Cesar Isaza, Joaquin Salas, & Bogdan Raducanu. (2012). Synthetic ground truth dataset to detect shadow cast by static objects in outdoor. In 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications (art. 11). ACM.
Abstract: In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software.
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2007). Traffic Sign Classification using Error Correcting Techniques. In 2nd International Conference on Computer Vision Theory and Applications (281–285).
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Karla Lizbeth Caballero, Joel Barajas, & Oriol Pujol. (2007). Reconstructing IVUS Images for an Accurate Tissue Classification. In Proceedings of the Second International Conference on Computer Vision Theory and Applications (Vol. Special Sessions, 113–119).
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Sergio Escalera, Oriol Pujol, & Petia Radeva. (2008). Loss-Weighted Decoding for Error-Correcting Output Coding. In 3rd International Conference on Computer Vision Theory and Applications, (Vol. 2, 117–122).
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David Masip, Agata Lapedriza, & Jordi Vitria. (2008). Multitask Learning: An Application to Incremental Face Recognition. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 1, 585–590).
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Agata Lapedriza, David Masip, & Jordi Vitria. (2008). Subject Recognition Using a New Approach for Feature Extraction. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 2, 61–66).
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Fadi Dornaika, & Angel Sappa. (2007). Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data. In H. Araujo and J. Jorge A. R. J. Braz (Ed.), Advances in Computer Graphics and Computer Vision, (354–366). Springer Verlag.
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Agnes Borras, & Josep Llados. (2009). Corest: A measure of color and space stability to detect salient regions according to human criteria. In 5th International Conference on Computer Vision Theory and Applications (pp. 204–209).
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Partha Pratim Roy, Josep Llados, & Umapada Pal. (2009). A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information. In 5th International Conference on Computer Vision Theory and Applications.
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Hongxing Gao, Marçal Rusiñol, Dimosthenis Karatzas, Apostolos Antonacopoulos, & Josep Llados. (2013). An interactive appearance-based document retrieval system for historical newspapers. In Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 84–87).
Abstract: In this paper we present a retrieval-based application aimed at assisting a user to semi-automatically segment an incoming flow of historical newspaper images by automatically detecting a particular type of pages based on their appearance. A visual descriptor is used to assess page similarity while a relevance feedback process allow refining the results iteratively. The application is tested on a large dataset of digitised historic newspapers.
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Diego Cheda, Daniel Ponsa, & Antonio Lopez. (2012). Monocular Depth-based Background Estimation. In 7th International Conference on Computer Vision Theory and Applications (pp. 323–328).
Abstract: In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences.
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Christophe Rigaud, Dimosthenis Karatzas, Joost Van de Weijer, Jean-Christophe Burie, & Jean-Marc Ogier. (2013). Automatic text localisation in scanned comic books. In Proceedings of the International Conference on Computer Vision Theory and Applications (pp. 814–819).
Abstract: Comic books constitute an important cultural heritage asset in many countries. Digitization combined with subsequent document understanding enable direct content-based search as opposed to metadata only search (e.g. album title or author name). Few studies have been done in this direction. In this work we detail a novel approach for the automatic text localization in scanned comics book pages, an essential step towards a fully automatic comics book understanding. We focus on speech text as it is semantically important and represents the majority of the text present in comics. The approach is compared with existing methods of text localization found in the literature and results are presented.
Keywords: Text localization; comics; text/graphic separation; complex background; unstructured document
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Carles Sanchez, Debora Gil, Antoni Rosell, Albert Andaluz, & F. Javier Sanchez. (2013). Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance. In Sebastiano Battiato and José Braz (Ed.), Proceedings of the International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 153–161). LNCS. Portugal: SciTePress.
Abstract: Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability
Keywords: Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model
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Pedro Martins, Carlo Gatta, & Paulo Carvalho. (2012). Feature-driven Maximally Stable Extremal Regions. In 7th International Conference on Computer Vision Theory and Applications (pp. 490–497).
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Patricia Marquez, Debora Gil, R.Mester, & Aura Hernandez-Sabate. (2014). Local Analysis of Confidence Measures for Optical Flow Quality Evaluation. In 9th International Conference on Computer Vision Theory and Applications (Vol. 3, pp. 450–457).
Abstract: Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance.
Keywords: Optical Flow; Confidence Measure; Performance Evaluation.
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