Claudia I. Gonzalez

Edge-Detection Method for Image Processing Based on Generalized Type-2 Fuzzy Logic

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 is used to implement generalized type-2 fuzzy logic for edge detection. For the defuzzification process, the heights and approximation methods are used. Simulation results with a type-1 fuzzy inference system, an interval type-2 fuzzy inference system, and with a generalized type-2 fuzzy inference system for edge detection are presented. The proposed generalized type-2 fuzzy edge-detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of using generalized type-2 fuzzy logic.

Modular Neural Networks and Fuzzy Sugeno Integral for Face and Fingerprint Recognition

We describe in this paper a new approach for pattern recognition using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces and fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral with some modifications. Response integration is required to combine the outputs of all the modules in the modular network. We have applied the new approach for fingerprint and face recognition with a real database from students of our institution.

Review of Recent Type-2 Fuzzy Controller Applications

Type-2 fuzzy logic controllers (T2 FLC) can be viewed as an emerging class of intelligent controllers because of their abilities in handling uncertainties; in many cases, they have been shown to outperform their Type-1 counterparts. This paper presents a literature review on recent applications of T2 FLCs. To follow the developments in this field, we first review general T2 FLCs and the most well-known interval T2 FLS algorithms that have been used for control design. Certain applications of these controllers include robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in the robotics and automotive areas, where T2 FLCs have been demonstrated and proven to perform better than traditional controllers. With the development of enhanced algorithms, along with the advancement in both hardware and software, we shall witness increasing applications of these frontier controllers.

Modular Neural Networks and Fuzzy Sugeno Integral for Pattern Recognition: The Case of Human Face and Fingerprint

We describe in this paper a new approach for pattern recogni- tion using modular neural networks with a fuzzy logic method for response integration. We proposed a new architecture for modular neural networks for achieving pattern recognition in the particular case of human faces and fingerprints. Also, the method for achieving response integration is based on the fuzzy Sugeno integral with some modifications. Response integra- tion is required to combine the outputs of all the modules in the modular network. We have applied the new approach for fingerprint and face rec- ognition with a real database from students of our institution.

Cuckoo search algorithm for the optimization of type-2 fuzzy image edge detection systems

This paper presents the optimization of the antecedent parameters for a system of image edge detection based on the Sobel technique combined with interval type-2 fuzzy logic. The optimal design of fuzzy systems is a difficult task and for this reason the use of meta-heuristic optimization techniques is considered in this paper. For the optimization of the fuzzy inference system the Cuckoo Search (CS) algorithm is applied, and the idea is to find the optimal design parameters of interval type 2 fuzzy systems and achieve better results in applications of edge detection for digital images.

An Extension of the Fuzzy Possibilistic Clustering Algorithm Using Type-2 Fuzzy Logic Techniques

In this work an extension of the Fuzzy Possibilistic C-Means (FPCM) algorithm using Type-2 Fuzzy Logic Techniques is presented, and this is done in order to improve the efficiency of FPCM algorithm. With the purpose of observing the performance of the proposal against the Interval Type-2 Fuzzy C-Means algorithm, several experiments were made using both algorithms with well-known datasets, such as Wine, WDBC, Iris Flower, Ionosphere, Abalone, and Cover type. In addition some experiments were performed using another set of test images to observe the behavior of both of the above-mentioned algorithms in image preprocessing. Some comparisons are performed between the proposed algorithm and the Interval Type-2 Fuzzy C-Means (IT2FCM) algorithm to observe if the proposed approach has better performance than this algorithm.

Fuzzy FWA with dynamic adaptation of parameters

We propose in this paper the use fuzzy logic to adjust parameters in the fireworks algorithm (FWA), that is, parameters that usually are considered as constants in the algorithm, we have transformed them to be dynamic parameters in the FWA. First, we realized an exhaustive experimentation of the parameters of the FWA algorithm, with the purpose of selecting the parameters that have more effect on the FWA performance, and we concluded that the main parameters of this algorithm are: numbers of sparks and the explosion amplitude of each firework. The modifications made to these parameters help us provide a better exploration and exploitation abilities to the algorithm. The main goal of this paper is to optimize the performance of the FWA. In this paper, we show the results of the modified algorithm, which we called fuzzy fireworks algorithm and we denoted as FFWA. The results of the experiments were obtained with 6 benchmarks functions.

General Type-2 Fuzzy Edge Detection in the Preprocessing of a Face Recognition System

In this paper, we present the advantage of using a general type-2 fuzzy edge detector method in the preprocessing phase of a face recognition system. The Sobel and Prewitt edge detectors combined with GT2 FSs are considered in this work. In our approach, the main idea is to apply a general type-2 fuzzy edge detector on two image databases to reduce the size of the dataset to be processed in a face recognition system. The recognition rate is compared using different edge detectors including the fuzzy edge detectors (type-1 and interval type-2 FS) and the traditional Prewitt and Sobel operators.

Review of Recent Type-2 Fuzzy Image Processing Applications

This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

A new approach based on generalized type-2 fuzzy logic for edge detection

This paper presents an edge detection method based on morphological gradient technique and generalized type-2 fuzzy logic. The theory of alpha planes is used to implement generalized type-2 fuzzy logic. For the test we used the method of defuzzification by height and approximation. The simulation results were obtained with a type-1 fuzzy inference system (T1FIS), an interval type-2 fuzzy inference system (IT2FIS) and with a generalized type-2 fuzzy logic (GT2FIS). The proposed type-2 fuzzy edge detection method was tested with benchmark images and synthetic images. We used the merit of Pratt measure to illustrate the advantages of the use of generalized type-2 fuzzy logic.