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	Author	Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo
	Title	Feature Selection Based on Reinforcement Learning for Object Recognition			Type	Conference Article
	Year	2012	Publication	Adaptive Learning Agents Workshop	Abbreviated Journal
	Volume		Issue		Pages	33-39
	Keywords
	Abstract
	Address	Valencia
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ALA
	Notes	ADAS; RV			Approved	no
	Call Number	Admin @ si @ PSL2012			Serial	2018
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	Author	German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez
	Title	Visual SLAM for Driverless Cars: A Brief Survey			Type	Conference Article
	Year	2012	Publication	IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	SLAM
	Abstract
	Address	Alcalá de Henares
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IVW
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RSP2012; ADAS @ adas			Serial	2019
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	Author	Naila Murray; Luca Marchesotti; Florent Perronnin
	Title	Learning to Rank Images using Semantic and Aesthetic Labels			Type	Conference Article
	Year	2012	Publication	23rd British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages	110.1-110.10
	Keywords
	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.
	Address	Guildford, London
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	1-901725-46-4	Medium
	Area		Expedition		Conference	BMVC
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ MMP2012b			Serial	2027
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	Author	Simeon Petkov; Adriana Romero; Xavier Carrillo; Petia Radeva; Carlo Gatta
	Title	Robust and accurate diaphragm border detection in cardiac X-Ray angiographies			Type	Conference Article
	Year	2012	Publication	Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges	Abbreviated Journal
	Volume	7746	Issue		Pages	225-234
	Keywords
	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.
	Address	Nice, France
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-36960-5	Medium
	Area		Expedition		Conference	STACOM
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ PRC2012			Serial	2028
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	Author	German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos
	Title	Articulated Particle Filter for Hand Tracking			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3581 - 3585
	Keywords
	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.
	Address	Tsukuba Science City, Japan
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4673-2216-4	Medium
	Area		Expedition		Conference	ICPR
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RMG2012			Serial	2031
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	Author	Jose Carlos Rubio; Joan Serrat; Antonio Lopez
	Title	Multiple target tracking and identity linking under split, merge and occlusion of targets and observations			Type	Conference Article
	Year	2012	Publication	1st International Conference on Pattern Recognition Applications and Methods	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Algarve, Portugal
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPRAM
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RSL2012c; ADAS @ adas			Serial	2034
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	Author	Ferran Diego; G.D. Evangelidis; Joan Serrat
	Title	Night-time outdoor surveillance by mobile cameras			Type	Conference Article
	Year	2012	Publication	1st International Conference on Pattern Recognition Applications and Methods	Abbreviated Journal
	Volume	2	Issue		Pages	365-371
	Keywords
	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.
	Address	Algarve, Portugal
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPRAM
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ DES2012			Serial	2035
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Three-Dimensional Design of Error Correcting Output Codes			Type	Conference Article
	Year	2012	Publication	8th International Conference on Machine Learning and Data Mining	Abbreviated Journal
	Volume		Issue		Pages	29-
	Keywords
	Abstract
	Address	Berlin, Germany
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	MLDM
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2012a			Serial	2041
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Efficient pairwise classification using Local Cross Off strategy			Type	Conference Article
	Year	2012	Publication	25th Canadian Conference on Artificial Intelligence	Abbreviated Journal
	Volume	7310	Issue		Pages	25-36
	Keywords
	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.
	Address	Toronto, Ontario
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-30352-4	Medium
	Area		Expedition		Conference	AI
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2012c			Serial	2044
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	Author	Francisco Cruz; Oriol Ramos Terrades
	Title	Document segmentation using relative location features			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1562-1565
	Keywords
	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.
	Address	Tsukuba Science City, Japan
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ CrR2012			Serial	2051
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	Author	Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke
	Title	Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	701-704
	Keywords
	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.
	Address	Tsukuba Science City, Japan
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4673-2216-4	Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FZE2012			Serial	2052
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	Author	Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
	Title	Multipage Document Retrieval by Textual and Visual Representations			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	521-524
	Keywords
	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.
	Address	Tsukuba Science City, Japan
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4673-2216-4	Medium
	Area		Expedition		Conference	ICPR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ RKB2012			Serial	2053
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	Author	Marçal Rusiñol; Josep Llados
	Title	The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback			Type	Conference Article
	Year	2012	Publication	13th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	55-60
	Keywords
	Abstract	In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.
	Address	Bari, Italy
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4673-2262-1	Medium
	Area		Expedition		Conference	ICFHR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ RuL2012			Serial	2054
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	Author	Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke
	Title	Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting			Type	Conference Article
	Year	2012	Publication	13th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	49-54
	Keywords
	Abstract	State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.
	Address	Bari, Italy
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	10.1109/ICFHR.2012.268	ISBN	978-1-4673-2262-1	Medium
	Area		Expedition		Conference	ICFHR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FBF2012			Serial	2055
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	Author	Emanuel Indermühle; Volkmar Frinken; Horst Bunke
	Title	Mode Detection in Online Handwritten Documents using BLSTM Neural Networks			Type	Conference Article
	Year	2012	Publication	13th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	302-307
	Keywords
	Abstract	Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data.
	Address	Bari, italy
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4673-2262-1	Medium
	Area		Expedition		Conference	ICFHR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ IFB2012			Serial	2056
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