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

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

Алгоритм обнаружения графических примитивов типа окружность при обработке изображений в реальном времени

Задача обнаружения окружностей важна для приложений анализа изображений, таких как автоматический контроль выпускаемой продукции и комплектующих изделий, автоматизированная векторизация чертежей, обнаружение цели и т.д. Наиболее применяемым для поиска аналитически заданных примитивов является Хаф-ориентированный подход, с помощью которого анализируется детектор края и выводится местоположение и значение радиуса окружности. Однако, такой подход требует большого объема памяти для хранения данных. Кроме того, точность обнаружения окружностей с нечеткими краями, особенно в условиях присутствия шума на изображении увеличивает требуемое время обработки. В связи с этим, нами предложен новый метод быстрого обнаружения окружностей при обработке изображений, позволяющий использовать его в реальном времени.Исследование выполнено при финансовой поддержке РФФИ в рамках научного проекта № 16-47-630829

Optical Character Recognition for Brahmi Script Using Geometric Method

Optical character recognition (OCR) system has been widely used for conversion of images of typed, handwritten or printed text into machine-encoded text (digital character). Previous researches on character recognition of South Asian scripts focus on modern scripts such as Sanskrit, Hindi, Tamil, Malayalam, and Sinhala etc. but little work is traceable to Brahmi script which is referred to as the origin of many scripts in south Asian. This study proposes a method for recognition of both handwritten and printed Brahmi characters which involve preprocessing, segmentation, feature extraction, and classification of Brahmi script characters. The geometric method was used for feature extraction into six different entities, followed by a newly developed classification rules to recognize the Brahmi characters based on the features. The method obtains accuracy of 91.69% and 89.55% for handwritten vowels and consonants character respectively and 93.30% and 94.90% for printed vowel and consonants character respectively

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 (HT) principles. The proposed algorithm is based on Learning Automata (LA) which is a probabilistic optimisation 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. � 2012 The Institution of Engineering and Technology

Fast algorithm for multiple-circle detection on images using learning automata

Hough transform has been the most common method for circle detection exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches include heuristic methods that employ iterative optimisation procedures for detecting multiple circles under the inconvenience that only one circle can be marked at each optimisation cycle demanding a longer execution time. In contrast, learning automata (LA) is a heuristic method to solve complex multi-modal optimisation problems. Although LA converges to just one global minimum, the final probability distribution holds valuable information regarding other local minima which have emerged during the optimisation process. The detection process is considered as a multi-modal optimisation problem, allowing the detection of multiple circular shapes through only one optimisation procedure. The algorithm uses a combination of three edge points as parameters to determine circles candidates. A reinforcement signal determines whether such circle candidates are actually present at the image. Guided by the values of such reinforcement signal, the set of encoded candidate circles are evolved using the LA so that they can fit into actual circular shapes over the edge-only map of the image. The overall approach is a fast multiple-circle detector despite facing complicated conditions. © The Institution of Engineering and Technology 2012