A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precision Edge Contrast and Orientation Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing
An Edge Detection Technique Using the Facet Model and Parameterized Relaxation Labeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relaxation Methods for Supervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Foundations of Probabilistic Relaxationwith Product Support
Journal of Mathematical Imaging and Vision
A Game-Theoretic Approach to Integration of Modules
IEEE Transactions on Pattern Analysis and Machine Intelligence
General Geometric Good Continuation: From Taylor to Laplace via Level Sets
International Journal of Computer Vision
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The problem of reinforcing local evidence of linear structure while suppressing unwanted information in noisy images is considered, using a modified form of relaxation labeling. The methodology is based on parametrizing a continuous set of orientation labels via a single vector and using a sigmoidal thresholding function to bias neighborhood influence and ensure convergence to a meaningful stable state. Label strength and label/no-label decisions are incorporated into a single functional. Optimal points of the functional represent the cases where as many pixels (objects) as possible have achieved the desirable linear-structure-reinforced and noise-suppressed labelings. Three different linear structure reinforcement tasks are considered within the general framework: edge reinforcement, edge reinforcement with thinning, and bar (line segment) reinforcement. Results from several image data sets are presented. This approach can directly handle continuous feature information from low-level image analysis operators, and the computational complexity of labeling is reduced.