A5056457256- Fuzzy Logic and Control Systems
- Neural Networks and Applications
- Rough Sets and Fuzzy Logic
- Fuzzy Systems and Optimization
- Multi-Criteria Decision Making
- Face and Expression Recognition
- Image Retrieval and Classification Techniques
- Advanced Scientific Research Methods
- Medical Image Segmentation Techniques
- Fault Detection and Control Systems
- Metaheuristic Optimization Algorithms Research
- Image and Object Detection Techniques
- Stock Market Forecasting Methods
- Remote-Sensing Image Classification
- 3D Surveying and Cultural Heritage
- 3D Shape Modeling and Analysis
- Advanced Text Analysis Techniques
- Computer Graphics and Visualization Techniques
- Explainable Artificial Intelligence (XAI)
- Advanced Algorithms and Applications
- Cognitive Science and Education Research
- Advanced Numerical Analysis Techniques
- Embodied and Extended Cognition
- Color Science and Applications
- Education, Healthcare and Sociology Research
Universidad Autónoma de Baja California
2011-2021
Instituto Tecnológico de Tijuana
2006-2016
Universidad de Tijuana
2006-2015
University of California, Berkeley
2012
This paper presents an edge-detection method that is based on the morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes used to implement logic for edge detection. For defuzzification process, heights approximation methods are used. Simulation results with a type-1 inference system, interval system detection presented. proposed was tested benchmark images synthetic images. We merit Pratt measure illustrate advantages using
In this paper, a modification of the Sugeno integral with interval type-2 fuzzy logic is proposed. The includes changing original equations Measures and that were initially proposed for type-1 logic. enables calculation combining multiple source information higher degree uncertainty than traditional integral. advantages are illustrated by reporting improved recognition rates in benchmark face databases. This new concept could also be useful tool other areas applications. Also, improvement...
A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 (IT2FLS) and Generalized (GT2FLS) for dynamic adaptation alpha beta parameters a Bee Colony Optimization (BCO) algorithm is presented. The objective work to focus on BCO technique find optimal distribution membership functions in design controllers. We use specifically tuning controller trajectory stability an autonomous mobile robot. add two perturbations model...
Edges detection in a digital image is the first step an recognition system. In this paper, we show efficient edges detector using interval type-2 fuzzy inference system (FIS-2). The FIS-2 uses as input original images after applying Sobel filters and attenuation filters, then rules infer normalized values for images, especially useful to enhance performance of neural networks. To illustrate results, built frequency histograms some compare results edge's with gradient magnitude method type-1...
A type-2 fuzzy edge detection method is presented in this paper. The general process consists of first obtaining the image gradients four directions—horizontal, vertical, and two diagonals—and technique known as morphological gradient. After that, Sugeno integral (GT2 FSI) used to integrate gradients. In second step, GT2 FSI establishes criteria determine at which level obtained gradient belongs an during process; calculated assigning different densities, these are aggregated using meet join...
Edges detection in digital images is a problem that has been solved by means of the application different techniques from signal processing. Also combination some these with fuzzy inference system (FIS) applied. In this work new FIS type-2 method implemented for edges and results three same goal are compared.
In this paper we present a method for face recognition combining modular neural networks and two interval type-2 fuzzy inference systems (FIS 2) recognition. The first FIS 2 is used edges detection in the training data, second one to find ideal parameters Sugeno integral as decision operator. Fuzzy logic shown be tool that can help improve results of system facilitating representation human perception.
In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard's index (JI) and Dice's coefficient (DC). These compare two first one is reference image, second image. It important mention that all existing must binarize images before their evaluation. Binarization step causes information be lost because an incomplete image being evaluated. this paper, propose a fuzzy (FI) for edge evaluation does not use binarization...