Mario Chacon

Instituto Tecnológico de Chihuahua · DEPI

Humans can solve image classification tasks by learning from a few images and reusing prior-knowledge. In Artificial Intelligence, deep-learning models have been implemented to simulate human learning and face problems with little data available, few-shot learning. Nevertheless, one crucial problem of deep-learning is the selection of architectures...

Image segmentation through fuzzy clustering has been widely used in diverse areas. However, most of those clustering algorithms require that some of their parameter values be determined manually. The number of clusters, C, is one of the most important parameters because it impacts the number of regions to segment and directly affects the performanc...

Healthcare has benefited from the implementation of deep-learning models to solve medical image classification tasks. For example, White Blood Cell (WBC) image analysis is used to diagnose different pathologies like leukemia. However, medical datasets are mostly imbalanced, inconsistent, and costly to collect. Hence, it is difficult to select an ad...

A leukogram is essential in the diagnosis of people’s diseases. It can help in the discovery of diseases such as infection, arthritis, leukemia, etc. Deep learning techniques found in the state of the art solve leukocyte classification with the use of complex models including too many components. In this work, we solve leukocyte classification with...

The analysis of leukocytes in blood smear sample images has been a successful tool for medical diagnosis, and there are machine-learning methods for segmenting and classifying leukocytes with these images. However, the datasets for designing these methods have images with different compositions than blood smear samples acquired with the standard pr...

Speech imagery has been successfully employed in developing Brain-Computer Interfaces because it is a novel mental strategy that generates brain activity more intuitively than evoked potentials or motor imagery. There are many methods to analyze speech imagery signals, but those based on deep neural networks achieve the best results. However, more...

Speech imagery has recently been included in the design of Brain–Computer Interfaces to develop novel communication or control systems based on brain activity that does not need external stimulation like evoked potentials. Three types of speech imagery exist: imagining words, syllables, or vowels. Words are composed of syllables and syllables by co...

This paper presents a fuzzy neural method to model background from videos in order to detect dynamic objects. The method includes a weak fuzzy classifier that performs an initial foreground and background separation based on color and depth differences between the actual frame and background models. The outputs of this fuzzy system are weighted acc...

In the human healthcare area, leukocytes are very important blood cells for the diagnosis of different pathologies, like leukemia. Recent technology and image-processing methods have contributed to the image classification of leukocytes. Especially, machine learning paradigms have been used for the classification of leukocyte images. However, repor...

Moving object detection methods, MOD, must solve complex situations found in video scenarios related to bootstrapping, illumination changes, bad weather, PTZ, intermittent objects, color camouflage, camera jittering, low camera frame rate, noisy videos, shadows, thermal videos, night videos, etc. Some of the most promising MOD methods are based on...

Classifier design highly depends on the quality and quantity of the data set. This issue is a problem in areas like medical image classification because acquiring these images may be expensive, time consuming, and require an expert. Thus, data augmentation, DA, may be important for medical images analysis. This paper presents three main contributio...

The implementation of brain-computer interfaces (BCI) for real-time has become a paramount technology. Implementation of real-time BCI systems requires of methodologies that achieve high performance on classification over general brain signals of different subjects. Therefore, this work presents two simple and efficient methodologies to classify tw...

Scene analysis in video sequences is a complex task for a computer vision system. Several schemes have been addressed in this analysis, such as deep learning networks or traditional image processing methods. However, these methods require thorough training or manual adjustment of parameters to achieve accurate results. Therefore, it is necessary to...

Object tracking in video sequences represents an important task in computer vision applications like human mobility and social distance in pandemic scenarios, intelligent surveillance systems, etc. This paper presents a new method, fuzzy weighted color histogram neural network (FWCHNN) to estimate the position of an object and to update the appeara...

Birds of prey especially eagles and hawks have a visual acuity two to five times better than humans. Among the peculiar characteristics of their biological vision are that they have two types of foveae; one shallow fovea used in their binocular vision, and a deep fovea for monocular vision. The deep fovea allows these birds to see objects at long d...

The P300 wave has been successfully employed to develop brain-computer interfaces (BCI) for speller applications. However, methods to analyze the P300 require computers with high processing capability because they are computationally complex and require many electrodes. Therefore, this paper proposes a novel BCI speller system based on the P300 wav...

This paper presents a new method that can classify multiple motor imageries and can be implemented in a realistic application because of its low computation time. The method proposes the use of pattern images, generated with the common spatial pattern (CSP) technique. The paper also suggests a new algorithm to determine the best frequency bands for...

EEG signal analysis provides a new alternative to implement brain computer interfaces. Among the possible signals that can be used for brain computer interfaces are signals generated during blinking. This chapter presents a novel fuzzy modular neural model for linguistic expression recognition using blinking coding detection and classification. The...

Nowadays, the amount of dynamic object detection in video sequences algorithms has increased considerably. Notwithstanding the many efforts to provide benchmarking resource, a standard methodology to achieve this evaluation does not exist. Most of the existing benchmarking resources concentrate on the evaluation of the algorithms from a rigid persp...

Background modeling in video sequences is a prominent topic which generates very relevant works regarding models, algorithms, and databases. Its importance is related to real world applications like, video segmentation, surveillance, Internet of things (IoT), privacy and video compression. This paper proposes an adaptive background modeling method...

Brain–Computer Interfaces (BCI) are systems that translate brain activity patterns into commands for an interactive application, and some of them recognize patterns generated by motor imagery. Currently, these systems present performances and methodologies that still are not practical enough for realistic applications. Therefore, this paper propose...

This chapter presents a pulse-coupled neural network architecture, PCNN, to segment imagery acquired with UAV images. The images correspond to normalized difference vegetation index values. The chapter describes the image analysis system design, the image acquisition elements, the original PCNN architect, the simplified PCNN, the automatic paramete...

Scene analysis is a complex task for a computer vision system, and therefore requires high level processing tasks. Although traditional image processing schemes have been used to implement these tasks, new alternatives based on bio-inspired model have been considered. This paper presents a novel bio-inspired model for static object segmentation fro...

The present work describes a novel method for dynamic object detection in RGB-D videos based completely on a fuzzy logic approach. The method is an original contribution because of its self-adapting fuzzy scheme that fuses color and depth information, RGB-D. The fuzzy system analyzes information related to fuzzy color and depth differences as well...

Background initialization and background update are two important stages considered in the design of most background modeling algorithms. Commonly, these algorithms implement strategies in which their parameters have a very high adaptability in the background initialization stage in order to learn all the variations of the background. Contrary, in...

Based on recently neurocomputational models inspired on neural synchronization for perceptual grouping, we propose in this paper the Gestalt Spiking Cortical Model (GSCM) and the Perceptual Grouping segmentation (PGSeg). The GSCM is a network based on the mechanisms of perceptual grouping models designed to detect scene attributes with excitatory a...

We propose a novel algorithm for segmentation of video background models in time-variant scenarios. It is robust to gradual or abrupt illumination changes, diverse kind of noises, and even scenario variation. The algorithm generates regions according to the scene composition by keeping region segmentation coherence. The proposed method based on a d...

This paper presents a new Background Subtraction System scheme based on two Self Organized Maps (SOM) that adapt in a parallel way at different rates. Our system can automatically identify the possible issue that mainly affects the performance of the video segmentation model (such as dynamic/static background, stationary dynamic objects, jittering...

Driving and transporting goods are necessary for human activity. As a consequence of drivers spending a considerable amount of time at the workplace, and usually under pressure, vehicular accidents have become a great contributor to mortality in several countries. Traffic accidents in countries such as the United States are a central concern. For i...

In this paper we propose a system that involves a Background Subtraction, BS, model implemented in a neural Self Organized Map with a Fuzzy Automatic Threshold Update that is robust to illumination changes and slight shadow problems. The system incorporates a scene analysis scheme to automatically update the Learning Rates values of the BS model co...

This paper describes preliminary results of an auxiliary system designed to obtain a standard of gait kinematic of children in the age of 6 to 12 years of a specific population. It is expected that the use of the system may help children from vulnerable social groups with disabilities due to accidents or illness. The system is based on the Microsof...

A bio-inspired model for head pose recognition is described in this paper. The bio-inspired model recognizes the head by using gray scale information as well as the silhouette of the person. A set of descriptors is generated from this analysis by a hierarchical model based on the visual cortex. Then the descriptors are classified by a multilayer pe...

In this article, a novel model of computer aided diagnosis (CAD) system through breast thermography is proposed with the purpose of diagnosing breast cancer. There are two main factors that were considered in our system: the data base for the design and the type of inputs used for the classifiers. The suggested model is based on a fuzzy classifier...

This chapter presents a novel cellular neural network architecture for image binarization in video sequence. The cellular network is part of a neuroinspired system used to detect dynamic objects in video sequences. Among its novelty is that besides binarization it is able to reduce also noise, and its parameters are self-adapted. Qualitative findin...

Breast thermography is a promising technique allowing breast cancer detection with the aid of infrared technology. However, the automatic segmentation of the regions of interest (ROI) to be analyzed is a difficult task and, thus, it is not commonly performed. In this paper we propose an automated technique for ROI extraction. The algorithm uses Can...

A video segmentation algorithm that takes advantage of using a background subtraction (BS) model with low learning rate (LLR) or a BS model with high learning rate (HLR) depending on the video scene dynamics is presented in this paper. These BS models are based on a neural network architecture, the self-organized map (SOM), and the algorithm is ter...

Object tracking is one of the most important tasks in video analysis systems. Starting with a precise object tracker it is possible to perform video analysis tasks such as people counting, object classification or determine abnormal behaviors to name a few. This paper reports a Rao-Blackwellized Particle Filter model for multiple object tracking. T...

The analysis of moving objects in video sequences has been a paramount issue in applications related to intelligent surveillance systems, robotics, and medicine. Although several works aimed to analyze objects in video sequences have been reported, many of them need manual parameter adjustments and they are not tolerant to illumination changes and...

The present work presents a methodology to automatically detect the symmetry point of breast. In order to achieve this goal, the algorithm corrects thermal image tilt to find a breast symmetry axis, compute a modified symmetric index that can be used as a measure of image quality, breast cosmetic and pathologic issues, and a seed location for a for...

In this paper we propose to combine some state-of-the-art video segmentation algorithms in an unsupervised fashion to take advantage of its strengths. The proposal is based on a Background Subtraction (BS) model with a Self Organized Map neural network architecture and automatic threshold update that has been proven to be robust to illumination cha...

This paper presents a DTCNN model for dynamic and static object segmentation in videos. The proposed method involves three main stages in the dynamic stage; dynamic background registration, dynamic objects detection and object segmentation improvement. Two DTCNNs are used, one to achieved object detection and other for morphologic operations in ord...

Motion detection represents a challenging issue in artificial vision systems. Besides detection of movement in normal scenario conditions robust systems must deal with other non-normal conditions. We propose the improvement of a former neuro-fuzzy motion detection method to face drastic illumination changes, gradual illumination conditions, moving...

Self Organizing Maps (SOMs) are neural networks that have been widely focused in computer vision applications and simulation of visual cortex areas. From among those applications, there are successful works related to neuroinspired motion processing. In this work, we propose the Retinotopic SOM (RESOM); a neural network based on Self-Organizing Ret...

Gait cycle phase detection provides useful information to diagnose possible problems on walking. The work reported here proposes the analysis of gait kinematic signals, extracted from videos, through fuzzy logic to automatically determine the different phases in the human gait cycle. The function of the fuzzy system is to detect the gait phases, lo...

There has been an increasing use of the graphic processing unit (GPU) in many areas including artificial neural networks (ANN) for several years. However, reported works concentrate on the application itself and not on the methodology used to implement the ANN model in the GPU. This paper presents a set of practical aspect to be considered by new G...

Self Organizing Maps (SOMs) are neural networks that have been widely focused in computer vision applications and simulation of visual cortex areas. From among those applications, there are successful works related to neuroinspired motion processing. In this work, we propose the Retinotopic SOM (RESOM); a neural network based on Self-Organizing Ret...

Object detection is a fundamental aspect in surveillance systems. Although several works aimed at detecting objects in video sequences have been reported, many are not tolerant to dynamic background or require complex computation in addition to manual parameter adjustments. This paper proposes an adaptive object detection method to work in dynamic...

In this paper a DTCNN model for dynamic object segmentation in videos is presented. The proposed method involves three main stages; dynamic background registration, dynamic objects detection and object segmentation improvement. Two DTCNNs are used, one to achieved object detection and other for morphologic operations in order to improve object segm...

Development of human gait analysis systems has become of great interest in the medical field because of their capacity to acquire information and perform diagnosis. This paper presents the design of a fuzzy system able to provide a linguistic interpretation of the kinematic analysis for the thigh and knee in the sagittal plane. This analysis allows...

This paper addresses the problem of recognizing signs generated by a person to guide a robot. The proposed method is based on video color analysis of a moving person making signs. The analysis consists of segmentation of the middle body, arm and forearm location and recognition of the arm and forearm positions. The proposed method was experimentall...

Artificial Neural Networks (ANNs) have been useful for decades to the development of Image Processing algorithms applied to several different fields, such as science, engineering, industry, security and medicine. This close relationship between ANNs and Image Processing has motivated a study of 160 papers that propose and deal with said algorithms....

The present work proposes a method for human gait and kinematic analysis. Gait analysis consists of the determination of hip, knee and ankle positions through video analysis. Gait kinematic for the thigh and knee is then generated from this data. Evaluations of the gait analysis method indicate an acceptable performance of 86.66% for hip and knee p...

Dust storms are meteorological phenomena that may affect human life. Therefore, it is of great interest to work towards the development of a stand-alone dust storm detection system that may help to prevent and/or counteract its negative effects. This work proposes a dust storm detection system based on an Artificial Neural Network, ANN. The ANN is...

This paper presents a method to detect fire regions in thermal videos that can be used for both outdoor and indoor environments. The proposed method works with static and moving cameras. The detection is achieved through a linear weighted classifier which is based on two features. The features are extracted from candidate regions by the following p...

Texture segmentation is a complex task in image analysis. Although many works have been done in this area, texture segmentation is still an open research area. The purpose of this paper is to investigate the potential of time signatures generated by a Pulse Coupled Neural Network, PCNN, to perform texture segmentation. Time series features are gene...

Object tracking is a paramount task in video surveillance systems. Although many efforts have been accomplished on object tracking during the last years more work is still needed in order to generate more robust systems. A new fuzzy method for object tracking is presented in this paper. The proposed method is composed of two Sugeno type systems wit...

This paper presents a Computational Intelligence scheme to deal with subjective human inspection tasks in the industry that are subjective measurements. The scheme is used to solve two cosmetic subjective measurements tasks, classification of cosmetic defects and detection of non-uniform color regions in a translucent film. The first problem is sol...

This paper address the detection of dust storms based on a probabilistic analysis of multispectral images. We develop a feature set based on the analysis of spectral bands reported in the literature. These studies have focused on the visual identification of the image channels that reflect the presence of dust storms through correlation with meteor...

The advance of science and technology has motivated to face new and more complex engineering applications. These new challenges must involve not only the design of adaptive and dynamic systems but also the use of correct information. Everyday, it is more evident that good multicriteria decision making systems require different types of information;...

This paper address the dust aerosol detection problem based on a probabilistic multispectral image analysis. Two classifiers are designed. First the Maximum Likelihood classifier is adapted to mode different types of atmospheric components. The second is a Probabilistic Neural Network (PNN) model. The data sets are MODIS multispectral bands from NA...

This paper presents a method to segment geometric structures from the Meccus Phyllosomus bug eggs. These structures are of interest because they may help on the taxonomy of the bug families and their possible hybrids. The segmentation method is based on the Perona - Malik anisotropic filtering to enhance the images. A tune in parameter process is p...

Electric signal analysis from live organism is an old area that was documented by Francesco Redi dated from 1666, Walsh 1773, and Galvani 1792 [1]. Contraction of muscular fibers by electric impulses was recorded by Debois-Raymmod 1849 [1]. Electric impulses known as myolectric signal and their recording are named electromyographic signals or EMG [...

The development of smart sensors involves the design of reconfigurable systems capable of working with different input sensors. Reconfigurable systems expend the least possible amount of time in their calibration. An autocalibration algorithm for smart sensors should be able to fix major problems such as offset, variation of gain and no linearity,...

In this paper we propose a methodology to detect lines or edges for a later construction of geometric maps using a URG-04LX laser range finder from Hokuyo Automatic, Co. First, the algorithm decodes the raw data from the sensor, using URG series, into a list of points in polar coordinates. Afterward, it converts to x-y coordinates in order to have...

Nowadays human beings need to perform dangerous tasks involving the possibility of physical injury. This creates a demand of novel collaborative technologies between human and machines. In this paper we present a collaborative scheme aimed to support a mobile face recognition system for surveillance applications. Such face recognition system is bas...

Background determination is crucial to visual intelligent surveillance systems. Although several methods have been proposed in the literature, research on this topic is still a paramount objective in the surveillance system community. High performance and low computational cost in a video segmentation model are some of the characteristics of the se...

The complexity of classification tasks in computer vision applications has increased, as these systems have been adopted in more real-world applications. One of the hardest tasks to model by a computer system is related to achieving a human task based on subjective perception of the environment or goods. This paper presents a fuzzy classifier aimed...

A new synergetic model based on a pulse neural network and the Perona-Malik anisotropic diffusion algorithm is introduced. The proposed model was developed because of the failure of conventional edge detectors to perform the extraction of geometric structures from low contrast edges and noisy assumed uniform regions. The synergetic model is a varia...

The authors gratefully acknowledge the support provided by SEP-DGEST for this research under grant DGEST 512.07-P.

Este artículo, describe un clasificador neuro difuso, que diferencia entre 4 tipos de defectos en maderas conocidos como botones. La inspección visual de estos defectos por humanos, tiene un alto grado de complejidad ya que dentro de una misma clase existen variaciones en forma, tamaño y color. Las características utilizadas por el clasificador se...

This paper describes a novel fuzzy based approach to determine the complexity of an image which is independent of a human perception criterion. The proposed method determines the complexity of an image based on the analysis of its edge level percentages. First, the method determines the complexity class of an image from among three classes, Little...

Image segmentation has attracted the attention of researcher for many decades. Different approaches have been developed in order to find the solution in many different segmentation situations. In this paper we propose a novel edge detection approach aimed to generate useful information to achieve segmentation. The proposed method is based on analys...

In this paper a fuzzy clustering approach for the classification of cosmetic defects is presented. The paper investigates the solution of this classification problem with the Gustafson-Kessel (GK), and Geth-Geva (GG) with Abonyi-Szeifert (AS) fuzzy algorithms. The clustering process is achieved on multidimensional feature vectors that represent the...

This paper presents a new approach aimed to design a fuzzy face recognition system. Face feature lines, new features proposed in the paper, are incorporated in the feature vector used to design the patter recognition system. Face feature lines are considered as new features based on previous studies related to face recognition tasks on newborns. Be...

Computational intelligence theories offer, individually, different potentials to solve real world problems. However, fusion of these potentials provides opportunities to generate more real world robust systems. Cosmetic inspection of possible non-uniform surfaces found in manufacturing is a challenge to human inspectors. This paper deals with the p...

This paper is concerned with the design of a classification system based on artificial neural networks to distinguish between natural and non-natural cosmetic defects found in ophthalmic lenses. Natural cosmetic defects are related to small cotton fabrics, and non-natural defects are formed during the fabrication process. A set of geometric, morpho...

This paper presents a novel fuzzy multinode communication priority protocol for intelligent network sensors. Each node is part of an intelligent sensor network. The node n_N basically consists of three logic blocks: a digital interface, application, and network communication. The network communication is across a serial bus system defined...

This paper illustrates the potentials of the PCNN for image processing. A description of three schemes for image processing using the PCNN is presented in this paper. The first scheme is related to image segmentation, the second to automatic target location, ATL, and the third to face recognition. The first scheme was developed in order to obtain...

In this paper a method to detect and classify typical electric power disturbances is presented. Voltage sags, swells, momentary outage and capacitor switching transient events (CSTs) are the electric disturbances considered in this work. Disturbance detection and some disturbance features are obtained by the discrete wavelet transform. These featur...

This paper presents a comparative study between a feedforward neural network and a SOM network. The paper also proposes the incorporation of a new spatial feature, face feature lines, FFL, to represent the faces. FFL are considered as new features based on previous studies related to face recognition tasks on newborns. Besides the face feature line...

This paper describes a novel fuzzy based approach to determine the complexity of an image which is independent of a human perception criterion. The proposed method determines the complexity of an image based on the analysis of its edge level percentages. First, the method determines the complexity class of an image from among three classes, Little...

This paper describes a novel fuzzy based approach to determine the complexity of an image which is independent of a human perception criterion. The proposed method determines the complexity of an image based on the analysis of its edge level percentages. First, the method determines the complexity class of an image from among three classes, Little...