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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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	Author	David Fernandez; Josep Llados; Alicia Fornes; R.Manmatha
	Title	On Influence of Line Segmentation in Efficient Word Segmentation in Old Manuscripts			Type	Conference Article
	Year	2012	Publication	13th International Conference on Frontiers in Handwriting Recognition	Abbreviated Journal
	Volume		Issue		Pages	763-768
	Keywords	document image processing;handwritten character recognition;history;image segmentation;Spanish document;historical document;line segmentation;old handwritten document;old manuscript;word segmentation;Bifurcation;Dynamic programming;Handwriting recognition;Image segmentation;Measurement;Noise;Skeleton;Segmentation;document analysis;document and text processing;handwriting analysis;heuristics;path-finding
	Abstract	he objective of this work is to show the importance of a good line segmentation to obtain better results in the segmentation of words of historical documents. We have used the approach developed by Manmatha and Rothfeder [1] to segment words in old handwritten documents. In their work the lines of the documents are extracted using projections. In this work, we have developed an approach to segment lines more efficiently. The new line segmentation algorithm tackles with skewed, touching and noisy lines, so it is significantly improves word segmentation. Experiments using Spanish documents from the Marriages Database of the Barcelona Cathedral show that this approach reduces the error rate by more than 20%
	Address
	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 @ FLF2012			Serial	2200
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	Author	Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva
	Title	Automatic Non-Rigid Temporal Alignment of IVUS Sequences			Type	Conference Article
	Year	2012	Publication	15th International Conference on Medical Image Computing and Computer Assisted Intervention	Abbreviated Journal
	Volume	1	Issue		Pages	642-650
	Keywords
	Abstract	Clinical studies on atherosclerosis regression/progression performed by Intravascular Ultrasound analysis require the alignment of pullbacks of the same patient before and after clinical interventions. In this paper, a methodology for the automatic alignment of IVUS sequences based on the Dynamic Time Warping technique is proposed. The method is adapted to the specific IVUS alignment task by applying the non-rigid alignment technique to multidimensional morphological signals, and by introducing a sliding window approach together with a regularization term. To show the effectiveness of our method, an extensive validation is performed both on synthetic data and in-vivo IVUS sequences. The proposed method is robust to stent deployment and post dilation surgery and reaches an alignment error of approximately 0.7 mm for in-vivo data, which is comparable to the inter-observer variability.
	Address	Nice, France
	Corporate Author				Thesis
	Publisher	Springer-Verlag Berlin, Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-33414-6	Medium
	Area		Expedition		Conference	MICCAI
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ ABC2012			Serial	2168
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	Author	Adriana Romero; Simeon Petkov; Carlo Gatta; M.Sabate; Petia Radeva
	Title	Efficient automatic segmentation of vessels			Type	Conference Article
	Year	2012	Publication	16th Conference on Medical Image Understanding and Analysis	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Swansea, United Kingdom
	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	MIUA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @			Serial	2137
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	Author	Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
	Title	Error Correcting Output Codes for multiclass classification: Application to two image vision problems			Type	Conference Article
	Year	2012	Publication	16th symposium on Artificial Intelligence & Signal Processing	Abbreviated Journal
	Volume		Issue		Pages	508-513
	Keywords
	Abstract	Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches.
	Address	Shiraz, Iran
	Corporate Author				Thesis
	Publisher	IEEE Xplore	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4673-1478-7	Medium
	Area		Expedition		Conference	AISP
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ BGE2012b			Serial	2042
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	Author	Ekaterina Zaytseva; Santiago Segui; Jordi Vitria
	Title	Sketchable Histograms of Oriented Gradients for Object Detection			Type	Conference Article
	Year	2012	Publication	17th Iberomerican Conference on Pattern Recognition	Abbreviated Journal
	Volume	7441	Issue		Pages	374-381
	Keywords
	Abstract	In this paper we investigate a new representation approach for visual object recognition. The new representation, called sketchable-HoG, extends the classical histogram of oriented gradients (HoG) feature by adding two different aspects: the stability of the majority orientation and the continuity of gradient orientations. In this way, the sketchable-HoG locally characterizes the complexity of an object model and introduces global structure information while still keeping simplicity, compactness and robustness. We evaluated the proposed image descriptor on publicly Catltech 101 dataset. The obtained results outperforms classical HoG descriptor as well as other reported descriptors in the literature.
	Address	Buenos Aires, Argentina
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-33274-6	Medium
	Area		Expedition		Conference	CIARP
	Notes	OR; MILAB;MV			Approved	no
	Call Number	Admin @ si @ ZSV2012			Serial	2048
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	Author	Ekaterina Zaytseva; Jordi Vitria
	Title	A search based approach to non maximum suppression in face detection			Type	Conference Article
	Year	2012	Publication	19th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Poster paper TA.P5.12 Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results.
	Address	Orlando; USA; September 2012
	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	1522-4880	ISBN	978-1-4673-2534-9	Medium
	Area		Expedition		Conference	ICIP
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ ZaV2012			Serial	2060
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	Author	Santiago Segui; Michal Drozdzal; Petia Radeva; Jordi Vitria
	Title	An Integrated Approach to Contextual Face Detection			Type	Conference Article
	Year	2012	Publication	1st International Conference on Pattern Recognition Applications and Methods	Abbreviated Journal
	Volume		Issue		Pages	143-150
	Keywords
	Abstract	Face detection is, in general, based on content-based detectors. Nevertheless, the face is a non-rigid object with well defined relations with respect to the human body parts. In this paper, we propose to take benefit of the context information in order to improve content-based face detections. We propose a novel framework for integrating multiple content- and context-based detectors in a discriminative way. Moreover, we develop an integrated scoring procedure that measures the ’faceness’ of each hypothesis and is used to discriminate the detection results. Our approach detects a higher rate of faces while minimizing the number of false detections, giving an average increase of more than 10% in average precision when comparing it to state-of-the art face detectors
	Address	Vilamoura, Algarve, Portugal
	Corporate Author				Thesis
	Publisher	Springer	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	MILAB; OR;MV			Approved	no
	Call Number	Admin @ si @ SDR2012			Serial	1895
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	Author	Diego Cheda; Daniel Ponsa; Antonio Lopez
	Title	Monocular Egomotion Estimation based on Image Matching			Type	Conference Article
	Year	2012	Publication	1st International Conference on Pattern Recognition Applications and Methods	Abbreviated Journal
	Volume		Issue		Pages	425-430
	Keywords	SLAM
	Abstract
	Address	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 @ CPL2012a;; ADAS @ adas @			Serial	2011
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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	Cesar Isaza; Joaquin Salas; Bogdan Raducanu
	Title	Synthetic ground truth dataset to detect shadow cast by static objects in outdoor			Type	Conference Article
	Year	2012	Publication	1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications	Abbreviated Journal
	Volume		Issue		Pages	art. 11
	Keywords
	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.
	Address	Capri, Italy
	Corporate Author				Thesis
	Publisher	ACM	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-4503-1405-3	Medium
	Area		Expedition		Conference	VIGTA
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ ISR2012a			Serial	2037
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	Author	David Vazquez; Antonio Lopez; Daniel Ponsa
	Title	Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3492 - 3495
	Keywords	Pedestrian Detection; Domain Adaptation; Virtual worlds
	Abstract	Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1).
	Address	Tsukuba Science City, Japan
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication	Tsukuba Science City, JAPAN	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	ADAS @ adas @ VLP2012			Serial	1981
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