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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Fernando Barrera, Felipe Lumbreras, Cristhian Aguilera, & Angel Sappa. (2012). Planar-Based Multispectral Stereo. In 11th Quantitative InfraRed Thermography.
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Cristhian Aguilera, Fernando Barrera, Angel Sappa, & Ricardo Toledo. (2012). A Novel SIFT-Like-Based Approach for FIR-VS Images Registration. In 11th Quantitative InfraRed Thermography.
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Monica Piñol, Angel Sappa, Angeles Lopez, & Ricardo Toledo. (2012). Feature Selection Based on Reinforcement Learning for Object Recognition. In Adaptive Learning Agents Workshop (pp. 33–39).
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German Ros, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2012). Visual SLAM for Driverless Cars: A Brief Survey. In IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles.
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Naila Murray, Luca Marchesotti, & Florent Perronnin. (2012). Learning to Rank Images using Semantic and Aesthetic Labels. In 23rd British Machine Vision Conference (110.pp. 1–110.10).
Abstract: Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one.
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Simeon Petkov, Adriana Romero, Xavier Carrillo, Petia Radeva, & Carlo Gatta. (2012). Robust and accurate diaphragm border detection in cardiac X-Ray angiographies. In Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges (Vol. 7746, pp. 225–234). LNCS.
Abstract: Workshop STACOM, dins del MICCAI
X-ray angiography is the most common imaging modality employed in the diagnosis of coronary diseases prior to or during a catheter-based intervention. The analysis of the patient X-Ray sequence can provide useful information about the degree of arterial stenosis, the myocardial perfusion and other clinical parameters. If the sequence has been acquired to evaluate the perfusion grade, the opacity due to the diaphragm could potentially hinder any kind of visual inspection and make more difficult a computer aided measurements. In this paper we propose an accurate and robust method to automatically identify the diaphragm border in each frame. Quantitative evaluation on a set of 11 sequences shows that the proposed algorithm outperforms previous methods.
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German Ros, Jesus Martinez del Rincon, & Gines Garcia-Mateos. (2012). Articulated Particle Filter for Hand Tracking. In 21st International Conference on Pattern Recognition (pp. 3581–3585).
Abstract: This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper.
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Jose Carlos Rubio, Joan Serrat, & Antonio Lopez. (2012). Multiple target tracking and identity linking under split, merge and occlusion of targets and observations. In 1st International Conference on Pattern Recognition Applications and Methods.
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Ferran Diego, G.D. Evangelidis, & Joan Serrat. (2012). Night-time outdoor surveillance by mobile cameras. In 1st International Conference on Pattern Recognition Applications and Methods (Vol. 2, pp. 365–371).
Abstract: This paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be “localized” in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods.
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Three-Dimensional Design of Error Correcting Output Codes. In 8th International Conference on Machine Learning and Data Mining (pp. 29–).
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Mohammad Ali Bagheri, Qigang Gao, & Sergio Escalera. (2012). Efficient pairwise classification using Local Cross Off strategy. In 25th Canadian Conference on Artificial Intelligence (Vol. 7310, pp. 25–36). LNCS.
Abstract: The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
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Francisco Cruz, & Oriol Ramos Terrades. (2012). Document segmentation using relative location features. In 21st International Conference on Pattern Recognition (pp. 1562–1565).
Abstract: In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works.
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Volkmar Frinken, Francisco Zamora, Salvador España, Maria Jose Castro, Andreas Fischer, & Horst Bunke. (2012). Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition. In 21st International Conference on Pattern Recognition (pp. 701–704).
Abstract: Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models.
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Marçal Rusiñol, Dimosthenis Karatzas, Andrew Bagdanov, & Josep Llados. (2012). Multipage Document Retrieval by Textual and Visual Representations. In 21st International Conference on Pattern Recognition (pp. 521–524).
Abstract: In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow.
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