Robust regression and outlier detection
Robust regression and outlier detection
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A probabilistic Hough transform
Pattern Recognition
CVGIP: Image Understanding
Procedural elements for computer graphics (2nd ed.)
Procedural elements for computer graphics (2nd ed.)
An efficient randomized algorithm for detecting circles
Computer Vision and Image Understanding
A new randomized algorithm for detecting lines
Real-Time Imaging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm for computer control of a digital plotter
IBM Systems Journal
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In this paper, we describe image feature as parameterized model and formulate feature detection as robust model fitting problem. It can detect global feature easily without parameter transformation, which is needed by Hough Transform methods. We adopt RANSAC paradigm to solve the problem. It is immune to outliers and can deal with image contains multiple features and noisy pixels. In the voting stage of RANSAC, in contrast with previous methods which need distance computation and comparison, we apply Bresenham algorithm to generate pixels in the inlier region of the feature and use the foreground pixels in this region to vote the potential feature. It greatly improves the efficiency and can detect spatially-linked features easily. Experimental results with both synthetic and real images are reported.