On the optimal detection of curves in noisy pictures
Communications of the ACM
Digital Picture Processing
Edge and Curve Detection for Visual Scene Analysis
IEEE Transactions on Computers
General purpose models: expectations about the unexpected
IJCAI'75 Proceedings of the 4th international joint conference on Artificial intelligence - Volume 1
Finding picture edges through collinearity of feature points
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Iterated tensor voting and curvature improvement
Signal Processing
Correction to "An Application of Relaxation Labeling to Line and Curve Enhancement"
IEEE Transactions on Computers
Curve Segmentation by Relaxation Labeling
IEEE Transactions on Computers
Parametric correspondence and chamfer matching: two new techniques for image matching
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
ALVEN: a study on motion understanding by computer
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 2
Relaxation labelling - the principle of 'least disturbance'
Pattern Recognition Letters
The least-disturbance principle and weak constraints
Pattern Recognition Letters
Relaxation labelling - the principle of 'least disturbance'
Pattern Recognition Letters
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A relaxation process is described and is applied to the detection of smooth lines and curves in noisy, real world images. There are nine labels associated with each image point, eight labels indicating line segments at various orientations and one indicating the no-line case. Attached to each label is a probability. In the relaxation process, interaction takes place among the probabilities at neighboring points. This permits line segments in compatible orientations to strengthen one another, and incompatible segments to weaken one another. Similarly, no-line labels are reinforced by neighboring no-line labels and weakened by appropriately oriented line labels. This process converges, in only a few iterations, to a condition in which points lying on long curves have achieved high line probabilities, while other points have high no-line probabilities, There is some tendency, under this process, for curves to thicken; however, a thinning procedure can be incorporated to counteract this. The process is effective even for curves of low contrast, and even when many curves lie close to one another.