Fast Hough transform: A hierarchical approach
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hough transform algorithms for mesh-connected SIMD parallel processors
Computer Vision, Graphics, and Image Processing
A probabilistic Hough transform
Pattern Recognition
A probabilistic algorithm for computing Hough transforms
Journal of Algorithms
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A linear algorithm for incremental digital display of circular arcs
Communications of the ACM
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
IMPSAC: Synthesis of Importance Sampling and Random Sample Consensus
IEEE Transactions on Pattern Analysis and Machine Intelligence
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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We propose a robust and efficient algorithm for curve tracking in a sequence of binary images. First it verifies the presence of a curve by votes, whose values indicate the number of the points on the curve, thus being able to robustly detect curves against outlier and occlusion. Furthermore, we introduce a procedure for preventing redundant verification by determining equivalence curves in the digital space to reduce the time complexity. Second it propagates the distribution which represents the presence of the curve to the successive image of a given sequence. This temporal propagation enables to focus on the potential region where the curves detected at time t – 1 are likely to appear at time t. As a result, the time complexity does not depend on the dimension of the curve to be detected. To evaluate the performance, we use three noisy image sequences, consisting of 90 frames with 320 × 240 pixels. The results shows that the algorithm successfully tracks the target even in noisy or cluttered binary images.