A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection and motion detection
Image and Vision Computing
Fundamentals of digital image processing
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Digital image processing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal infinite impulse response zero crossing based edge detectors
CVGIP: Image Understanding
On Optimal Infinite Impulse Response Edge Detection Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal template matching by nonorthogonal image expansion using restoration
Machine Vision and Applications
Optimal Ramp Edge Detection Using Expansion Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance Assessment Through Bootstrap
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Discrete Expression of Canny's Criteria for Step Edge Detector Performances Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Deformable Contour Based Algorithm for Segmentation of the Hippocampus from MRI
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Optimal DSNR detector for ramp edges
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Salient Closed Boundary Extraction with Ratio Contour
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Operator-Based Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating edge detection through boundary detection
EURASIP Journal on Applied Signal Processing
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Computer Methods and Programs in Biomedicine
Edge Drawing: A combined real-time edge and segment detector
Journal of Visual Communication and Image Representation
Edge Detection Filter based on Mumford-Shah Green Function
SIAM Journal on Imaging Sciences
Computer Methods and Programs in Biomedicine
Two-dimensional multi-pixel anisotropic Gaussian filter for edge-line segment (ELS) detection
Image and Vision Computing
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Discusses the application of a newly developed expansion matching method for edge detection. Expansion matching optimizes a novel matching criterion called the discriminative signal-to-noise ratio (DSNR) and has been shown to robustly recognize templates under conditions of noise, severe occlusion and superposition. The DSNR criterion is better suited to evaluate matching in practical conditions than the traditional SNR since it considers as "noise" even the off-center response of the filter to the template itself. We introduce a family of optimal DSNR edge detectors based on the expansion filter for several edge models. For step edges, the optimal DSNR step expansion filter (SEF) is compared with the widely used Canny edge detector (CED). Experimental comparisons show that our edge detector yields better performance than the CED in terms of DSNR even under very adverse noise conditions. As for boundary detection, the SEF consistently yields higher figures of merit than the CED on a synthetic binary image over a wide range of noise levels. Results also show that the design parameters of size or width of the SEF are less critical than the CED variance. This means that a single scale of the SEF spans a larger range of input noise than a single scale of the CED. Experiments on a noisy image reveal that the SEF yields less noisy edge elements and preserves structural details more accurately. On the other hand, the CED output has better suppression of multiple responses than the corresponding SEF output.