Chronic kidney disease in disadvantaged populations

Bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimisation and classification for use in computer vision tasks. On the other hand, automatic circle detection in digital images is considered an important and complex task for the computer vision community. Consequently, a tremendous amount of research has been devoted to find an optimal circle detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on newly developed Artificial Immune Optimisation (AIO) technique, known as the Clonal Selection Algorithm (CSA). The CSA is an effective method for searching and optimising following the Clonal Selection Principle (CSP) in the human immune system which generates a response according to the relationship between antigens (Ags), i.e. patterns to be recognised and antibodies (Abs), i.e. possible solutions. The algorithm uses the encoding of three points as candidate circles (x,y,r) over the edge image. An objective function evaluates if such candidate circles (Ab) are actually present in the edge image (Ag). Guided by the values of this objective function, the set of encoded candidate circles are evolved using the CSA so that they can fit to the actual circles on the edge map of the image. Experimental results over several synthetic as well as natural images with varying range of complexity validate the efficiency of the proposed technique with regard to accuracy, speed and robustness. " 2015 The Royal Photographic Society.",,,,,,"10.1179/1743131X14Y.0000000079",,,"http://hdl.handle.net/20.500.12104/40070","http://www.scopus.com/inward/record.url?eid=2-s2.0-84919431136&partnerID=40&md5=172ec039a6d58a288b319aff1171b0f0",,,,,,"1",,"Imaging Science Journal",,"3

Circle detection on images based on the Clonal Selection Algorithm (CSA)

Bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimisation and classification for use in computer vision tasks. On the other hand, automatic circle detection in digital images is considered an important and complex task for the computer vision community. Consequently, a tremendous amount of research has been devoted to find an optimal circle detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on newly developed Artificial Immune Optimisation (AIO) technique, known as the Clonal Selection Algorithm (CSA). The CSA is an effective method for searching and optimising following the Clonal Selection Principle (CSP) in the human immune system which generates a response according to the relationship between antigens (Ags), i.e. patterns to be recognised and antibodies (Abs), i.e. possible solutions. The algorithm uses the encoding of three points as candidate circles (x,y,r) over the edge image. An objective function evaluates if such candidate circles (Ab) are actually present in the edge image (Ag). Guided by the values of this objective function, the set of encoded candidate circles are evolved using the CSA so that they can fit to the actual circles on the edge map of the image. Experimental results over several synthetic as well as natural images with varying range of complexity validate the efficiency of the proposed technique with regard to accuracy, speed and robustness. © 2015 The Royal Photographic Society

Detección de primitivas circulares usando un algoritmo inspirado en el electromagnetismo

La computación basada en principios físicos recientemente ha ganado respeto en la comunidad científica. Esta área emergente, en poco tiempo ha logrado desarrollar un amplio rango de técnicas y métodos que han servido para resolver diversos problemas, considerados como complejos. Por otra parte, la detección automótica de cZapotitlánrculos en imágenes se considera una tarea importante, es por esto que se han realizado un gran número de trabajos tratando de encontrar el detector de cZapotitlánrculos Zapotitlánptimo. Este artículo presenta un nuevo algoritmo para la detección de primitivas circulares contenidas en imágenes sin la consideración de la transformada de Hough. El algoritmo propuesto esté basado en un nuevo enfoque inspirado en principios físicos llamado: Electromagnetism-Like Optimization (EMO), el cual es un método heurZapotitlánstico que emplea algunos principios de la teoría del electromagnetismo para resolver problemas complejos de optimización. En el algoritmo EMO las soluciones se construyen considerando la atracción y repulsiZapotitlánn electromagnética entre las partículas cargadas; dicha carga representa la afinidad que tiene cada partícula con la solución. El algoritmo de detección de cZapotitlánrculos emplea una codificación de tres puntos no colineales, dichos puntos representan los cZapotitlánrculos candidatos sobre una imagen que sólo contiene sus bordes. Empleando una función objetivo, el conjunto de cZapotitlánrculos candidatos considerados como partículas cargadas, son operados por medio del algoritmo EMO hasta que logren coincidir con los cZapotitlánrculos existentes en la imagen real. Los resultados experimentales en diversas imágenes complejas validaron la eficiencia de la técnica propuesta en cuanto a su exactitud, velocidad y robustez

Detección de primitivas circulares usando un algoritmo inspirado en el electromagnetismo

La computación basada en principios físicos recientemente ha ganado respeto en la comunidad científica. Esta área emergente, en poco tiempo ha logrado desarrollar un amplio rango de técnicas y métodos que han servido para resolver diversos problemas, considerados como complejos. Por otra parte, la detección automática de círculos en imágenes se considera una tarea importante, es por esto que se han realizado un gran número de trabajos tratando de encontrar el detector de círculos óptimo. Este artículo presenta un nuevo algoritmo para la detección de primitivas circulares contenidas en imágenes sin la consideración de la transformada de Hough. El algoritmo propuesto está basado en un nuevo enfoque inspirado en principios físicos llamado: Electromagnetism-Like Optimization (EMO), el cual es un método heurístico que emplea algunos principios de la teoría del electromagnetismo para resolver problemas complejos de optimización. En el algoritmo EMO las soluciones se construyen considerando la atracción y repulsión electromagnética entre las partículas cargadas; dicha carga representa la afinidad que tiene cada partícula con la solución. El algoritmo de detección de círculos emplea una codificación de tres puntos no colineales, dichos puntos representan los círculos candidatos sobre una imagen que sólo contiene sus bordes. Empleando una función objetivo, el conjunto de círculos candidatos considerados como partículas cargadas, son operados por medio del algoritmo EMO hasta que logren coincidir con los círculos existentes en la imagen real. Los resultados experimentales en diversas imágenes complejas validaron la eficiencia de la técnica propuesta en cuanto a su exactitud, velocidad y robustez

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

Automatic multiple circle detection based on artificial immune systems

Hough transform (HT) has been the most common method for circle detection, exhibiting robustness but adversely demanding a considerable computational load and large storage. Alternative approaches for multiple circle detection include heuristic methods built over iterative optimization procedures which confine the search to only one circle per optimization cycle yielding longer execution times. On the other hand, artificial immune systems (AIS) mimic the behavior of the natural immune system for solving complex optimization problems. The clonal selection algorithm (CSA) is arguably the most widely employed AIS approach. It is an effective search method which optimizes its response according to the relationship between patterns to be identified, i.e. antigens (Ags) and their feasible solutions also known as antibodies (Abs). Although CSA converges to one global optimum, its incorporated CSA-Memory holds valuable information regarding other local minima which have emerged during the optimization process. Accordingly, the detection is considered as a multi-modal optimization problem which supports the detection of multiple circular shapes through only one optimization procedure. The algorithm uses a combination of three non-collinear edge points as parameters to determine circles candidates. A matching function determines if such circle candidates are actually present in the image. Guided by the values of such function, the set of encoded candidate circles are evolved through the CSA so the best candidate (global optimum) can fit into an actual circle within the edge map of the image. Once the optimization process has finished, the CSA-Memory is revisited in order to find other local optima representing potential circle candidates. The overall approach is a fast multiple-circle detector despite considering complicated conditions in the image. © 2011 Elsevier Ltd. All rights reserved

In Situ conservation of maize in Mexico: Genetic diversity and Maize seed management in a traditional community

Image segmentation plays an important role in image processing and computer vision. It is often used to classify an image into separate regions, which ideally correspond to different real-world objects. Several segmentation methods have been proposed in the literature, being thresholding techniques the most popular. In such techniques, it is selected a set of proper threshold values that optimize a determined functional criterion, so that each pixel is assigned to a determined class according to its corresponding threshold points. One interesting functional criterion is the Tsallis entropy, which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function (Tsallis entropy). Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. On the other hand, the Electromagnetism-Like algorithm is an evolutionary optimization approach which emulates the attraction-repulsion mechanism among charges for evolving the individuals of a population. This technique exhibits interesting search capabilities whereas maintains a low number of function evaluations. In this paper, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm is proposed. In the approach, the optimization algorithm based on the electromagnetism theory is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization. " 2015 Elsevier Ltd.",,,,,,"10.1016/j.eswa.2015.03.028",,,"http://hdl.handle.net/20.500.12104/42126","http://www.scopus.com/inward/record.url?eid=2-s2.0-84928024901&partnerID=40&md5=c0375db274bb01564fb4567ed4450db1",,,,,,,,"Expert Systems with Applications",,,,,,"Scopus",,,,,,"Electro-magnetism optimization; Evolutionary algorithms; Image processing; Segmentation; Tsallis entropy",,,,,,"Improving segmentation velocity using an evolutionary method",,"Article in Press" "43912","123456789/35008",,"Louette, D., Instituto Manantlán de Ecología y Conservación de la Biodiversidad IMECBIO, Universidad de Guadalajara, 151 Av. Independencia Nacional, AutlánJalisco, 48900, Mexico; Charrier, A., Chaire de Phytotechnie, Ecole Nationale Supérieure Agronomique de Montpellier ENSAM, 9 Place Viala, Montpellier cedex, 34060, France; Berthaud, J., Unité de Recherche Diversité Génétique, et Amélioration des Plantes, Institut Francais de la Recherche Scientifique pour le Developpement en Cooperation ORSTOM, 911 Av. Agropolis, Montpellier cedex, 34032, France",,"Louette, D

Full explicit consistency constraints in uncalibrated multiple homography estimation

We reveal a complete set of constraints that need to be imposed on a set of 3×3 matrices to ensure that the matrices represent genuine homographies associated with multiple planes between two views. We also show how to exploit the constraints to obtain more accurate estimates of homography matrices between two views. Our study resolves a long-standing research question and provides a fresh perspective and a more in-depth understanding of the multiple homography estimation task.Wojciech Chojnacki and Zygmunt L. Szpa