Abstract Edge detection is a fundamental image analysis task, as it provides insight on the content of an image. There are weaknesses in some of the edge detectors developed until now, such as disconnected edges, the impossibility to detect branching edges, or the need for a ground truth that is not always accessible. Therefore,…
Abstract The exploitation of the important features exhibited by the complex systems found in the surrounding natural and artificial space will improve computational model performance. Therefore, the purpose of the current paper is to use cellular automata as a tool simulating complexity, able to bring forth an interesting global behaviour based only on simple,…
Abstract The most common cancer type amongst women is the breast cancer with a large number of cases reported each year, many of them diagnosed in an advanced phase. In this paper our scope is to create a base for a support system that helps detecting breast cancer in an early stage. After defining…
Abstract We aimed to assess the reliability of an automatic solution for whole-heart segmentation of MRI images of patients with atrial fibrillation (AF). We propose a semi-interactive image segmentation algorithm based on region growing, GrowCut1, using novel neighborhood structures based on Cellular Automata. We complemented the algorithm with a global view of the signal…
Abstract We aimed to assess the reliability of teaching Artificial Intelligencefor Software Engineering master students. We propose a semi-interactive course where the students have to develop applications for solving real world problems by using various intelligent tools. We try to integrate these two disciplines, since both deal with modeling of the real case studies,…
Abstract Cellular automata have been widely used for solving the edge detection problem. This paper proposes an algorithm which optimizes cellular automata rules using Particle Swarm Optimization based on an existing method in the literature. Moreover, the method is extended from grayscale to RGB images by performing the optimization on each colour channel individually.…
Abstract In this paper, we introduce Competitive Unsupervised GrowCut, a cellular automata-based, unsupervised and autonomous algorithm that combines the label merging component of Unsupervised GrowCut with the soft label propagation mechanism of GrowCut. We evaluated our algorithm on two benchmark image segmentation datasets, along with two related methods proposed in the literature. We also…
Abstract The Shift minimization personnel task scheduling problem (SMPTSP) is a known NP-hard problem. The present paper introduces a novel four-phase meta-heuristic approach for solving the Shift minimization personnel task scheduling problem which consists of an optimal assignment of jobs to multi-skilled employees, such that a minimal number of employees is used and no…
Abstract Extending and optimizing cellular automata to handle 3D volume segmentation is a non-trivial task. First, it does not suffice to simply alter the cell neighborhood (be it von Neumann or Moore), and second, going from 2D to 3D means that the number of operations increases by an order of magnitude, thus GPU acceleration…
Abstract This paper presents an edge detection method based on Cellular Automata where the rules are evolved to optimize the edge detection in binary images. This method divides the edge detection problem into two sub-problems: on the one hand it trains the rules to detect the edge pixels, on the other hand it trains…