A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precision Edge Contrast and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Authenticating Edges Produced by Zero-Crossing Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subpixel Measurements Using a Moment-Based Edge Operator
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Designs for Efficient Multiresolution Edge Detection and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale and the differential structure of images
Image and Vision Computing - Special issue: information processing in medical imaging 1991
On the Edge Location Error for Local Maximum and Zero-Crossing Edge Detectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Ridge Seeking Operators for Multimodality Medical Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparison of edge detectors: a methodology and initial study
Computer Vision and Image Understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Using Angular Dispersion of Gradient Direction for Detecting Edge Ribbons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
Edge detection in untextured and textured images-a common computational framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Local orientation analysis in images by means of the Hermite transform
IEEE Transactions on Image Processing
Edge Detection with Embedded Confidence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint classification based on extraction and analysis of singularities and pseudoridges
VIP '01 Proceedings of the Pan-Sydney area workshop on Visual information processing - Volume 11
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Efficient, recursively implemented differential operator, with application to edge detection
Pattern Recognition Letters
Extracting image orientation feature by using integration operator
Pattern Recognition
Recognition of circular patterns on GPUs: Performance analysis and contributions
Journal of Parallel and Distributed Computing
Fingerprint singular point detection algorithm by Poincaré index
WSEAS TRANSACTIONS on SYSTEMS
Performance evaluation of corner detectors using consistency and accuracy measures
Computer Vision and Image Understanding
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
Re-illuminating single images using Albedo estimation
Pattern Recognition
Edge detection in ventriculograms using support vector machine classifiers and deformable models
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
A simple and efficient approach to barcode localization
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Precise estimation of painting surfaces for digital archiving
CCIW'13 Proceedings of the 4th international conference on Computational Color Imaging
Corisco: Robust edgel-based orientation estimation for generic camera models
Image and Vision Computing
Hi-index | 0.14 |
Fine, accurate gradient information is required in many image-processing algorithms and systems including differential geometric methods, orientation analysis, and integrated vision sensors. In this paper, wepropose optimal gradient operators based on a newly derived consistency criterion. This criterion is based on an orthogonal decomposition of the difference between a continuous gradient and discrete gradients into the intrinsic smoothing effect and the self-inconsistency involved in the operator. We show that consistency assures the exactness of gradient direction of a locally one-dimensional (1D) pattern in spite of its orientation, spectral composition, and subpixel translation. Stressing that inconsistency reduction is of primary importance, we derive an iterative algorithm which leads to accurate gradient operators of arbitrary size. We compute the optimum $3\times 3$, $4\times 4$, and $5\times 5$ operators, compare them with conventional operators and examine the performance for one synthetic and several real images. The results indicate that the proposed operators are superior with respect to accuracy, bandwidth, and isotropy.