Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two stages of curve detection suggest two styles of visual computation
Neural Computation
An Active Testing Model for Tracking Roads in Satellite Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamental Limits of Bayesian Inference: Order Parameters and Phase Transitions for Road Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Transactions on Mathematical Software (TOMS)
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Perceptual Organization for Artificial Vision Systems
Perceptual Organization for Artificial Vision Systems
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logical/Linear Operators for Image Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invertible Orientation Bundles on 2D Scalar Images
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Computing Stochastic Completion Fields in Linear-Time Using a Resolution Pyramid
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
Toward discrete geometric models for early vision
Toward discrete geometric models for early vision
The curve indicator random field
The curve indicator random field
Correction to "An Application of Relaxation Labeling to Line and Curve Enhancement"
IEEE Transactions on Computers
Volterra Filtering of Noisy Images of Curves
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
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A Markov process model for contour curvature is introduced via a stochastic differential equation. We analyze the distribution of such curves, and show that its mode is the Euler spiral, a curve minimizing changes in curvature. To probabilistically enhance noisy and low contrast curve images (e.g., edge and line operator responses), we combine this curvature process with the curve indicator random field, which is a prior for ideal curve images. In particular, we provide an expression for a nonlinear, minimum mean square error filter that requires the solution of two elliptic partial differential equations. Initial computations are reported, highlighting how the filter is curvature-selective, even when curvature is absent in the input.