Examinando por Autor "Herrera-Rivera, Sergio"
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- PublicaciónSólo datosColor Classification Methods for Perennial Weed Detection in Cereal Crops(Lecture Notes in Computer Science, 2019-03-03) Forero Vargas, Manuel Guillermo; Herrera-Rivera, Sergio; Ávila-Navarro, Julián; Franco, Camilo Andres; Rasmussen, Jesper; Nielsen, JonCirsium arvense is an invasive plant normally found in cold climates that affects cereal crops. Therefore, its detection is important to improve crop production. A previous study based on the analysis of aerial photographs focused on its detection using deep learning techniques and established methods based on image processing. This study introduces an image processing technique that generates even better results than those found with machine learning algorithms; this is reflected in aspects such as the accuracy and speed of the detection of the weeds in the cereal crops. The proposed method is based on the detection of the extreme green color characteristic of this plant with respect to the crops. To evaluate the technique, it was compared to six popular machine learning methods using images taken from two different heights: 10 and 50 m. The accuracy obtained with the machine learning techniques was 97.07% at best with execution times of more than 2 min with 200 × 200-pixel subimages, while the accuracy of the proposed image processing method was 98.23% and its execution time was less than 3 s.
- PublicaciónSólo datosGenerating Random Variates via Kernel Density Estimation and Radial Basis Function Based Neural Networks(Lecture Notes in Computer Science, 2019-03-03) Forero Vargas, Manuel Guillermo; Herrera-Rivera, Sergio; Candia-García, CristianWhen modeling phenomena that cannot be studied by deterministic analytical approaches, one of the main tasks is to generate random variates. The widely-used techniques, such as the inverse transformation, convolution, and rejection-acceptance methods, involve a significant amount of statistical work and do not provide satisfactory results when the data do not conform to the known probability density functions. This study aims to propose an alternative nonparametric method for generating random variables that combines kernel density estimation (KDE), and radial basis function based neural networks (RBFBNNs). We evaluate the method’s performance using Poisson, triangular, and exponential probability density distributions and assessed its utility for unknown distributions. The results show that the model’s effectiveness depends substantially on selecting an appropriate bandwidth value for KDE and a certain minimum number of data points to train the algorithm. the proposed method enabled us to achieve an R2 value between 0.91 and 0.99 for analyzed distributions.
- PublicaciónSólo datosNew Method for Extreme Color Detection in Images(Lecture notes in computer sciences, 2020-06-24) Forero, Manuel G.; Ávila-Navarro, Julián Alberto; Herrera-Rivera, SergioIn image processing and computer vision, it is common to find applications, in which it is necessary to detect reference points characterized by extreme color, i.e., a primary color RGB or complementary CMY with very high saturation. Thus, there are cases in which a certain class of objects can be distinguished according to their characteristic extreme color, which can be used as landmarks or to identify objects. Therefore, there is an interest in identifying landmarks characterized by extreme colors. In this paper, a new method for detecting objects with an extreme color is introduced and compared with other approaches found in the literature. The methods are analyzed and compared using a color palette in which a transition between R, G, B, C, M and Y colors is generated. The results obtained show that the methods studied allow the specific colors to be adequately discriminated, while the proposed method is the only one that allows the full range of extreme colors R, G, B, C, M and Y to be detected, being more selective than the others, by taking practically the areas corresponding to each color separately