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Dr. Jorge Azorin-Lopez
University of Alicante

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0 Computational Intelligence
0 Computer Vision
0 Image Registration
0 neural networks & deep learning
0 3D Computer Vision

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Computer Vision
neural networks & deep learning
Computational Intelligence
3D Computer Vision

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Journal article
Published: 25 June 2021 in Journal of Computational Science
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Nowadays, the emergence of large-scale and highly distributed cyber-physical systems (CPSs) in applications including Internet of things (IoT), cloud computing, mobility, Big Data, and sensor networks involves that architecture models have to work in an open and highly dynamic world. This fact increasingly highlights the importance of designing real-time and intelligent CPSs that are capable of decision making. In this paper, an Intelligent Architecture model for CPS management (IA-CPS) based on online processing is proposed for this purpose. Specifically, it can manage simple and complex events based on a service-oriented architecture and directed by an event-driven architecture, using event processing technology as Complex Event Processing (CEP). The novelty of our approach relies on the fact that the proposed architecture is service-oriented, which models the functionalities of the event-driven system. This gives us the possibility to offer a flexible service catalog, allowing us to connect to the system on any kind of device and interact in different scenarios. The model has been applied to two use cases: processing images from video surveillance cameras, and processing of consumption data captured by water and energy sensors installed in end-user environments.

ACS Style

Henry Duque Gómez; Jose García Rodríguez; Jorge Azorin-Lopez; David Tomás; Andres Fuster-Guillo; Higinio Mora-Mora. IA-CPS: Intelligent architecture for cyber-physical systems management. Journal of Computational Science 2021, 53, 101409 .

AMA Style

Henry Duque Gómez, Jose García Rodríguez, Jorge Azorin-Lopez, David Tomás, Andres Fuster-Guillo, Higinio Mora-Mora. IA-CPS: Intelligent architecture for cyber-physical systems management. Journal of Computational Science. 2021; 53 ():101409.

Chicago/Turabian Style

Henry Duque Gómez; Jose García Rodríguez; Jorge Azorin-Lopez; David Tomás; Andres Fuster-Guillo; Higinio Mora-Mora. 2021. "IA-CPS: Intelligent architecture for cyber-physical systems management." Journal of Computational Science 53, no. : 101409.

Journal article
Published: 21 May 2021 in Computers in Industry
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Product assembly is a crucial process in manufacturing plants. In Industry 4.0, the offer of mass-customized products is expanded, thereby increasing the complexity of the assembling phase. This implies that operators should pay close attention to small details, potentially resulting in errors during the manufacturing process owing to its high level of complexity. To mitigate this, we propose a novel architecture that evaluates the activities of an operator during manual assembly in a production cell so that errors in the manufacturing process can be identified, thus avoiding low quality in the final product and reducing rework and waste of raw materials or time. To perform this assessment, it is necessary to use state-of-the-art computer vision techniques, such as deep learning, so that tools, components, and actions may be identified by visual control systems. We develop a deep-learning-based visual control assembly assistant that enables real-time evaluation of the activities in the assembly process so that errors can be identified. A general-use language is developed to describe the actions in assembly processes, which can also be used independently of the proposed architecture. Finally, we generate two datasets with annotated data to be fed to the deep learning methods, the first for the recognition of tools and accessories and the second for the identification of basic actions in manufacturing processes. To validate the proposed method, a set of experiments are conducted, and high accuracy is obtained.

ACS Style

Mauricio-Andrés Zamora-Hernández; John Alejandro Castro-Vargas; Jorge Azorin-Lopez; Jose Garcia-Rodriguez. Deep learning-based visual control assistant for assembly in Industry 4.0. Computers in Industry 2021, 131, 103485 .

AMA Style

Mauricio-Andrés Zamora-Hernández, John Alejandro Castro-Vargas, Jorge Azorin-Lopez, Jose Garcia-Rodriguez. Deep learning-based visual control assistant for assembly in Industry 4.0. Computers in Industry. 2021; 131 ():103485.

Chicago/Turabian Style

Mauricio-Andrés Zamora-Hernández; John Alejandro Castro-Vargas; Jorge Azorin-Lopez; Jose Garcia-Rodriguez. 2021. "Deep learning-based visual control assistant for assembly in Industry 4.0." Computers in Industry 131, no. : 103485.

Journal article
Published: 15 November 2020 in Sustainability
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Cultural identity is a complex concept that includes subjective factors such as ideology, family knowledge, customs, language, and acquired skills, among others. Measuring culture involves a significant level of difficulty, since its study and scope differ from the point of view, the time and the place where the studies are carried out. In the Amazon, indigenous communities are in an accelerated process of acculturation that results in a loss of cultural identity that is not easy to quantify. This paper presents a method to measure the cultural distance between individuals or between groups of people using Artificial Intelligence techniques. The distance between individuals is calculated as the distance of the minimum path in the self-organizing map using Dijkstra’s algorithm. The experiments have been carried out to measure the cultural identity of indigenous people in the Waorani Amazon community and compares them with people living in cities who have a modern identity. The results showed that the communities are still distant in terms of identity from the westernised cities around them, although there are already factors where the distances are minimal concerning these cities. In any case, the method makes it possible to quantify the state of acculturation. This quantification can help the authorities to monitor these communities and take political decisions that will enable them to preserve their cultural identity.

ACS Style

Aldrin Espín-León; Antonio Jimeno-Morenilla; María Pertegal-Felices; Jorge Azorín-López. Cultural Identity Distance Computation through Artificial Intelligence as an Analysis Tool of the Amazon Indigenous People. A Case Study in the Waorani Community. Sustainability 2020, 12, 9513 .

AMA Style

Aldrin Espín-León, Antonio Jimeno-Morenilla, María Pertegal-Felices, Jorge Azorín-López. Cultural Identity Distance Computation through Artificial Intelligence as an Analysis Tool of the Amazon Indigenous People. A Case Study in the Waorani Community. Sustainability. 2020; 12 (22):9513.

Chicago/Turabian Style

Aldrin Espín-León; Antonio Jimeno-Morenilla; María Pertegal-Felices; Jorge Azorín-López. 2020. "Cultural Identity Distance Computation through Artificial Intelligence as an Analysis Tool of the Amazon Indigenous People. A Case Study in the Waorani Community." Sustainability 12, no. 22: 9513.

Review
Published: 26 October 2020 in Applied Sciences
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This paper reviews recent deep learning-based registration methods. Registration is the process that computes the transformation that aligns datasets, and the accuracy of the result depends on multiple factors. The most significant factors are the size of input data; the presence of noise, outliers and occlusions; the quality of the extracted features; real-time requirements; and the type of transformation, especially those defined by multiple parameters, such as non-rigid deformations. Deep Registration Networks (DRNs) are those architectures trying to solve the alignment task using a learning algorithm. In this review, we classify these methods according to a proposed framework based on the traditional registration pipeline. This pipeline consists of four steps: target selection, feature extraction, feature matching, and transform computation for the alignment. This new paradigm introduces a higher-level understanding of registration, which makes explicit the challenging problems of traditional approaches. The main contribution of this work is to provide a comprehensive starting point to address registration problems from a learning-based perspective and to understand the new range of possibilities.

ACS Style

Victor Villena-Martinez; Sergiu Oprea; Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Robert B. Fisher. When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs). Applied Sciences 2020, 10, 7524 .

AMA Style

Victor Villena-Martinez, Sergiu Oprea, Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Robert B. Fisher. When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs). Applied Sciences. 2020; 10 (21):7524.

Chicago/Turabian Style

Victor Villena-Martinez; Sergiu Oprea; Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Robert B. Fisher. 2020. "When Deep Learning Meets Data Alignment: A Review on Deep Registration Networks (DRNs)." Applied Sciences 10, no. 21: 7524.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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Given the application domains and challenges presented to cyber-physical systems (CPSs), it is necessary to design a CPS system able to deal with temporal constrains. There are various software architecture models to meet this challenge. Models have been developed under three types of software structural units, such as: Component-based architecture, service-based architecture and agent-based architecture. These models are analyzed under the compliance of adaptability, autonomy and interoperability properties. Our proposal provides a conceptual architecture model driven by events for the real-time management of CPS, which is proposed under the rigor of software engineering based on a service-oriented architecture (SOA 2.0 - Service Oriented Architecture) and directed by EDA events (Event Driven Architecture), using event processing technology CEP (Complex Event Processing).

ACS Style

Henry Duque Gómez; Jose García Rodríguez; Jorge Azorin-Lopez. Event-Based Conceptual Architecture for the Management of Cyber-Physical Systems Tasks in Real Time. Advances in Intelligent Systems and Computing 2020, 731 -740.

AMA Style

Henry Duque Gómez, Jose García Rodríguez, Jorge Azorin-Lopez. Event-Based Conceptual Architecture for the Management of Cyber-Physical Systems Tasks in Real Time. Advances in Intelligent Systems and Computing. 2020; ():731-740.

Chicago/Turabian Style

Henry Duque Gómez; Jose García Rodríguez; Jorge Azorin-Lopez. 2020. "Event-Based Conceptual Architecture for the Management of Cyber-Physical Systems Tasks in Real Time." Advances in Intelligent Systems and Computing , no. : 731-740.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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Currently, the automation of activity recognition of a group of people in closed and open environments is a major problem, especially in video surveillance. It is becoming increasingly important to have computer vision architectures that allow automatic recognition of group activities to make decisions. This paper proposes a computer vision architecture capable of learning and recognizing abnormal group activities using the movements of the group in the scene. It is based on the Activity Description Vector, a descriptor capable of representing the trajectory information of a sequence of images as a collection of local movements that occur in specific regions of the scene. The proposal is based on the evolution of different versions of this descriptor towards the generation of images that will be input of a two-stream classifier capable of robustly classifying abnormal group activities. Moreover, it includes context information to provide extra information to classify the activities including it as the third stream of the classifier resulting in a robust architecture for one class classification problems. The architecture has been evaluated and compared with other approaches using Ped 1 and Ped 2 datasets, obtaining a high performance in abnormal group activity recognition.

ACS Style

Luis Felipe Borja-Borja; Jorge Azorín-López; Marcelo Saval-Calvo. A Deep Learning Architecture for Recognizing Abnormal Activities of Groups Using Context and Motion Information. Advances in Intelligent Systems and Computing 2020, 760 -769.

AMA Style

Luis Felipe Borja-Borja, Jorge Azorín-López, Marcelo Saval-Calvo. A Deep Learning Architecture for Recognizing Abnormal Activities of Groups Using Context and Motion Information. Advances in Intelligent Systems and Computing. 2020; ():760-769.

Chicago/Turabian Style

Luis Felipe Borja-Borja; Jorge Azorín-López; Marcelo Saval-Calvo. 2020. "A Deep Learning Architecture for Recognizing Abnormal Activities of Groups Using Context and Motion Information." Advances in Intelligent Systems and Computing , no. : 760-769.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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The use of intelligent systems to improve manufacturing processes is the basis for the development of robotic solutions in Industry 4.0. Monitoring operators manipulating tools and objects is one of the key tasks. Deep learning methods are obtaining state-of-the-art results to solve this problem but large amounts of labelled data should be provided to these networks. However, no specific manufacturing tools datasets exist. For this purpose, we proposed a new dataset for this type of environment. An hybrid dataset of 29550 images has been proposed for network training that combines real and synthetic images of tools and components commonly used in manufacturing cells. This project is part of a set of proposed modules of a solution that allows us to evaluate in real-time the execution of assembly instructions of the operators throughout the production process.

ACS Style

Mauricio-Andres Zamora-Hernandez; John Alejandro Castro-Vargas; Jorge Azorin-Lopez; Jose Garcia-Rodriguez. ToolSet: A Real-Synthetic Manufacturing Tools and Accessories Dataset. Advances in Intelligent Systems and Computing 2020, 800 -809.

AMA Style

Mauricio-Andres Zamora-Hernandez, John Alejandro Castro-Vargas, Jorge Azorin-Lopez, Jose Garcia-Rodriguez. ToolSet: A Real-Synthetic Manufacturing Tools and Accessories Dataset. Advances in Intelligent Systems and Computing. 2020; ():800-809.

Chicago/Turabian Style

Mauricio-Andres Zamora-Hernandez; John Alejandro Castro-Vargas; Jorge Azorin-Lopez; Jose Garcia-Rodriguez. 2020. "ToolSet: A Real-Synthetic Manufacturing Tools and Accessories Dataset." Advances in Intelligent Systems and Computing , no. : 800-809.

Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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In factories, the assembly of products or components by operators is a complex task that is not free of recurring problems. In this process, operators often make mistakes that can lead to defective products. Therefore, they need to be inspected later to verify their correct assembly. The main problems are caused by several reasons, including high employee turnover due to a lack of experience in manufacturing specific products or confusion of instructions for similar components. In this paper, a novel structured language aimed to describe the required actions to manufacture a product in industrial assembly environments is presented. The main contribution is to provide a formal language that can help in the future to an automatic system can verify through visual control, whether the actions performed by the operator are carried out in accordance with the standard described by this language. It will allow to minimize the negative impact of errors during assembly.

ACS Style

Mauricio-Andrés Zamora-Hernández; Jose Andrez Chaves Ceciliano; Alonso Villalobos Granados; John Alejandro Castro Vargas; Jose Garcia-Rodriguez; Jorge Azorín-López. Manufacturing Description Language for Process Control in Industry 4.0. Advances in Intelligent Systems and Computing 2020, 790 -799.

AMA Style

Mauricio-Andrés Zamora-Hernández, Jose Andrez Chaves Ceciliano, Alonso Villalobos Granados, John Alejandro Castro Vargas, Jose Garcia-Rodriguez, Jorge Azorín-López. Manufacturing Description Language for Process Control in Industry 4.0. Advances in Intelligent Systems and Computing. 2020; ():790-799.

Chicago/Turabian Style

Mauricio-Andrés Zamora-Hernández; Jose Andrez Chaves Ceciliano; Alonso Villalobos Granados; John Alejandro Castro Vargas; Jose Garcia-Rodriguez; Jorge Azorín-López. 2020. "Manufacturing Description Language for Process Control in Industry 4.0." Advances in Intelligent Systems and Computing , no. : 790-799.

Review
Published: 13 August 2020 in Applied Sciences
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Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, The difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.

ACS Style

Daniel Cortés; David Gil; Jorge Azorín; Florian Vandecasteele; Steven Verstockt. A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires. Applied Sciences 2020, 10, 5609 .

AMA Style

Daniel Cortés, David Gil, Jorge Azorín, Florian Vandecasteele, Steven Verstockt. A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires. Applied Sciences. 2020; 10 (16):5609.

Chicago/Turabian Style

Daniel Cortés; David Gil; Jorge Azorín; Florian Vandecasteele; Steven Verstockt. 2020. "A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires." Applied Sciences 10, no. 16: 5609.

Journal article
Published: 01 July 2020 in Sensors
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This research aims to improve dietetic-nutritional treatment using state-of-the-art RGB-D sensors and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved using multimedia technologies. However, there are few studies using 3D data and VR technologies for this purpose. On the other hand, obtaining 3D measurements of the human body and analyzing them over time (4D) in patients undergoing dietary treatment is a challenging field. The main contribution of the work is to provide a framework to study the effect of 4D body model visualization on adherence to obesity treatment. The system can obtain a complete 3D model of a body using low-cost technology, allowing future straightforward transference with sufficient accuracy and realistic visualization, enabling the analysis of the evolution (4D) of the shape during the treatment of obesity. The 3D body models will be used for studying the effect of visualization on adherence to obesity treatment using 2D and VR devices. Moreover, we will use the acquired 3D models to obtain measurements of the body. An analysis of the accuracy of the proposed methods for obtaining measurements with both synthetic and real objects has been carried out.

ACS Style

Andrés Fuster-Guilló; Jorge Azorín-López; Marcelo Saval-Calvo; Juan Miguel Castillo-Zaragoza; Nahuel Garcia-D’Urso; Robert B. Fisher. RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments. Sensors 2020, 20, 3690 .

AMA Style

Andrés Fuster-Guilló, Jorge Azorín-López, Marcelo Saval-Calvo, Juan Miguel Castillo-Zaragoza, Nahuel Garcia-D’Urso, Robert B. Fisher. RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments. Sensors. 2020; 20 (13):3690.

Chicago/Turabian Style

Andrés Fuster-Guilló; Jorge Azorín-López; Marcelo Saval-Calvo; Juan Miguel Castillo-Zaragoza; Nahuel Garcia-D’Urso; Robert B. Fisher. 2020. "RGB-D-Based Framework to Acquire, Visualize and Measure the Human Body for Dietetic Treatments." Sensors 20, no. 13: 3690.

Proceedings
Published: 01 January 2019 in Proceedings
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This research aims to improve adherence to dietetic-nutritional treatment using state-of-the-art RGB-D sensor and virtual reality (VR) technology. Recent studies show that adherence to treatment can be improved by using multimedia technologies which impact on the body awareness of patients. However, there are no studies published to date using 3D data and VR technologies for this purpose. This paper describes a system capable of obtaining the complete 3D model of a body with high accuracy and a realistic visualization for 2D and VR devices to be used for studying the effect of 3D technologies on adherence to obesity treatment.

ACS Style

Andrés Fuster-Guilló; Jorge Azorín-López; Juan Miguel Castillo Zaragoza; Luis Fernando Pérez Pérez; Marcelo Saval-Calvo; Robert B. Fisher. 3D Technologies to Acquire and Visualize the Human Body for Improving Dietetic Treatment. Proceedings 2019, 31, 53 .

AMA Style

Andrés Fuster-Guilló, Jorge Azorín-López, Juan Miguel Castillo Zaragoza, Luis Fernando Pérez Pérez, Marcelo Saval-Calvo, Robert B. Fisher. 3D Technologies to Acquire and Visualize the Human Body for Improving Dietetic Treatment. Proceedings. 2019; 31 (1):53.

Chicago/Turabian Style

Andrés Fuster-Guilló; Jorge Azorín-López; Juan Miguel Castillo Zaragoza; Luis Fernando Pérez Pérez; Marcelo Saval-Calvo; Robert B. Fisher. 2019. "3D Technologies to Acquire and Visualize the Human Body for Improving Dietetic Treatment." Proceedings 31, no. 1: 53.

Proceedings
Published: 01 January 2019 in Proceedings
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There is a huge demand for new techniques and technologies to tackle life-threatening in fire emergencies. Enclosure fires are a type of emergency involving firefighters whose lives are sometimes put at risk. In any confined fire, the emergency team may encounter two types of combustion environments, ventilated or under-ventilated. The rapidly changing behaviour of this scenario depends on multiple factors such as enclosure size, ventilation, or type and quantity of fuel involved. However, the difficulty of handling this situation coupled with the potential for human error, even if there is undivided attention to the task in hand, remains an unresolved challenge for firefighters today. New technologies based in Thermal Imaging Cameras can help firefighters to prevent this situation. Fire Science Living Lab is presented as a solution to test new technologies for this area. This papers shows a resume about the process to develop a prediction system in a living lab for one of the most dangerous situation for firefighters, flashover.

ACS Style

Daniel Cortés; David Gil; Jorge Azorín. Fire Science Living Lab for Flashover Prediction. Proceedings 2019, 31, 87 .

AMA Style

Daniel Cortés, David Gil, Jorge Azorín. Fire Science Living Lab for Flashover Prediction. Proceedings. 2019; 31 (1):87.

Chicago/Turabian Style

Daniel Cortés; David Gil; Jorge Azorín. 2019. "Fire Science Living Lab for Flashover Prediction." Proceedings 31, no. 1: 87.

Journal article
Published: 01 April 2018 in Computer Vision and Image Understanding
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ACS Style

Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Victor Villena-Martinez; Robert B. Fisher. 3D non-rigid registration using color: Color Coherent Point Drift. Computer Vision and Image Understanding 2018, 169, 119 -135.

AMA Style

Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Victor Villena-Martinez, Robert B. Fisher. 3D non-rigid registration using color: Color Coherent Point Drift. Computer Vision and Image Understanding. 2018; 169 ():119-135.

Chicago/Turabian Style

Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Victor Villena-Martinez; Robert B. Fisher. 2018. "3D non-rigid registration using color: Color Coherent Point Drift." Computer Vision and Image Understanding 169, no. : 119-135.

Preprint
Published: 05 February 2018
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Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method [1]). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.

ACS Style

Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Victor Villena-Martinez; Robert B. Fisher. 3D non-rigid registration using color: Color Coherent Point Drift. 2018, 1 .

AMA Style

Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Victor Villena-Martinez, Robert B. Fisher. 3D non-rigid registration using color: Color Coherent Point Drift. . 2018; ():1.

Chicago/Turabian Style

Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Victor Villena-Martinez; Robert B. Fisher. 2018. "3D non-rigid registration using color: Color Coherent Point Drift." , no. : 1.

Chapter
Published: 01 January 2018 in Computer Vision
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In the creation of new industries, products and services -- all of which are advances of the Fourth Industrial Revolution -- the human-robot interaction that includes automatic learning and computer vision are elements to consider since they promote collaborative environments between people and robots. The use of machine learning and computer vision provides the tools needed to increase productivity and minimizes delivery reaction times by assisting in the optimization of complex production planning processes. This review of the state of the art presents the main trends that seek to improve human-robot interaction in productive environments, and identifies challenges in research as well as in industrial - technological development in this topic. In addition, this review offers a proposal on the needs of use of artificial intelligence in all processes of industry 4.0 as a crucial linking element among humans, robots, intelligent and traditional machines; as well as a mechanism for quality control and occupational safety.

ACS Style

Mauricio Andres Zamora Hernandez; Eldon Glen Caldwell Marin; José García-Rodríguez; Jorge Azorin-Lopez; Miguel Cazorla. Automatic Learning Improves Human-Robot Interaction in Productive Environments. Computer Vision 2018, 2014 -2024.

AMA Style

Mauricio Andres Zamora Hernandez, Eldon Glen Caldwell Marin, José García-Rodríguez, Jorge Azorin-Lopez, Miguel Cazorla. Automatic Learning Improves Human-Robot Interaction in Productive Environments. Computer Vision. 2018; ():2014-2024.

Chicago/Turabian Style

Mauricio Andres Zamora Hernandez; Eldon Glen Caldwell Marin; José García-Rodríguez; Jorge Azorin-Lopez; Miguel Cazorla. 2018. "Automatic Learning Improves Human-Robot Interaction in Productive Environments." Computer Vision , no. : 2014-2024.

Journal article
Published: 10 October 2017 in Sensors
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The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

ACS Style

Higinio Mora; David Gil; Rafael Muñoz Terol; Jorge Azorin-Lopez; Julian Szymanski. An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments. Sensors 2017, 17, 2302 .

AMA Style

Higinio Mora, David Gil, Rafael Muñoz Terol, Jorge Azorin-Lopez, Julian Szymanski. An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments. Sensors. 2017; 17 (10):2302.

Chicago/Turabian Style

Higinio Mora; David Gil; Rafael Muñoz Terol; Jorge Azorin-Lopez; Julian Szymanski. 2017. "An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments." Sensors 17, no. 10: 2302.

Preprint
Published: 04 August 2017
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Many applications including object reconstruction, robot guidance, and scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown an it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the Signal-to-Noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, {\mu}-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The ...

ACS Style

Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Higinio Mora-Mora. ?-MAR: Multiplane 3D Marker based Registration for Depth-sensing Cameras. 2017, 1 .

AMA Style

Marcelo Saval-Calvo, Jorge Azorin-Lopez, Andres Fuster-Guillo, Higinio Mora-Mora. ?-MAR: Multiplane 3D Marker based Registration for Depth-sensing Cameras. . 2017; ():1.

Chicago/Turabian Style

Marcelo Saval-Calvo; Jorge Azorin-Lopez; Andres Fuster-Guillo; Higinio Mora-Mora. 2017. "?-MAR: Multiplane 3D Marker based Registration for Depth-sensing Cameras." , no. : 1.

Journal article
Published: 01 July 2017 in International Journal of Computer Vision and Image Processing
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In this paper, the problem of 3D body registration using a single RGB-D sensor is approached. It has been guided by three main requirements: low-cost, unconstrained movement and accuracy. In order to fit them, an iterative registration method for accurately aligning data from single RGB-D sensor is proposed. The data is acquired while a person rotates in front of the camera, without the need of any external marker or constraint about its pose. The articulated alignment is carried out in a model-free approach in order to be more consistent with the real data. The iterative method is divided in stages, contributing to each other by the refinement of a specific part of the acquired data. The exploratory results validate the proposed method that is able to feed on itself in each iteration improving the final result by a progressive iteration, with the required precision under the conditions of affordability and unconstrained movement acquisition.

ACS Style

Victor Villena-Martinez; Andres Fuster-Guillo; Marcelo Saval-Calvo; Jorge Azorin-Lopez. An Iterative Method for 3D Body Registration Using a Single RGB-D Sensor. International Journal of Computer Vision and Image Processing 2017, 7, 26 -39.

AMA Style

Victor Villena-Martinez, Andres Fuster-Guillo, Marcelo Saval-Calvo, Jorge Azorin-Lopez. An Iterative Method for 3D Body Registration Using a Single RGB-D Sensor. International Journal of Computer Vision and Image Processing. 2017; 7 (3):26-39.

Chicago/Turabian Style

Victor Villena-Martinez; Andres Fuster-Guillo; Marcelo Saval-Calvo; Jorge Azorin-Lopez. 2017. "An Iterative Method for 3D Body Registration Using a Single RGB-D Sensor." International Journal of Computer Vision and Image Processing 7, no. 3: 26-39.

Journal article
Published: 01 July 2017 in International Journal of Computer Vision and Image Processing
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The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

ACS Style

Luis Felipe Borja; Jorge Azorin-Lopez; Marcelo Saval-Calvo. A Compilation of Methods and Datasets for Group and Crowd Action Recognition. International Journal of Computer Vision and Image Processing 2017, 7, 40 -53.

AMA Style

Luis Felipe Borja, Jorge Azorin-Lopez, Marcelo Saval-Calvo. A Compilation of Methods and Datasets for Group and Crowd Action Recognition. International Journal of Computer Vision and Image Processing. 2017; 7 (3):40-53.

Chicago/Turabian Style

Luis Felipe Borja; Jorge Azorin-Lopez; Marcelo Saval-Calvo. 2017. "A Compilation of Methods and Datasets for Group and Crowd Action Recognition." International Journal of Computer Vision and Image Processing 7, no. 3: 40-53.

Journal article
Published: 22 June 2017 in Sensors
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The use of visual information is a very well known input from different kinds of sensors. However, most of the perception problems are individually modeled and tackled. It is necessary to provide a general imaging model that allows us to parametrize different input systems as well as their problems and possible solutions. In this paper, we present an active vision model considering the imaging system as a whole (including camera, lighting system, object to be perceived) in order to propose solutions to automated visual systems that present problems that we perceive. As a concrete case study, we instantiate the model in a real application and still challenging problem: automated visual inspection. It is one of the most used quality control systems to detect defects on manufactured objects. However, it presents problems for specular products. We model these perception problems taking into account environmental conditions and camera parameters that allow a system to properly perceive the specific object characteristics to determine defects on surfaces. The validation of the model has been carried out using simulations providing an efficient way to perform a large set of tests (different environment conditions and camera parameters) as a previous step of experimentation in real manufacturing environments, which more complex in terms of instrumentation and more expensive. Results prove the success of the model application adjusting scale, viewpoint and lighting conditions to detect structural and color defects on specular surfaces.

ACS Style

Jorge Azorin-Lopez; Andres Fuster-Guillo; Marcelo Saval-Calvo; Higinio Mora-Mora; Juan Manuel Garcia-Chamizo. A Novel Active Imaging Model to Design Visual Systems: A Case of Inspection System for Specular Surfaces. Sensors 2017, 17, 1466 .

AMA Style

Jorge Azorin-Lopez, Andres Fuster-Guillo, Marcelo Saval-Calvo, Higinio Mora-Mora, Juan Manuel Garcia-Chamizo. A Novel Active Imaging Model to Design Visual Systems: A Case of Inspection System for Specular Surfaces. Sensors. 2017; 17 (7):1466.

Chicago/Turabian Style

Jorge Azorin-Lopez; Andres Fuster-Guillo; Marcelo Saval-Calvo; Higinio Mora-Mora; Juan Manuel Garcia-Chamizo. 2017. "A Novel Active Imaging Model to Design Visual Systems: A Case of Inspection System for Specular Surfaces." Sensors 17, no. 7: 1466.