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Miguel Oliveira, Vítor Santos, Angel D. Sappa, & Paulo Dias. (2015). Scene representations for autonomous driving: an approach based on polygonal primitives. In Iberian Robotics Conference (ROBOT 2015), Lisbon, Portugal, 2015 (Vol. 417, pp. 503–515). Springer International Publishing Switzerland 2016.
Abstract: 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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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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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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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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