of ways. The proposed filter can be optimized at any desired scale. Hence it is very effective in extracting the features from a noisy picture. The filter is rotationally symmetric. Zero-crossing map of any picture filtered with the proposed model gives a half-toning effect to the retrieved image and hence preserves the intensity information in the image even in the edge map.