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Oriol Rodriguez-Leor; Carlo Gatta; E. Fernandez-Nofrerias; Oriol Pujol; Neus Salvatella; C. Bosch; H. Tizon; Petia Radeva; J. Mauri |
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Computationally Efficient Image-based IVUS Pullbacks Gating |
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2008 |
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European Heart Journal, ESC Supplement, Munich, 2008, p. 775 |
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MILAB;HuPBA |
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BCNPCL @ bcnpcl @ RGF2008 |
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1036 |
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David Berga; Xavier Otazu |
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Computations of inhibition of return mechanisms by modulating V1 dynamics |
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2019 |
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28th Annual Computational Neuroscience Meeting |
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In this study we present a unifed model of the visual cortex for predicting visual attention using real image scenes. Feedforward mechanisms from RGC and LGN have been functionally modeled using wavelet filters at distinct orientations and scales for each chromatic pathway (Magno-, Parvo-, Konio-cellular) and polarity (ON-/OFF-center), by processing image components in the CIE Lab space. In V1, we process cortical interactions with an excitatory-inhibitory network of fring rate neurons, initially proposed by (Li, 1999), later extended by (Penacchio et al. 2013). Firing rates from model’s output have been used as predictors of neuronal activity to be projected in a map in superior colliculus (with WTA-like computations), determining locations of visual fxations. These locations will be considered as already visited areas for future saccades, therefore we integrated a spatiotemporal function of inhibition of return mechanisms (where LIP/FEF is responsible) to feed to the model with spatial memory for next saccades. Foveation mechanisms have been simulated with a cortical magnifcation function, which distort spatial viewing properties for each fxation. Results show lower prediction errors than with respect no IoR cases (Fig. 1), and it is functionally consistent with human psychophysical measurements. Our model follows a biologically-constrained architecture, previously shown to reproduce visual saliency (Berga & Otazu, 2018), visual discomfort (Penacchio et al. 2016), brightness (Penacchio et al. 2013) and chromatic induction (Cerda & Otazu, 2016). |
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Barcelona; July 2019 |
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CNS |
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NEUROBIT; no menciona |
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Admin @ si @ BeO2019a |
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3373 |
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Author |
David Berga; Xavier Otazu |
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Computations of top-down attention by modulating V1 dynamics |
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2020 |
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Computational and Mathematical Models in Vision |
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St. Pete Beach; Florida; May 2020 |
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MODVIS |
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NEUROBIT |
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Admin @ si @ BeO2020a |
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3376 |
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Angel Sappa (ed) |
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Computer Graphics and Imaging |
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2010 |
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Computer Graphics and Imaging |
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Angel Sappa |
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978–0–88986–836–6 |
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CGIM |
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ADAS |
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ADAS @ adas @ Sap2010 |
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1468 |
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Angel Sappa; George A. Triantafyllid |
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Computer Graphics and Imaging |
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2012 |
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Computer Graphics and Imaging |
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Crete, Greece |
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978-0-88986-921-9 |
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ADAS |
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no |
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Admin @ si @ Sap2012 |
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2067 |
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Author |
Ferran Poveda |
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Computer Graphics and Vision Techniques for the Study of the Muscular Fiber Architecture of the Myocardium |
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2013 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Debora Gil;Enric Marti |
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IAM |
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Admin @ si @ Pov2013 |
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2417 |
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Fernando Vilariño |
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Computer Vision and Performing Arts |
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2015 |
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Korean Scholars of Marketing Science |
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Seoul; Korea; October 2015 |
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KAMS |
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MV;SIAI |
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no |
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Admin @ si @Vil2015 |
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2799 |
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Author |
David Geronimo; Antonio Lopez; Angel Sappa |
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Title |
Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey |
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2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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1 |
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547–554 |
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Pedestrian detection |
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Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study. |
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Girona (Spain) |
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J. Marti et al. |
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ADAS |
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ADAS @ adas @ GLS2007 |
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804 |
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Author |
Henry Velesaca; Patricia Suarez; Raul Mira; Angel Sappa |
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Title |
Computer Vision based Food Grain Classification: a Comprehensive Survey |
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2021 |
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Computers and Electronics in Agriculture |
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CEA |
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187 |
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106287 |
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This manuscript presents a comprehensive survey on recent computer vision based food grain classification techniques. It includes state-of-the-art approaches intended for different grain varieties. The approaches proposed in the literature are analyzed according to the processing stages considered in the classification pipeline, making it easier to identify common techniques and comparisons. Additionally, the type of images considered by each approach (i.e., images from the: visible, infrared, multispectral, hyperspectral bands) together with the strategy used to generate ground truth data (i.e., real and synthetic images) are reviewed. Finally, conclusions highlighting future needs and challenges are presented. |
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MSIAU; 600.130; 600.122 |
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Admin @ si @ VSM2021 |
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3576 |
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Author |
Albert Ali Salah; Theo Gevers; Nicu Sebe; Alessandro Vinciarelli |
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Computer Vision for Ambient Intelligence |
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2011 |
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Journal of Ambient Intelligence and Smart Environments |
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JAISE |
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3 |
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3 |
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187-191 |
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ISE |
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Admin @ si @ SGS2011a |
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1725 |
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Author |
Michael Teutsch; Angel Sappa; Riad I. Hammoud |
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Title |
Computer Vision in the Infrared Spectrum: Challenges and Approaches |
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2021 |
Publication |
Synthesis Lectures on Computer Vision |
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10 |
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2 |
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1-138 |
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Human visual perception is limited to the visual-optical spectrum. Machine vision is not. Cameras sensitive to the different infrared spectra can enhance the abilities of autonomous systems and visually perceive the environment in a holistic way. Relevant scene content can be made visible especially in situations, where sensors of other modalities face issues like a visual-optical camera that needs a source of illumination. As a consequence, not only human mistakes can be avoided by increasing the level of automation, but also machine-induced errors can be reduced that, for example, could make a self-driving car crash into a pedestrian under difficult illumination conditions. Furthermore, multi-spectral sensor systems with infrared imagery as one modality are a rich source of information and can provably increase the robustness of many autonomous systems. Applications that can benefit from utilizing infrared imagery range from robotics to automotive and from biometrics to surveillance. In this book, we provide a brief yet concise introduction to the current state-of-the-art of computer vision and machine learning in the infrared spectrum. Based on various popular computer vision tasks such as image enhancement, object detection, or object tracking, we first motivate each task starting from established literature in the visual-optical spectrum. Then, we discuss the differences between processing images and videos in the visual-optical spectrum and the various infrared spectra. An overview of the current literature is provided together with an outlook for each task. Furthermore, available and annotated public datasets and common evaluation methods and metrics are presented. In a separate chapter, popular applications that can greatly benefit from the use of infrared imagery as a data source are presented and discussed. Among them are automatic target recognition, video surveillance, or biometrics including face recognition. Finally, we conclude with recommendations for well-fitting sensor setups and data processing algorithms for certain computer vision tasks. We address this book to prospective researchers and engineers new to the field but also to anyone who wants to get introduced to the challenges and the approaches of computer vision using infrared images or videos. Readers will be able to start their work directly after reading the book supported by a highly comprehensive backlog of recent and relevant literature as well as related infrared datasets including existing evaluation frameworks. Together with consistently decreasing costs for infrared cameras, new fields of application appear and make computer vision in the infrared spectrum a great opportunity to face nowadays scientific and engineering challenges. |
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978-1636392431 |
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MSIAU |
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Admin @ si @ TSH2021 |
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3666 |
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Author |
Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez |
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Computer Vision in Vehicle Technology: Land, Sea & Air |
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2017 |
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161-163 |
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Summary This chapter examines different vision-based commercial solutions for real-live problems related to vehicles. It is worth mentioning the recent astonishing performance of deep convolutional neural networks (DCNNs) in difficult visual tasks such as image classification, object recognition/localization/detection, and semantic segmentation. In fact,
different DCNN architectures are already being explored for low-level tasks such as optical flow and disparity computation, and higher level ones such as place recognition. |
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John Wiley & Sons, Ltd |
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978-1-118-86807-2 |
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ADAS; 600.118 |
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Admin @ si @ LIP2017a |
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2937 |
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Antonio Lopez; Atsushi Imiya; Tomas Pajdla; Jose Manuel Alvarez |
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Computer Vision in Vehicle Technology: Land, Sea & Air |
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Computer Vision in Vehicle Technology: Land, Sea & Air |
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A unified view of the use of computer vision technology for different types of vehicles
Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).
The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed. |
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978-1-118-86807-2 |
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DAG |
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Admin @ si @ LIP2017b |
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3049 |
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Author |
Joan M. Nuñez |
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Computer vision techniques for characterization of finger joints in X-ray image |
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Report |
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2011 |
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CVC Technical Report |
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165 |
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Rheumatoid arthritis, X-ray, Sharp Van der Heijde, joint characterization, sclerosis detection, bone detection, edge, ridge |
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Rheumatoid arthritis (RA) is an autoimmune inflammatory type of arthritis which mainly affects hands on its first stages. Though it is a chronic disease and there is no cure for it, treatments require an accurate assessment of illness evolution. Such assessment is based on evaluation of hand X-ray images by using one of the several available semi-quantitative methods. This task requires highly trained medical personnel. That is why the automation of the assessment would allow professionals to save time and effort. Two stages are involved in this task. Firstly, the joint detection, afterwards, the joint characterization. Unlike the little existing previous work, this contribution clearly separates those two stages and sets the foundations of a modular assessment system focusing on the characterization stage. A hand joint dataset is created and an accurate data analysis is achieved in order to identify relevant features. Since the sclerosis and the lower bone were decided to be the most important features, different computer vision techniques were used in order to develop a detector system for both of them. Joint space width measures are provided and their correlation with Sharp-Van der Heijde is verified |
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Bellaterra (Barcelona) |
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Computer Vision Center |
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Master's thesis |
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Dr. Fernando Vilariño and Dra. Debora Gil |
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MV;IAM; |
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no |
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Call Number |
IAM @ iam @ Nuñ2011 |
Serial |
1795 |
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| Permanent link to this record |
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Author |
Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados |
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Title |
Computer Vision Tools for Visually Impaired Children Learning |
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Journal |
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Year |
2007 |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ SFM2007a |
Serial |
891 |
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| Permanent link to this record |
