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Cristhian A. Aguilera, Angel D. Sappa, & R. Toledo. (2015). LGHD: A feature descriptor for matching across non-linear intensity variations. In IEEE International Conference on, Quebec City, QC, 2015 (pp. 178–181). Quebec City, QC, Canada: IEEE.
Abstract: This paper presents a new feature descriptor suitable to the task of matching features points between images with nonlinear intensity variations. This includes image pairs with significant illuminations changes, multi-modal image pairs and multi-spectral image pairs. The proposed method describes the neighbourhood of feature points combining frequency and spatial information using multi-scale and multi-oriented Log- Gabor filters. Experimental results show the validity of the proposed approach and also the improvements with respect to the state of the art.
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Wilton Agila, Ricardo Cajo, & Douglas Plaza. (2015). Experts Agents in PEM Fuel Cell Control. In 4ta International Conference on Renewable Energy Research and Applications (pp. 896–900). Palermo, Italy: IEEE.
Abstract: 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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A. Amato, F. Lumbreras, & Angel D. Sappa. (2014). A general-purpose crowdsourcing platform for mobile devices. In Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014 (Vol. 3, pp. 211–215). Lisbon, Portugal: IEEE.
Abstract: This paper presents details of a general purpose micro-taskon-demand platform based on the crowdsourcing philosophy. This platformwas specifically developed for mobile devices in order to exploit the strengths of such devices; namely: i) massivity, ii) ubiquityand iii) embedded sensors.The combined use of mobile platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks.Users experience is the highlighted feature of this platform (this fact is extended to both task-proposer and task- solver).Proper tools according with a specific task are provided to a task-solver in order to perform his/her job in a simpler, faster and appealing way.Moreover, a task can be easily submitted by just selecting predefined templates, which cover a wide range of possible applications.Examples of its usage in computer vision and computer games are provided illustrating the potentiality of the platform.
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M. Oliveira, L. Seabra Lopes, G. Hyun Lim, S. Hamidreza Kasaei, Angel D. Sappa, & A. Tomé. (2015). Concurrent Learning of Visual Codebooks and Object Categories in Open- ended Domains. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, Hamburg, Germany, 2015 (pp. 2488–2495). Hamburg, Germany: IEEE.
Abstract: In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are usually constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using codebooks constructed offline.
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Arias Alexandra. Ing., Peña Roxanna. Ing., Chávez Patricia. MSEE., & Basurto Juan. Ing. (2011). Análisis Comparativo de la Implementación de una PBX de Código Abierto instalada en un Servidor Tradicional y en un Enrutador Inalámbrico en términos de Calidad de Servicio en Redes Inalámbricas Amalladas. Revista Tecnologica ESPOL RTE, Vol. 24, pp. 1–6.
Abstract: El presente trabajo compara dos Implementaciones de Centrales Telefónicas VoIP de Código Abierto implementados sobre una Red Inalámbrica Amallada. El primero comprende la instalación de la PBX en un servidor tradicional y el segundo la instalación de una PBX en un enrutador inalámbrico. Nuestro objetivo es
determinar cuál de estos dos sistemas es superior en cuanto a calidad de servicio se refiere. Para determinar la mejor solución, realizamos un estudio técnico de los paquetes capturados durante diferentes pruebas, considerando parámetros como el ancho de banda, retardo y jitter. Nuestros métodos de análisis pueden ser utilizados para futuros trabajos con una mayor complejidad y número de enrutadores inalámbrico, así como establecer el grado de afectación y el comportamiento de las dos PBX cuando haya congestión en la red.
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Mildred Cruz, Cristhian A. Aguilera, Boris X. Vintimilla, Ricardo Toledo, & Ángel D. Sappa. (2015). Cross-spectral image registration and fusion: an evaluation study. In 2nd International Conference on Machine Vision and Machine Learning (Vol. 331). Barcelona, Spain: Computer Vision Center.
Abstract: This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented.
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Dennis G. Romero, A. F. Neto, T. F. Bastos, & Boris X. Vintimilla. (2012). An approach to automatic assistance in physiotherapy based on on-line movement identification. In VI Andean Region International Conference – ANDESCON 2012. Andean Region International Conference (ANDESCON), 2012 VI: IEEE.
Abstract: This paper describes a method for on-line movement identification, oriented to patient’s movement evaluation during physiotherapy. An analysis based on Mahalanobis distance between temporal windows is performed to identify the “idle/motion” state, which defines the beginning and end of the patient’s movement, for posterior patterns extraction based on Relative Wavelet Energy from sequences of invariant moments.
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Carlos Monsalve, Alain April, & Alain Abran. (2011). Requirements Elicitation Using BPM Notations: Focusing on the Strategic Level Representation. In 10th WSEAS international conference on Applied computer and applied computational science (pp. 235–241). 1100 rue Notre-Dame Ouest, Montréal, Québec H3C 1K3 CANADA.
Abstract: Business process models (BPM) can be useful for requirements elicitation, among other uses. Since the active participation of all stakeholders is a key factor for successful requirements engineering, it is important that BPM be shared by all stakeholders. Unfortunately, organizations may end up with inconsistent BPM not covering all stakeholders’ needs and constraints. The use of multiple levels of abstraction (MLA), such as at the strategic, tactical and operational levels, is often used in various process-oriented initiatives to facilitate the consolidation of various stakeholders’ needs and constraints. This article surveys the use of MLA in recent BPM research publications and reports on a BPM action-research case study conducted in a Canadian organization, with the aim of exploring the usefulness of the strategic level.
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Roberto Jacome Galarza, Miguel-Andrés Realpe-Robalino, Chamba-Eras LuisAntonio, & Viñán-Ludeña MarlonSantiago and Sinche-Freire Javier-Francisco. (2019). Computer vision for image understanding. A comprehensive review. In International Conference on Advances in Emerging Trends and Technologies (ICAETT 2019); Quito, Ecuador (pp. 248–259).
Abstract: Computer Vision has its own Turing test: Can a machine describe the contents of an image or a video in the way a human being would do? In this paper, the progress of Deep Learning for image recognition is analyzed in order to know the answer to this question. In recent years, Deep Learning has increased considerably the precision rate of many tasks related to computer vision. Many datasets of labeled images are now available online, which leads to pre-trained models for many computer vision applications. In this work, we gather information of the latest techniques to perform image understanding and description. As a conclusion we obtained that the combination of Natural Language Processing (using Recurrent Neural Networks and Long Short-Term Memory) plus Image Understanding (using Convolutional Neural Networks) could bring new types of powerful and useful applications in which the computer will be able to answer questions about the content of images and videos. In order to build datasets of labeled images, we need a lot of work and most of the datasets are built using crowd work. These new applications have the potential to increase the human machine interaction to new levels of usability and user’s satisfaction.
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Cristhian A. Aguilera, Cristhian Aguilera, & Angel D. Sappa. (2018). Melamine faced panels defect classification beyond the visible spectrum. In Sensors 2018, Vol. 11(Issue 11).
Abstract: In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond
the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution.
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