Ponce, Hiram
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Ponce, Hiram
Official Name
Ponce Espinosa, Hiram Eredín
Alternative Name
hponce
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ORCID
Scopus Author ID
54911890000
Researcher ID
K-7593-2019

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An Implementation of a Monocular 360-Degree Vision System for Mobile Robot Navigation

2018 , Acevedo Medina, Eduardo , Beltrán, Arturo , Castellanos Canales, Mauricio , Chaverra, Luis , González Mora, José Guillermo , Sarmiento Oseguera, Manuel Antonio , Ponce, Hiram

One of the problems facing autonomous navigation is obstacle sensing and dynamic surroundings. Multi-sensor systems and omni directional vision systems have been implemented to increase the observability of robots. However, these approaches consider several drawbacks: the cost of processing multiple signals, synchronization of data collection, cost of materials and energy consumption, among others. Thus in this paper, we propose a new 360-degree vision system for mobile robot navigation using a static monocular camera. We demonstrate that using our system, it is possible to monitor the complete surroundings of the robot with a single sensor, i.e. the camera. Moreover, it can detect the position and orientation of an object from an egocentric point of view. We also present a low cost prototype of our proposal to validate it. © 2018 IEEE.

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Vision-Based Analysis on Leaves of Tomato Crops for Classifying Nutrient Deficiency using Convolutional Neural Networks

2020 , Cevallos Vega, Claudio Sebastián , Ponce, Hiram , Moya-Albor, Ernesto , Brieva, Jorge

Tomato 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.

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An Edge Detection Method using a Fuzzy Ensemble Approach

2017 , Moya-Albor, Ernesto , Ponce, Hiram , Brieva, Jorge

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.

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