Special issue on real-time computer vision in smart cities

According to United Nations, more than half of the population of the Earth now lives in urban areas. The need to rethink the city in efficient and modern ways, including new actors in the scene such as automatic decision makers, has motivated plenty of national and international initiatives to foster ICT research and innovation in this field. For the European Union, the sustainable development of urban areas is a challenge of key importance: it requires new, efficient, and user-friendly technologies and services, thus becoming one of the main topics within the European Research Programme “Horizon 2020”

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

A Variable Neighbourhood Search approach to the Cutwidth Minimization Problem

The Cutwidth Minimization Problem, also known as the Minimum Cut Linear Arrangement consists of finding an arrangement of the vertices of a graph on a line, in such a way that the maximum number of edges between each pair of consecutive vertices is minimized. This problem has practical applications in VLSI Design, Network Migration and Graph Drawing, among others. In this paper we propose several heuristics based on the Variable Neighbourhood Search methodology to tackle the problem and we compare them with other state-of-the-art methods

Optical engineering

This research presents an algorithm for three-dimensional (3-D) pose tracking of a rigid object by processing sequences of monocular images. The pose trajectory of the object is estimated by performing linear correlation between the current scene and a filter bank constructed from different views of a 3-D model of the target, which are created synthetically with computer graphics. The pose tracking is guided by particle filters that dynamically adapt the filter bank by taking into account the kinematics of the target in the scene. Experimental results obtained with the proposed algorithm in processing synthetic and real images are presented and discussed. These results show that the proposed algorithm achieves a higher accuracy of pose tracking in terms ofobjective metrics, in comparison with that of existing similar algorithms.Optical Engineeringdoi: 10.1117/1.OE.57.7.07310

Local Search Particle Filter for a Video Surveillance System

This paper presents a work in progress for indoor and outdoor target detection and feature extraction in video sequences. The framework can be applied to AmI systems related to surveillance activities. The system is based on a Local Search Particle Filter (LSPF) algorithm, which tracks a moving target and calculates its bounding box. Possible applications of this prototype include assisted monitoring to supervise video sequences from different cameras, and scene analysis for domotic environments.