Jesus Martínez-Gómez

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Verified

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• PhD
• Professor (Associate) at University of Castilla-La Mancha

Indoor localization determines an object's position within enclosed spaces, with applications in navigation, asset tracking, robotics, and context-aware computing. Technologies range from WiFi and Bluetooth to advanced systems like Massive Multiple Input-Multiple Output (MIMO). MIMO, initially designed to enhance wireless communication, is now key...

Data augmentation is widely applied in various computer vision problems for artificially increasing the size of a dataset by transforming the original data. These techniques are employed in small datasets to prevent overfitting, and also in problems where labelling is difficult. Nevertheless, data augmentation assumes that transformations preserve...

This paper addresses the research problem of Offline Arabic Handwriting Text Recognition (HTR). One of the most important approaches to HTR systems is deep learning. A large amount of annotated data is needed to train deep learning-based HTR systems. The Arabic language is spoken by hundreds of millions of people in North Africa and the Middle East...

Many of the research problems in robot vision involve the detection of keypoints, areas with salient information in the input images and the generation of local descriptors, that encode relevant information for such keypoints. Computer vision solutions have recently relied on Deep Learning techniques, which make extensive use of the computational c...

Predictive maintenance is a key point to reduce cost in energy production. In this work we focus on wind energy and so on wind turbines. We start from the basis of having a sensors-based condition monitoring system installed in the wind turbine, which is in charge of registering measures/signals about some critical components. In this paper we prop...

One of the most common tasks in assistive robotics is to find some specific object in a home environment. Usually, this task is tackled by adding the objects of interest to a map of the environment as soon as the objects are detected by the vision system of the robot. However, these maps are usually static, and do not take into account the dynamic...

A 3D semantic map can be defined as a grid-based representation of the environment, where each bin stores a probability distribution over the possible elements to be found in it. This probability distribution can be obtained with any state-of-the-art image classifier, while the 3D position depends on the localization accuracy of the robot, the sens...

In the last few years, the platforms for online learning, such as MOOCs, are becoming more and more popular. Particularly, in fields like computer science, students very often choose this way, instead of official programs, to complete their formation. In this context, universities must adapt to changes in order to offer the kind of formation that i...

CORTEX is a cognitive robotics architecture inspired by three key ideas: modularity, internal modelling and graph representations. CORTEX is also a computational framework designed to support early forms of intelligence in human interacting robots by selecting, a priori, a functional decomposition of the capabilities of the robot. This set of abili...

The generation of semantic environment representations is still an open problem in robotics. Most of the current proposals are based on metric representations, and incorporate semantic information in a supervised fashion. The purpose of the robot is key in the generation of these representations, which has traditionally reduced the inter-usability...

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict ac...

The semantic segmentation problem has been widely studied in the computer vision community. However, state-of-the-art solutions based on deep learning are only available for 2D images. The lack of large annotated datasets makes more difficult the training of models with 3D images. In this work we propose to use the already available 2D deep learnin...

The goal of the LifeBots project is the study and development of long-life mechanisms that facilitate and improve the integration of robotics platforms in smart homes to support elder and handicapped people. Specifically the system aims to design, build and validate an assistive ecosystem formed by a person living in a smart home with a social robo...

The small-conductance, Ca2+-activated K+ (SK) channel subtype SK2 regulates the spike rate and firing frequency, as well as Ca2+ transients in Purkinje cells (PCs). To understand the molecular basis by which SK2 channels mediate these functions, we analyzed the exact location and densities of SK2 channels along the neuronal surface of the mouse cer...

Despite the outstanding results of Convolutional Neural Networks (CNNs) in object recognition and classification, there are still some open problems to address when applying these solutions to real-world problems. Specifically, CNNs struggle to generalize under challenging scenarios, like recognizing the variability and heterogeneity of the instanc...

The task of identifying the semantic localization of a robot has commonly been treated as a classification problem, where images are taken as input and a set of predefined labels is the output. While traditional approaches have focused on the performance of the image features extracted from computer vision techniques, the contextual information tha...

Indoor scene classification is usually approached from a computer vision perspective. However, in some fields like robotics, additional constraints must be taken into account. Specifically, in systems with low resources, state-of-the-art techniques (CNNs) cannot be successfully deployed. In this paper, we try to close this gap between theoretical a...

Finding an appropriate environment representation is a crucial problem in robotics. 3D data has been recently used thanks to the advent of low cost RGB-D cameras. We propose a new way to represent a 3D map based on the information provided by an expert. Namely, the expert is the output of a Convolutional Neural Network trained with deep learning te...

The Robotics Cognitive Architecture CORTEX is introduced along with two use cases where itis being applied.

Any robot should be provided with a proper representation of its environment in order to perform navigation and other tasks. In addition to metrical approaches, topological mapping generates graph representations in which nodes and edges correspond to locations and transitions. In this article, we present LexToMap, a topological mapping procedure t...

The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focus...

This work presents a methodology to apply Bayesian networks classifiers (BNCs) to the problem of semantic localization in robotics. This task consists of determining where the robot is located by using semantic annotations instead of metric locations, and based on robots perceptions, namely images. The proposal covers the two key steps of (1) extra...

Finding an appropriate image representation is a crucial problem in robotics. This problem has been classically addressed by means of computer vision techniques, where local and global features are used. The selection or/and combination of different features is carried out taking into account repeatability and distinctiveness, but also the specific...

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensi...

In several computer science degrees there are subjects related to computer vision or robotics. Although those subjects are usually related to industrial engineers, those areas are closely related to computer science, i.e., everyday more and more computer science students begin a master's degree or PhD in robotics. This paper could serve as a guided...

In 2015, a new degree has been implemented at University of Alicante, degree of robotics. It is the first degree about robotics in Spain and one of the first degrees in the world. Although there are several similar degrees related with robotics, this one consists in a mixture between industrial engineering and computer science. The implementation o...

Traditionally, the indoor scene classification problem has been approached from a 2D image recognition point of view. In most visual scene classification systems, a descriptor for the input image is generated to obtain a suitable representation that includes features related to color, shape or spatial information. Techniques based on the use of a s...

The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories. The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor. In this paper we propose...

The semantic localization problem in robotics consists in determining the place where a robot is located by means of semantic categories. The problem is usually addressed as a supervised classification process, where input data correspond to robot perceptions while classes to semantic categories, like kitchen or corridor. In this paper we propose a...

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict ac...

This article describes the Robot Vision challenge, a competition that evaluates solutions for the visual place classification problem. Since its origin, this challenge has been proposed as a common benchmark where worldwide proposals are measured using a common overall score. Each new edition of the competition introduced novelties, both for the t...

In this article we describe a semantic localization dataset for indoor environments named ViDRILO. The dataset provides five sequences of frames acquired with a mobile robot in two similar office buildings under different lighting conditions. Each frame consists of a point cloud representation of the scene and a perspective image. The frames in the...

Object categorization from robot perceptions has become one of the most well-known problems in robotics. How to select proper representations for these perceptions, specially when using RGB-D images, has received a significant attention in the last years. We present in this paper an object categorization approach from RGB-D images. This approach is...

Visual descriptors are widely used in several recognition and classification tasks in robotics. The main challenge for these tasks is to find a descriptor that could represent the image content without losing representative information of the image. Nowadays, there exists a wide range of visual descriptors computed with computer vision techniques a...

Detection of keypoints in an image is a crucial step in most registration and recognition tasks. The information encoded in RGB-D images can be redundant and, usually, only specific areas in the image are useful for the classification process. The process of identifying those relevant areas is known as keypoint detection. The use of keypoints can f...

Semantic localization describes the surrounding of a robot by using semantic labels. These labels are used to identify neighboring objects , but also the category of the place where the robot is located. Mul-timodal human-robot interaction refers to the communication between humans and robots by means of several information sources. This paper pres...

This paper describes a robotics cognitive architecture for social robots named CORTEX. This architecture integrates different levels of abstraction (from basic geometry to high-level predicates) into a unique Deep Space Representation (DSR) that different agents interface. These agents update the contents of the DSR with new data from the outer wor...

This paper describes the genesis of Gualzru, a robot commissioned by a large Spanish technological company to provide advertisement services in open public spaces. Gualzru has to stand by at an interactive panel observing the people passing by and, at some point, select a promising candidate and approach her to initiate a conversation. After a smal...

Nowdays it is very common the use of Open Source Software in different fields in society. For example, OpenOffice is widely used instead its proprietary competitor to write documents, prepare presentations, spreedsheets, etc. Evidently, Linux operating systems has a great acceptance in academic and industry. Other OSS packages widely used are, for...

Over the past decades, the number of robots deployed in museums, trade shows and exhibitions have grown steadily. This new application domain has become a key research topic in the robotics community. Therefore, new robots are designed to interact with people in these domains, using natural and intuitive channels. Visual perception and speech proce...

Herein we report a method supported by a two-level Naive Bayes classifier to help and improve the automatic detection and counting of cells overexpressing GFP-chimeric proteins. This toll is frequently used as a reporter for the localization and the distribution pattern of a protein in a cell. This approximation requires, besides confocal microscop...

This paper presents an overview of the ImageCLEF 2014 evaluation lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and medi...

This paper introduces a taxonomy of vision systems for ground mobile robots. In the last five years, a significant number of relevant papers have contributed to this subject. Firstly, a thorough review of the papers is proposed to discuss and classify both past and the most current approaches in the field. As a result, a global picture of the state...

This paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and...

Developing a simple multimodal interaction game with a 31 dof's mobile manipulator can become a challenging enterprise. A conceptually simple task quickly unfolds into a rather complex ensemble of driver-oriented, framework-based, software-enabled, state-machine controlled mechatronics. In this paper we propose a multimodal interaction game designe...

This article describes the RobotVision@ImageCLEF 2013 challenge, which addresses two problems: place classification and object recognition. Participants of the challenge were asked to classify rooms on the basis of image sequences captured by cameras mounted on a mobile robot. They were also asked to detect the appearance or lack of several objects...

The objective of this article is reporting the participation of the SIMD-IDIAP group in the RobotVision@ImageCLEF 2012 challenge. This challenge addresses the problem of multimodal place classification, and the 2012 edition has been organized by the members of the SIMDIDIAP team. One of the main novelties in the 2012 edition of the task has been th...

There exist a wide number of works in the literature related to new systems devoted to manage thermal control in buildings. Commonly, their evaluation is performed by using simulation of users and environmental conditions. Thus, in this work we choose a successful thermal-comfort system, formerly evaluated with simulations, and evaluate it by using...

The primary goal of this dissertation is to use machine learning and pattern recognition techniques to generate robot behaviour. This thesis presents different contributions in the fields of image processing for robots and self-localization. Robot localization is a key challenge in making truly autonomous robots. In order to localize itself, a rob...

The ability of building robust semantic space representations of environments is crucial for the development of truly autonomous robots. This task, inherently connected with cognition, is traditionally achieved by training the robot with a supervised learning phase. We argue that the design of robust and autonomous systems would greatly benefit fro...

In the last decade competitions proved to be a very efficient way of encouraging researchers to advance the state of the art in different research fields in artificial intelligence.In this paper we focus on the optional task of the RobotVision@ImageCLEF competition, which consists of a visual place classification problem where images are not isolat...

The ability of building robust semantic space representations of environments is crucial for the development of truly autonomousrobots. This task, inherently connected with cognition, is traditionally achieved by training the robot with a supervised learning phase. We argue that the design of robust and autonomous systems would greatly benefit from...

Object detection is a key point in robotics, both in localization and robot decision making. Genetic Algorithms (GAs) have proven to work well in this type of tasks, but they usually give rise to heavy computational processes. The scope of this study is the Standard Platform category of the RoboCup soccer competition, and so real-time object detect...

This article presents a new approach to mobile robot vision based on genetic algorithms. The major contribution of this proposal is the real-time adaptation of genetic algorithms, which are generally used offline. In order to achieve this goal, the execution time must be as short as possible. The scope of this system is the Standard Platform catego...

This paper presents the techniques developed by the SIMD group and the results obtained for the 2010 RobotVision task in the ImageCLEF competition. The approach presented tries to solve the problem of robot localization using only visual information. The proposed system presents a classification method using training sequences acquired under differ...

This paper describes the participation of Idiap-MULTI to the Robot Vision Task at imageCLEF 2010. Our approach was based on a discriminative classification algorithm using multiple cues. Specically, we used an SVM and combined up to four different histogram-based features with the kernel averaging method. We considered as output of the classifier,...

In this paper we present an adaptive vision system for the AIBO robots which is able to dynamically change the calibration of the colour filters implemented in the robots. This system tries to be robust by using techniques for coping with noise and hard environment characteristics, specially changing lighting conditions. This system has to be run w...

This paper presents a new approach for the classical Markov localization method for mobile robots by using image quality evaluation. Machine learning techniques have been used to obtain the quality of the images. This quality value is used to select the best information source, between odometry and sensor information. Real experiments in different...