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Author |
Dennis G. Romero; A. Frizera; Angel D. Sappa; Boris X. Vintimilla; T.F. Bastos |
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Title |
A predictive model for human activity recognition by observing actions and context |
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Conference Article |
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2015 |
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ACIVS 2015 (Advanced Concepts for Intelligent Vision Systems), International Conference on, Catania, Italy, 2015 |
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323 - 333 |
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Edge width, Image blu,r Defocus map, Edge model |
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This paper presents a novel model to estimate human activities – a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. |
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cidis @ cidis @ |
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43 |
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Author |
Julien Poujol; Cristhian A. Aguilera; Etienne Danos; Boris X. Vintimilla; Ricardo Toledo; Angel D. Sappa |
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Title |
A visible-Thermal Fusion based Monocular Visual Odometry |
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Conference Article |
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2015 |
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Iberian Robotics Conference (ROBOT 2015), International Conference on, Lisbon, Portugal, 2015 |
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417 |
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517-528 |
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Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion |
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The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach. |
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English |
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cidis @ cidis @ |
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44 |
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Author |
Miguel Oliveira; Vítor Santos; Angel D. Sappa; Paulo Dias |
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Title |
Scene representations for autonomous driving: an approach based on polygonal primitives |
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Conference Article |
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2015 |
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Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 |
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417 |
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503-515 |
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Scene reconstruction, Point cloud, Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Springer International Publishing Switzerland 2016 |
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Second Iberian Robotics Conference |
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cidis @ cidis @ |
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45 |
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Author |
Wilton Agila; Ricardo Cajo; Douglas Plaza |
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Title |
Experts Agents in PEM Fuel Cell Control |
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Conference Article |
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Year |
2015 |
Publication |
4ta International Conference on Renewable Energy Research and Applications |
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896 - 900 |
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s- PEM Fuel Cell; Expert Agent; Perceptive Agents; Acting Agent; Fuzzy Controller |
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In the control of the PEM (Proton Exchange Membrane) fuel cell, the existence of both deliberative and reactive processes that facilitate the tasks of control resulting from a wide range of operating scenarios and range of conditions it is required. The latter is essential to adjust its parameters to the multiplicity of circumstances that may occur in the operation of the PEM stack. In this context, the design and development of an expert-agents based architecture for autonomous control of the PEM stack in top working conditions is presented. The architecture integrates perception and control algorithms using sensory and context information. It is structured in a hierarchy of levels with different time window and level of abstraction. The monitoring model and autonomic control of PEM stack has been validated with different types of PEM stacks and operating conditions demonstrating high reliability in achieving the objective of the proposed energy efficiency. Dynamic control of the wetting of the membrane is a clear example. |
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IEEE |
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Palermo, Italy |
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English |
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English |
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2015 International Conference on Renewable Energy Research and Applications (ICRERA) |
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no |
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cidis @ cidis @ |
Serial |
46 |
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Author |
Cristhian A. Aguilera; Francisco J. Aguilera; Angel D. Sappa; Ricardo Toledo |
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Title |
Learning crossspectral similarity measures with deep convolutional neural networks |
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Conference Article |
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Year |
2016 |
Publication |
IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops |
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267-275 |
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The simultaneous use of images from different spectra can be helpful to improve the performance of many com- puter vision tasks. The core idea behind the usage of cross- spectral approaches is to take advantage of the strengths of each spectral band providing a richer representation of a scene, which cannot be obtained with just images from one spectral band. In this work we tackle the cross-spectral image similarity problem by using Convolutional Neural Networks (CNNs). We explore three different CNN archi- tectures to compare the similarity of cross-spectral image patches. Specifically, we train each network with images from the visible and the near-infrared spectrum, and then test the result with two public cross-spectral datasets. Ex- perimental results show that CNN approaches outperform the current state-of-art on both cross-spectral datasets. Ad- ditionally, our experiments show that some CNN architec- tures are capable of generalizing between different cross- spectral domains. |
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English |
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no |
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cidis @ cidis @ |
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48 |
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Author |
Miguel Realpe; Boris X. Vintimilla; Ljubo Vlacic |
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Title |
A Fault Tolerant Perception system for autonomous vehicles |
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Conference Article |
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2016 |
Publication |
35th Chinese Control Conference (CCC2016), International Conference on, Chengdu |
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1-6 |
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Fault Tolerant Perception, Sensor Data Fusion, Fault Tolerance, Autonomous Vehicles, Federated Architecture |
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Driverless vehicles are currently being tested on public roads in order to examine their ability to perform in a safe and reliable way in real world situations. However, the long-term reliable operation of a vehicle’s diverse sensors and the effects of potential sensor faults in the vehicle system have not been tested yet. This paper is proposing a sensor fusion architecture that minimizes the influence of a sensor fault. Experimental results are presented simulating faults by introducing displacements in the sensor information from the KITTI dataset. |
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English |
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cidis @ cidis @ |
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52 |
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Author |
Juan A. Carvajal; Dennis G. Romero; Angel D. Sappa |
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Title |
Fine-tuning based deep covolutional networks for lepidopterous genus recognition |
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Conference Article |
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Year |
2016 |
Publication |
XXI IberoAmerican Congress on Pattern Recognition |
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1-9 |
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This paper describes an image classi cation approach ori- ented to identify specimens of lepidopterous insects recognized at Ecuado- rian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butter ies and also to facilitate the reg- istration of unrecognized specimens. The proposed approach is based on the ne-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists, is presented|a recognition accuracy above 92% is reached. 1 Introductio |
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cidis @ cidis @ |
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53 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Colorizing Infrared Images through a Triplet Condictional DCGAN Architecture |
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Conference Article |
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2017 |
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19th International Conference on Image Analysis and Processing. |
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287-297 |
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gtsi @ user @ |
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66 |
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Byron Lima; Ricardo Cajo; Victor Huilcapi; Wilton Agila |
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Modeling and comparative study of linear and nonlinear controllers for rotary inverted pendulum |
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Conference Article |
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2017 |
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Journal of Physics: Conference Series |
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783 |
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The rotary inverted pendulum (RIP) is a problem difficult to control, several studies have been conducted where different control techniques have been applied. Literature reports that, although problem is nonlinear, classical PID controllers presents appropriate performances when applied to the system. In this paper, a comparative study of the performances of linear and nonlinear PID structures is carried out. The control algorithms are evaluated in the RIP system, using indices of performance and power consumption, which allow the categorization of control strategies according to their performance. This article also presents the modeling system, which has been estimated some of the parameters involved in the RIP system, using computer-aided design tools (CAD) and experimental methods or techniques proposed by several authors attended. The results indicate a better performance of the nonlinear controller with an increase in the robustness and faster response than the linear controller |
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gtsi @ user @ |
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69 |
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Author |
Patricia L. Suarez; Angel D. Sappa; Boris X. Vintimilla |
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Title |
Learning Image Vegetation Index through a Conditional Generative Adversarial Network |
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2017 |
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2nd IEEE Ecuador Tehcnnical Chapters Meeting (ETCM) |
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