Graphical Models and Image Processing
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Randomized or probabilistic Hough transform: unified performance evaluation
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Pattern Recognition Letters
On the Convergence of a Population-Based Global Optimization Algorithm
Journal of Global Optimization
Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
Circle detection on images using genetic algorithms
Pattern Recognition Letters
Detection of linear and circular shapes in image analysis
Computational Statistics & Data Analysis
The randomized-Hough-transform-based method for great-circle detection on sphere
Pattern Recognition Letters
Support Vector Machines for TCP traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Pattern Recognition
Shape detection from line drawings with local neighborhood structure
Pattern Recognition
An optimization on pictogram identification for the road-sign recognition task using SVMs
Computer Vision and Image Understanding
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
Real-time traffic sign recognition in three stages
Robotics and Autonomous Systems
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In this paper, a general framework is presented for detection of arbitrary triangles, regular polygons, and circles, which is inspired by the common geometric property that the incenter of the shape is equidistant to the tangential lines of the contour points. The idea of point-lines distance distribution (PLDD) is introduced to compute the shape energy of each pixel. Then, shape centers can be exacted from the produced PLDD map, and shape radii are obtained simultaneously based on the distance distribution of the shape center. The shape candidates are thus determined and represented with three independent characteristics: shape center, shape radius, and contour points. Finally, distinguish different types of the shape from shape candidates using shape contour points information. Compared with exiting methods, the PLDD based method detects the shapes mainly using the inherent information of edge points, such as distance, and it is simple and general. Comparative experiments both on synthetic and natural images with the state of the art also prove that the PLDD based method performs more robustly and accurately with the maximal time complexity O(n^2) at the worst condition.