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Acasandrei L.,Institute Microelectronica Of Seville | Barriga A.,University of Seville
Proceedings - 16th IEEE International Conference on High Performance Computing and Communications, HPCC 2014, 11th IEEE International Conference on Embedded Software and Systems, ICESS 2014 and 6th International Symposium on Cyberspace Safety and Security, CSS 2014 | Year: 2014

In computer vision during the recent years a new paradigm for object detection has stimulated researchers and designers interest. The foundation of this new paradigm is the Local Binary Pattern (LBP) which is a nonparametric operator that efficiently extracts the features of local structures in images. This communication describes a software embedded implementation of LBP based algorithm for object detection, in particular targeting frontal face detection. © 2014 IEEE. Source

Acasandrei L.,Institute Microelectronica Of Seville | Barriga A.,University of Seville
IECON Proceedings (Industrial Electronics Conference) | Year: 2013

A design methodology to accelerate the face detection for embedded systems is described, starting from algorithm optimization (high level) and ending with software and hardware codesign (low level) by addressing the issues and the design decisions made at each level based on the performance measurements and system limitations. The implemented embedded face detection system consumes very little power compared with the traditional PC software implementations while maintaining the same detection accuracy. The proposed face detection acceleration methodology is suitable for real time applications. © 2013 IEEE. Source

Pouzols F.M.,Aalto University | Lopez D.R.,RedIRIS | Barros A.B.,Institute Microelectronica Of Seville
Studies in Computational Intelligence | Year: 2011

In this chapter, we focus on long-term modeling and prediction of univariate nonlinear time series. First, a method for long-term time series prediction by means of fuzzy inference systems combined with residual variance estimation techniques is developed and validated through a number of time series prediction benchmarks. This method provides an automatic means of modeling and predicting network traffic load, and can thus be classified as a method for predictive data mining. Although the primary focus in this section is to develop a methodology for building simple and thus interpretable fuzzy inference systems, it will be shown that they also outperform some of the most accurate and commonly used techniques in the field of time series prediction. © 2011 Springer-Verlag Berlin Heidelberg. Source

Posch C.,University Pierre and Marie Curie | Serrano-Gotarredona T.,Institute Microelectronica Of Seville | Linares-Barranco B.,Institute Microelectronica Of Seville | Delbruck T.,ETH Zurich
Proceedings of the IEEE | Year: 2014

State-of-the-art image sensors suffer from significant limitations imposed by their very principle of operation. These sensors acquire the visual information as a series of 'snapshot' images, recorded at discrete points in time. Visual information gets time quantized at a predetermined frame rate which has no relation to the dynamics present in the scene. Furthermore, each recorded frame conveys the information from all pixels, regardless of whether this information, or a part of it, has changed since the last frame had been acquired. This acquisition method limits the temporal resolution, potentially missing important information, and leads to redundancy in the recorded image data, unnecessarily inflating data rate and volume. Biology is leading the way to a more efficient style of image acquisition. Biological vision systems are driven by events happening within the scene in view, and not, like image sensors, by artificially created timing and control signals. Translating the frameless paradigm of biological vision to artificial imaging systems implies that control over the acquisition of visual information is no longer being imposed externally to an array of pixels but the decision making is transferred to the single pixel that handles its own information individually. In this paper, recent developments in bioinspired, neuromorphic optical sensing and artificial vision are presented and discussed. It is suggested that bioinspired vision systems have the potential to outperform conventional, frame-based vision systems in many application fields and to establish new benchmarks in terms of redundancy suppression and data compression, dynamic range, temporal resolution, and power efficiency. Demanding vision tasks such as real-time 3-D mapping, complex multiobject tracking, or fast visual feedback loops for sensory-motor action, tasks that often pose severe, sometimes insurmountable, challenges to conventional artificial vision systems, are in reach using bioinspired vision sensing and processing techniques. © 1963-2012 IEEE. Source

Lenero-Bardallo J.A.,University of Oslo | Serrano-Gotarredona T.,Institute Microelectronica Of Seville | Linares-Barranco B.,Institute Microelectronica Of Seville
IEEE Journal of Solid-State Circuits | Year: 2011

This paper presents a 128 × 128 dynamic vision sensor. Each pixel detects temporal changes in the local illumination. A minimum illumination temporal contrast of 10% can be detected. A compact preamplification stage has been introduced that allows to improve the minimum detectable contrast over previous designs, while at the same time reducing the pixel area by 1/3. The pixel responds to illumination changes in less than 3.6 μs. The ability of the sensor to capture very fast moving objects, rotating at 10 K revolutions per second, has been verified experimentally. A frame-based sensor capable to achieve this, would require at least 100 K frames per second. © 2011 IEEE. Source

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