Detecção de bordas em imagens de ressonância magnética por meio de processamento de imagenscom algoritmos genéticos

A detecção de bordas em imagens digitais é uma etapa importante do processamento e análise de imagens, pois permite a localização dos objectos presentes nas mesmas, bem como a extracção de características importantes para o seu reconhecimento, tais como rugosidade da borda e dimensões e forma do objecto. Na tentativa de obter resultados mais precisos, viários métodos de detecção de bordas têm sido propostos. Neste trabalho, aborda-se a aplicação de algoritmos genéticos para detectar bordas de regiões anormais em imagens de ressonância magnética, com o objectivo de auxiliar no diagnóstico de tumores cerebrais. Os algoritmos genéticos são métodos de busca e optimização baseados na evolução dos seres vivos proposta por Charles Darwin, que declarou que os seres vivos adaptados ao seu ambiente são os que possuem maiores chances de sobreviver e gerar descendência. Estes algoritmos possuem duas estruturas básicas (genes e cromossomos) e três operações (selecção, cruzamento e mutação). Para ser aplicado em processamento de imagens, cada pixel é considerado um gene e os cromossomos um grupo de genes, ou seja, uma região com um determinado número de pixels. Os resultados obtidos neste trabalho mostraram-se animadores na detecção de tumores cerebrais de difícil diagnóstico visual, melhorando a visualização do mesmo pelo especialista médico

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

EDGE DETECTION PARAMETER OPTIMIZATION BASED ON THE GENETIC ALGORITHM FOR RAIL TRACK DETECTION

One of the most important parameters in an edge detection process is setting up the proper threshold value. However, that parameter can be different for almost each image, especially for infrared (IR) images. Traditional edge detectors cannot set it adaptively, so they are not very robust. This paper presents optimization of the edge detection parameter, i.e. threshold values for the Canny edge detector, based on the genetic algorithm for rail track detection with respect to minimal value of detection error. First, determination of the optimal high threshold value is performed, and the low threshold value is calculated based on the well-known method. However, detection results were not satisfactory so that, further on, the determination of optimal low and high threshold values is done. Efficiency of the developed method is tested on set of IR images, captured under night-time conditions. The results showed that quality detection is better and the detection error is smaller in the case of determination of both threshold values of the Canny edge detector

Towards computer vision based ancient coin recognition in the wild — automatic reliable image preprocessing and normalization

As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this lies in the (often implicit) assumptions made by many of the proposed algorithms — a lack of background clutter, and a uniform scale, orientation, and translation of coins across different images. These assumptions are not satisfied by default and before any further progress in the realm of more complex analysis is made, a robust method capable of preprocessing and normalizing images of coins acquired ‘in the wild’ is needed. In this paper we introduce an algorithm capable of localizing and accurately segmenting out a coin from a cluttered image acquired by an amateur collector. Specifically, we propose a two stage approach which first uses a simple shape hypothesis to localize the coin roughly and then arrives at the final, accurate result by refining this initial estimate using a statistical model learnt from large amounts of data. Our results on data collected ‘in the wild’ demonstrate excellent accuracy even when the proposed algorithm is applied on highly challenging images.Postprin

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