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
Inference of Surfaces, 3D Curves, and Junctions from Sparse, Noisy, 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Dynamics of Nonlinear Relaxation Labeling Processes
Journal of Mathematical Imaging and Vision
A neural model of contour integration in the primary visual cortex
Neural Computation
A Comparison of Measures for Detecting Natural Shapes in Cluttered Backgrounds
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
IEEE Transactions on Pattern Analysis and Machine Intelligence
N-Dimensional Tensor Voting and Application to Epipolar Geometry Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Framework for Segmentation and Grouping
Computational Framework for Segmentation and Grouping
Curvature-Augmented Tensor Voting for Shape Inference from Noisy 3D Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recurrent long-range interactions in early vision
Emergent neural computational architectures based on neuroscience
Layered 4D Representation and Voting for Grouping from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
First Order Augmentation to Tensor Voting for Boundary Inference and Multiscale Analysis in 3D
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural mechanisms for the robust representation of junctions
Neural Computation
Robust Estimation of Adaptive Tensors of Curvature by Tensor Voting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse approximation of images inspired from the functional architecture of the primary visual areas
EURASIP Journal on Applied Signal Processing
An Application of Relaxation Labeling to Line and Curve Enhancement
IEEE Transactions on Computers
On the Foundations of Relaxation Labeling Processes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous Relaxation and Local Maxima Selection: Conditions for Equivalence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual grouping based on iterative multi-scale tensor voting
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Computer Vision and Image Understanding
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Tensor voting (TV) methods have been developed in a series of papers by Medioni and coworkers during the last years. The method has been proved efficient for feature extraction and grouping and has been applied successfully in a diversity of applications such as contour and surface inferences, motion analysis, etc. We present here two studies on improvements of the method. The first one consists in iterating the TV process, and the second one integrates curvature information. In contrast to other grouping methods, TV claims the advantage to be non-iterative. Although non-iterative TV methods provide good results in many cases, the algorithm can be iterated to deal with more complex or more ambiguous data configurations. We present experiments that demonstrate that iterations substantially improve the process of feature extraction and help to overcome limitations of the original algorithm. As a further contribution, we propose a curvature improvement for TV. Unlike the curvature-augmented TV proposed by Tang and Medioni, our method evaluates the full curvature, sign and amplitude in the 2D case. Another advantage of the method is that it uses part of the curvature calculation already performed by the classical TV, limiting the computational costs. Curvature-modified voting fields are also proposed. Results show smoother curves, a lower degree of artifacts and a high tolerance against scale variations of the input. The methods are finally tested under noisy conditions showing that the proposed improvements preserve the noise robustness of the TV method.