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Item type:Publication, An Edge Detection Method using a Fuzzy Ensemble Approach(2017); ; Edge detection is one of the most important low level steps in image processing. In this work we propose a fuzzy ensemble based method for edge detection including a fuzzy c-means (FCM) approach to define the input membership functions of the fuzzy inference system (FIS). We tested the performance of the method using a public database with ground truth. Also, we compared our proposal with classical and other fuzzy based methods, using F-measure curves and the precision metric. We conducted experiments with different levels of salt & pepper noise to evaluate the performance of the edge detectors. The metrics illustrate the robustness of the choice of the threshold in the binarization step using this fuzzy ensemble method. In noisy conditions, the proposed method works better than other fuzzy approaches. Comparative results validated that our proposal overcomes traditional techniques. © 2017, Budapest Tech Polytechnical Institution. All rights reserved.Scopus© Citations 11 14 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A multiprocess Salp swarm optimization with a heuristic based on crossing partial solutions(2021); Murillo-Suarez, AlfonsoThe Salp swarm algorithm (SSA) is one of the most recent metaheuristic optimization algorithms. SSA has been used succesfully to solve optimization problems in different research areas such as machine learning, engineering design, wireless networks, image processing, mobile robotics, and energy. In this article, we present a multi-threaded implementation of the SSA algorithm. Each thread executes an SSA algorithm that shares information among the swarms to get a better solution. The best partial solutions of each swarm intersect in a similar way of genetic algorithms. The experiments with nineteen benchmark functions (unimodal, multimodal, and composite) show the results obtained with this new algorithm are better than those achieved with the original algorithm. © 2020 The Authors. Published by Elsevier B.V.Scopus© Citations 2 26 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Implementation of a Monocular 360-Degree Vision System for Mobile Robot Navigation(2018)Scopus© Citations 3 25 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Vision-Based Analysis on Leaves of Tomato Crops for Classifying Nutrient Deficiency using Convolutional Neural Networks(2020); ; Brieva, JorgeTomato crops are one of the most important agricultural products at economic level in the world. However, the quality of the tomato fruits is highly dependent to the growing conditions such as the nutrients. One of consequences of the latter during tomato harvesting is nutrient deficiency. Manually, it is possible to anticipate the lack of primary nutrients (i.e. nitrogen, phosphorus and potassium) by looking the appearance of the leaves in tomato plants. Thus, this paper presents a supervised vision-based monitoring system for detecting nutrients deficiencies in tomato crops by taking images from the leaves of the plants. It uses a Convolutional Neural Network (CNN) to recognize and classify the type of nutrient that is deficient in the plants. First, we created a data set of images of leaves of tomato plants showing different symptoms due to the nutrient deficiency. Then, we trained a suitable CNN-model with our images and other augmented data. Experimental results showed that our CNN-model can achieve 86.57% of accuracy. We anticipate the implementation of our proposal for future precision agriculture applications such as automated nutrient level monitoring and control in tomato crops. © 2020 IEEE.Scopus© Citations 23 15 1