25 research outputs found
Circle detection on images using Learning Automata
Circle detection over digital images has received considerable attention from
the computer vision community over the last few years devoting a tremendous
amount of research seeking for an optimal detector. This article presents an
algorithm for the automatic detection of circular shapes from complicated and
noisy images with no consideration of conventional Hough transform principles.
The proposed algorithm is based on Learning Automata (LA) which is a
probabilistic optimization method that explores an unknown random environment
by progressively improving the performance via a reinforcement signal
(objective function). The approach uses the encoding of three non-collinear
points as a candidate circle over the edge image. A reinforcement signal
(matching function) indicates if such candidate circles are actually present in
the edge map. Guided by the values of such reinforcement signal, the
probability set of the encoded candidate circles is modified through the LA
algorithm so that they can fit to the actual circles on the edge map.
Experimental results over several complex synthetic and natural images have
validated the efficiency of the proposed technique regarding accuracy, speed
and robustness.Comment: 26 Page
The unsupervised learning algorithm for detecting ellipsoid objects
This paper is devoted to the analysis and implementation of the algorithms for automatic detection of the circular objects in the image. The practical aim of this task is development of the algorithm for automatic detection of log abuts in the images of roundwood batches. Based on literature review four methods were chosen for the further analysis and the best performance out of them was provided by ELSD algorithm. Some modifications were implemented to the algorithm to fulfill the requirements of the given task. After all, the modified ELSD algorithm was tested on the dataset of the images. The relative accuracy of the algorithm in comparison with manual measurement is 95.2% for the images with total area of background scene less than 20%. © 2019 International Association of Computer Science and Information Technology
Automatic gauge detection via geometric fitting for safety inspection
For safety considerations in electrical substations, the inspection robots are recently deployed to monitor important devices and instruments with the presence of skilled technicians in the high-voltage environments. The captured images are transmitted to a data station and are usually analyzed manually. Toward automatic analysis, a common task is to detect gauges from captured images. This paper proposes a gauge detection algorithm based on the methodology of geometric fitting. We first use the Sobel filters to extract edges which usually contain the shapes of gauges. Then, we propose to use line fitting under the framework of random sample consensus (RANSAC) to remove straight lines that do not belong to gauges. Finally, the RANSAC ellipse fitting is proposed to find most fitted ellipse from the remaining edge points. The experimental results on a real-world dataset captured by the GuoZi Robotics demonstrate that our algorithm provides more accurate gauge detection results than several existing methods
Ellipse detection through decomposition of circular arcs and line segments
International audienceIn this work we propose an efficient and original method for ellipse detection which relies on a recent contour representation based on arcs and line segments \cite{NguyenD11a}. The first step of such a detection is to locate ellipse candidate with a grouping process exploiting geometric properties of adjacent arcs and lines. Then, for each ellipse candidate we extract a compact and significant representation defined from the segment and arc extremities together with the arc middle points. This representation allows then a fast ellipse detection by using a simple least square technique. Finally some first comparisons with other robust approaches are proposed