The MoCA Workbench: Support for Creativity in Movie Content Analysis

Semantic access to the content of a video is highly desirable for multimedia content retrieval. Automatic extraction of semantics requires content analysis algorithms. Our MoCA (Movie Content Analysis) project provides an interactive workbench supporting the researcher in the development of new movie content analysis algorithms. The workbench offers data management facilities for large amounts of video/audio data and derived parameters. It also provides an easy-to-use interface for a free combination of basic operators into more sophisticated operators. We can combine results from video track and audio track analysis. The MoCA Workbench shields the researcher from technical details and provides advanced visualization capabilities, allowing attention to focus on the development of new algorithms. The paper presents the design and implementation of the MoCA Workbench and reports practical experience

Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing

[EN] The use of remote sensing to map the distribution of plant diseases has evolved considerably over the last three decades and can be performed at different scales, depending on the area to be monitored, as well as the spatial and spectral resolution required. This work describes the development of a small low-cost field robot (Remotely Operated Vehicle for Infection Monitoring in orchards, XF-ROVIM), which is intended to be a flexible solution for early detection of Xylella fastidiosa (X. fastidiosa) in olive groves at plant to leaf level. The robot is remotely driven and fitted with different sensing equipment to capture thermal, spectral and structural information about the plants. Taking into account the height of the olive trees inspected, the design includes a platform that can raise the cameras to adapt the height of the sensors to a maximum of 200 cm. The robot was tested in an olive grove (4 ha) potentially infected by X. fastidiosa in the region of Apulia, southern Italy. The tests were focused on investigating the reliability of the mechanical and electronic solutions developed as well as the capability of the sensors to obtain accurate data. The four sides of all trees in the crop were inspected by travelling along the rows in both directions, showing that it could be easily adaptable to other crops. XF-ROVIM was capable of inspecting the whole field continuously, capturing geolocated spectral information and the structure of the trees for later comparison with the in situ observations.This work was partially supported by funding from the European Union's Horizon 2020 research and innovation programme under grant agreement 727987 Xylella Fastidiosa Active Containment Through a multidisciplinary-Oriented Research Strategy (XF-ACTORS).Rey, B.; Aleixos Borrás, MN.; Cubero-García, S.; Blasco Ivars, J. (2019). Xf-Rovim. A Field Robot to Detect Olive Trees Infected by Xylella Fastidiosa Using Proximal Sensing. Remote Sensing. 11(3). https://doi.org/10.3390/rs11030221113Martelli, G. P., Boscia, D., Porcelli, F., & Saponari, M. (2015). The olive quick decline syndrome in south-east Italy: a threatening phytosanitary emergency. European Journal of Plant Pathology, 144(2), 235-243. doi:10.1007/s10658-015-0784-7Olmo, D., Nieto, A., Adrover, F., Urbano, A., Beidas, O., Juan, A., … Landa, B. B. (2017). First Detection of Xylella fastidiosa Infecting Cherry (Prunus avium) and Polygala myrtifolia Plants, in Mallorca Island, Spain. Plant Disease, 101(10), 1820-1820. doi:10.1094/pdis-04-17-0590-pdnSaponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D., & Saldarelli, P. (2019). Xylella fastidiosa in Olive in Apulia: Where We Stand. Phytopathology®, 109(2), 175-186. doi:10.1094/phyto-08-18-0319-fiVergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., … Araus, J. L. (2016). A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization. Frontiers in Plant Science, 7. doi:10.3389/fpls.2016.00666Thenkabail, P. S., & Lyon, J. G. (Eds.). (2016). Hyperspectral Remote Sensing of Vegetation. doi:10.1201/b11222Calderón, R., Navas-Cortés, J. A., Lucena, C., & Zarco-Tejada, P. J. (2013). High-resolution airborne hyperspectral and thermal imagery for early detection of Verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices. Remote Sensing of Environment, 139, 231-245. doi:10.1016/j.rse.2013.07.031Gonzalez-Dugo, V., Hernandez, P., Solis, I., & Zarco-Tejada, P. (2015). Using High-Resolution Hyperspectral and Thermal Airborne Imagery to Assess Physiological Condition in the Context of Wheat Phenotyping. Remote Sensing, 7(10), 13586-13605. doi:10.3390/rs71013586Hernández-Clemente, R., Navarro-Cerrillo, R., Ramírez, F., Hornero, A., & Zarco-Tejada, P. (2014). A Novel Methodology to Estimate Single-Tree Biophysical Parameters from 3D Digital Imagery Compared to Aerial Laser Scanner Data. Remote Sensing, 6(11), 11627-11648. doi:10.3390/rs61111627Colaço, A. F., Molin, J. P., Rosell-Polo, J. R., & Escolà, A. (2018). Application of light detection and ranging and ultrasonic sensors to high-throughput phenotyping and precision horticulture: current status and challenges. Horticulture Research, 5(1). doi:10.1038/s41438-018-0043-0Ma, Q., Su, Y., Luo, L., Li, L., Kelly, M., & Guo, Q. (2018). Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data. Ecological Indicators, 95, 298-310. doi:10.1016/j.ecolind.2018.07.050Ma, Q., Su, Y., & Guo, Q. (2017). Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(9), 4225-4236. doi:10.1109/jstars.2017.2711482Martinelli, F., Scalenghe, R., Davino, S., Panno, S., Scuderi, G., Ruisi, P., … Dandekar, A. M. (2014). Advanced methods of plant disease detection. A review. Agronomy for Sustainable Development, 35(1), 1-25. doi:10.1007/s13593-014-0246-1Calderón, R., Navas-Cortés, J., & Zarco-Tejada, P. (2015). Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas. Remote Sensing, 7(5), 5584-5610. doi:10.3390/rs70505584Zarco-Tejada, P. J., Camino, C., Beck, P. S. A., Calderon, R., Hornero, A., Hernández-Clemente, R., … Navas-Cortes, J. A. (2018). Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nature Plants, 4(7), 432-439. doi:10.1038/s41477-018-0189-7Aasen, H., Honkavaara, E., Lucieer, A., & Zarco-Tejada, P. (2018). Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows. Remote Sensing, 10(7), 1091. doi:10.3390/rs10071091Vicent, A., & Blasco, J. (2017). When prevention fails. Towards more efficient strategies for plant disease eradication. New Phytologist, 214(3), 905-908. doi:10.1111/nph.14555Wang, X., Singh, D., Marla, S., Morris, G., & Poland, J. (2018). Field-based high-throughput phenotyping of plant height in sorghum using different sensing technologies. Plant Methods, 14(1). doi:10.1186/s13007-018-0324-5Bourgeon, M. A., Gée, C., Debuisson, S., Villette, S., Jones, G., & Paoli, J. N. (2016). « On-the-go » multispectral imaging system to characterize the development of vineyard foliage with quantitative and qualitative vegetation indices. Precision Agriculture, 18(3), 293-308. doi:10.1007/s11119-016-9489-yUnderwood, J. P., Hung, C., Whelan, B., & Sukkarieh, S. (2016). Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Computers and Electronics in Agriculture, 130, 83-96. doi:10.1016/j.compag.2016.09.014Zampetti, E., Papa, P., Di Flaviano, F., Paciucci, L., Petracchini, F., Pirrone, N., … Macagnano, A. (2017). Remotely Controlled Terrestrial Vehicle Integrated Sensory System for Environmental Monitoring. Sensors, 338-343. doi:10.1007/978-3-319-55077-0_43Hiremath, S. A., van der Heijden, G. W. A. M., van Evert, F. K., Stein, A., & ter Braak, C. J. F. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41-50. doi:10.1016/j.compag.2013.10.005Pérez-Ruiz, M., Gonzalez-de-Santos, P., Ribeiro, A., Fernandez-Quintanilla, C., Peruzzi, A., Vieri, M., … Agüera, J. (2015). Highlights and preliminary results for autonomous crop protection. 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AI Knowledge Transfer from the University to Society

AI Knowledge Transfer from the University to Society: Applications in High-Impact Sectors brings together examples from the "Innovative Ecosystem with Artificial Intelligence for Andalusia 2025" project at the University of Seville, a series of sub-projects composed of research groups and different institutions or companies that explore the use of Artificial Intelligence in a variety of high-impact sectors to lead innovation and assist in decision-making. Key Features Includes chapters on health and social welfare, transportation, digital economy, energy efficiency and sustainability, agro-industry, and tourism Great diversity of authors, expert in varied sectors, belonging to powerful research groups from the University of Seville with proven experience in the transfer of knowledge to the productive sector and agents attached to the Andalucía TECH Campu

High Performance Implementation of Support Vector Machines Using OpenCL

Support Vector Machines are a machine learning approach that is well studied, thoroughly vetted and effective in a large number of applications. The objective of this thesis is to accelerate an implementation of Support Vector Machines (SVM) using a heterogeneous computing system programmed using OpenCL in C/C++. LIBSVM, a widely-available, popular and open source implementation of SVM is chosen, allowing the presented work to be integrated seamlessly into existing systems. The proposed framework is evaluated in terms of speed and accuracy when performing training and classification on a number of standard data sets. Testing was based on two work station GPUs, the NVIDIA GTX 480 and Tesla K20, and a modern, work station CPU (Intel i5 Quad Core, 3 GHz). We find that, for large data sets, training is accelerated by a factor ranging from 9 to 22. In general, speedup increases with the total number of training samples in the data set until the GPU device is fully utilized. While these gains in speedup are significant, they do not match the ideal parallel speedup, that is the total number of cores in the parallel system. Our findings indicate that performance is hampered by the portions of the SVM training algorithm that are sequential. In addition, we find that the classification phase of the SVM system is accelerated by a factor of up to 12. During classification only a relatively small number of samples are classified compared to the typical number of training samples, and the computational complexity of classification grows only linearly with the number of samples processed, as opposed to the training phase where it grows quadratically. The contri- butions of this thesis include the use of OpenCL for accelerating SVM training and testing on heterogeneous systems, and the performance analysis of the acceleration of SVM

Is Reduced Visual Processing the Price of Language?

We suggest a later timeline for full language capabilities in Homo sapiens, placing the emergence of language over 200,000 years after the emergence of our species. The late Paleolithic period saw several significant changes. Homo sapiens became more gracile and gradually lost significant brain volumes. Detailed realistic cave paintings disappeared completely, and iconic/symbolic ones appeared at other sites. This may indicate a shift in perceptual abilities, away from an accurate perception of the present. Language in modern humans interact with vision. One example is the McGurk effect. Studies show that artistic abilities may improve when language-related brain areas are damaged or temporarily knocked out. Language relies on many pre-existing non-linguistic functions. We suggest that an overwhelming flow of perceptual information, vision, in particular, was an obstacle to language, as is sometimes implied in autism with relative language impairment. We systematically review the recent research literature investigating the relationship between language and perception. We see homologues of language-relevant brain functions predating language. Recent findings show brain lateralization for communicative gestures in other primates without language, supporting the idea that a language-ready brain may be overwhelmed by raw perception, thus blocking overt language from evolving. We find support in converging evidence for a change in neural organization away from raw perception, thus pushing the emergence of language closer in time. A recent origin of language makes it possible to investigate the genetic origins of language.publishedVersio

Web GIS to support irrigation management: a prototype for SAGRA network, Alentejo Portugal

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAn efficient water management, not only allows significant savings in costs of irrigation, but also an effective control on the quality of products, which can have obvious consequences on income operation and reducing the environmental impact of irrigation. As the Internet is becoming the easiest way of information distribution, irrigation management system can also be benefitted with it. Integrating GIS functionality with internet capacity will redefine the way of decision making, sharing and processing of information. In irrigation systems weather plays an imperative role in decision making, implementing and forecasting. Temperature, humidity, precipitation, and solar radiation are the most important parameters to calculate evapotranspiration by which crop water requirement can be determined. SAGRA (Sistema Agrometeorológico para a Gestão da Rega no Alentejo) network is providing information to the farmers through web but still lacks the use of GIS in their information to decision support system. Irrigation management support system can be benefitted with the use of Web GIS. In this thesis, web based GIS is designed using popular open source tools and software. Using data from automatic weather station maps are produced using Geo-statistical interpolation techniques and published in web map. These maps can be viewed with popular online maps like Google maps, Microsoft Bing and Openstreet maps. Animated weather maps are also created which are useful for visualizing changing pattern of weather parameters and water requirement over time

High Efficiency Real-Time Sensor and Actuator Control and Data Processing

The advances in sensor and actuator technology foster the use of large multitransducer networks in many different fields. The increasing complexity of such networks poses problems in data processing, especially when high-efficiency is required for real-time applications. In fact, multi-transducer data processing usually consists of interconnection and co-operation of several modules devoted to process different tasks. Multi-transducer network modules often include tasks such as control, data acquisition, data filtering interfaces, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, fuzzy-rules used to implement such tasks may introduce module interconnection and co-operation issues. To help dealing with these problems the author here presents a software library architecture for a dynamic and efficient management of multi-transducer data processing and control techniques. The framework’s base architecture and the implementation details of several extensions are described. Starting from the base models available in the framework core dedicated models for control processes and neural network tools have been derived. The Facial Automaton for Conveying Emotion (FACE) has been used as a test field for the control architecture