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	Author	Palaiahnakote Shivakumara; Anjan Dutta; Trung Quy Phan; Chew Lim Tan; Umapada Pal
	Title	A Novel Mutual Nearest Neighbor based Symmetry for Text Frame Classification in Video			Type	Journal Article
	Year	2011	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	44	Issue	8	Pages	1671-1683
	Keywords
	Abstract	In the field of multimedia retrieval in video, text frame classification is essential for text detection, event detection, event boundary detection, etc. We propose a new text frame classification method that introduces a combination of wavelet and median moment with k-means clustering to select probable text blocks among 16 equally sized blocks of a video frame. The same feature combination is used with a new Max–Min clustering at the pixel level to choose probable dominant text pixels in the selected probable text blocks. For the probable text pixels, a so-called mutual nearest neighbor based symmetry is explored with a four-quadrant formation centered at the centroid of the probable dominant text pixels to know whether a block is a true text block or not. If a frame produces at least one true text block then it is considered as a text frame otherwise it is a non-text frame. Experimental results on different text and non-text datasets including two public datasets and our own created data show that the proposed method gives promising results in terms of recall and precision at the block and frame levels. Further, we also show how existing text detection methods tend to misclassify non-text frames as text frames in term of recall and precision at both the block and frame levels.
	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		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ SDP2011			Serial	1727
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	Author	Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Xavier Roca
	Title	Efficient Discriminative Multiresolution Cascade for Real-Time Human Detection Applications			Type	Journal Article
	Year	2011	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	32	Issue	13	Pages	1581-1587
	Keywords
	Abstract	Human detection is fundamental in many machine vision applications, like video surveillance, driving assistance, action recognition and scene understanding. However in most of these applications real-time performance is necessary and this is not achieved yet by current detection methods. This paper presents a new method for human detection based on a multiresolution cascade of Histograms of Oriented Gradients (HOG) that can highly reduce the computational cost of detection search without affecting accuracy. The method consists of a cascade of sliding window detectors. Each detector is a linear Support Vector Machine (SVM) composed of HOG features at different resolutions, from coarse at the first level to fine at the last one. In contrast to previous methods, our approach uses a non-uniform stride of the sliding window that is defined by the feature resolution and allows the detection to be incrementally refined as going from coarse-to-fine resolution. In this way, the speed-up of the cascade is not only due to the fewer number of features computed at the first levels of the cascade, but also to the reduced number of windows that need to be evaluated at the coarse resolution. Experimental results show that our method reaches a detection rate comparable with the state-of-the-art of detectors based on HOG features, while at the same time the detection search is up to 23 times faster.
	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		Medium
	Area		Expedition		Conference
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ PGB2011a			Serial	1707
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	Author	Alicia Fornes; Anjan Dutta; Albert Gordo; Josep Llados
	Title	The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1511-1515
	Keywords
	Abstract	In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a ground-truth of handwritten music score images. This paper describes the competition details, including the dataset and ground-truth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results.
	Address	Beijing, China
	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	978-0-7695-4520-2	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ FDG2011b			Serial	1794
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	Author	Dimosthenis Karatzas; Sergi Robles; Joan Mas; Farshad Nourbakhsh; Partha Pratim Roy
	Title	ICDAR 2011 Robust Reading Competition – Challege 1: Reading Text in Born-Digital Images (Web and Email)			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1485-1490
	Keywords
	Abstract	This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition. Challenge 1 is focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails. The challenge was organized in terms of three tasks that look at different stages of the process: text localization, text segmentation and word recognition. In this paper we present the results of the challenge for all three tasks, and make an open call for continuous participation outside the context of ICDAR 2011.
	Address	Beijing, China
	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	1520-5363	ISBN	978-1-4577-1350-7	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ KRM2011			Serial	1793
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	Author	Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez
	Title	A Coarse-to-fine Approach for fast Deformable Object Detection			Type	Conference Article
	Year	2011	Publication	IEEE conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1353-1360
	Keywords
	Abstract
	Address	Colorado Springs; USA
	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	CVPR
	Notes	ISE			Approved	no
	Call Number	Admin @ si @ PVG2011			Serial	1764
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	Author	Jose Seabra; Francesco Ciompi; Oriol Pujol; J. Mauri; Petia Radeva; Joao Sanchez
	Title	Rayleigh Mixture Model for Plaque Characterization in Intravascular Ultrasound			Type	Journal Article
	Year	2011	Publication	IEEE Transactions on Biomedical Engineering	Abbreviated Journal	TBME
	Volume	58	Issue	5	Pages	1314-1324
	Keywords
	Abstract	Vulnerable plaques are the major cause of carotid and coronary vascular problems, such as heart attack or stroke. A correct modeling of plaque echomorphology and composition can help the identification of such lesions. The Rayleigh distribution is widely used to describe (nearly) homogeneous areas in ultrasound images. Since plaques may contain tissues with heterogeneous regions, more complex distributions depending on multiple parameters are usually needed, such as Rice, K or Nakagami distributions. In such cases, the problem formulation becomes more complex, and the optimization procedure to estimate the plaque echomorphology is more difficult. Here, we propose to model the tissue echomorphology by means of a mixture of Rayleigh distributions, known as the Rayleigh mixture model (RMM). The problem formulation is still simple, but its ability to describe complex textural patterns is very powerful. In this paper, we present a method for the automatic estimation of the RMM mixture parameters by means of the expectation maximization algorithm, which aims at characterizing tissue echomorphology in ultrasound (US). The performance of the proposed model is evaluated with a database of in vitro intravascular US cases. We show that the mixture coefficients and Rayleigh parameters explicitly derived from the mixture model are able to accurately describe different plaque types and to significantly improve the characterization performance of an already existing methodology.
	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		Medium
	Area		Expedition		Conference
	Notes	MILAB;HuPBA			Approved	no
	Call Number	Admin @ si @ SCP2011			Serial	1712
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	Author	Partha Pratim Roy; Umapada Pal; Josep Llados
	Title	Document Seal Detection Using Ght and Character Proximity Graphs			Type	Journal Article
	Year	2011	Publication	Pattern Recognition	Abbreviated Journal	PR
	Volume	44	Issue	6	Pages	1282-1295
	Keywords	Seal recognition; Graphical symbol spotting; Generalized Hough transform; Multi-oriented character recognition
	Abstract	This paper deals with automatic detection of seal (stamp) from documents with cluttered background. Seal detection involves a difficult challenge due to its multi-oriented nature, arbitrary shape, overlapping of its part with signature, noise, etc. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors computed from recognition result of individual connected components (characters). Scale and rotation invariant features are used in a Support Vector Machine (SVM) classifier to recognize multi-scale and multi-oriented text characters. The concept of generalized Hough transform (GHT) is used to detect the seal and a voting scheme is designed for finding possible location of the seal in a document based on the spatial feature descriptor of neighboring component pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal in a document. Experiment is performed in an archive of historical documents of handwritten/printed English text. Experimental results show that the method is robust in locating seal instances of arbitrary shape and orientation in documents, and also efficient in indexing a collection of documents for retrieval purposes.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier	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
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ RPL2011			Serial	1820
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	Author	Antonio Hernandez; Carlos Primo; Sergio Escalera
	Title	Automatic user interaction correction via Multi-label Graph cuts			Type	Conference Article
	Year	2011	Publication	In ICCV 2011 1st IEEE International Workshop on Human Interaction in Computer Vision HICV	Abbreviated Journal
	Volume		Issue		Pages	1276-1281
	Keywords
	Abstract	Most applications in image segmentation requires from user interaction in order to achieve accurate results. However, user wants to achieve the desired segmentation accuracy reducing effort of manual labelling. In this work, we extend standard multi-label α-expansion Graph Cut algorithm so that it analyzes the interaction of the user in order to modify the object model and improve final segmentation of objects. The approach is inspired in the fact that fast user interactions may introduce some pixel errors confusing object and background. Our results with different degrees of user interaction and input errors show high performance of the proposed approach on a multi-label human limb segmentation problem compared with classical α-expansion algorithm.
	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	978-1-4673-0062-9	Medium
	Area		Expedition		Conference	HICV
	Notes	MILAB; HuPBA			Approved	no
	Call Number	Admin @ si @ HPE2011			Serial	1892
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	Author	Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny
	Title	Wall Patch-Based Segmentation in Architectural Floorplans			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	1270-1274
	Keywords
	Abstract	Segmentation of architectural floor plans is a challenging task, mainly because of the large variability in the notation between different plans. In general, traditional techniques, usually based on analyzing and grouping structural primitives obtained by vectorization, are only able to handle a reduced range of similar notations. In this paper we propose an alternative patch-based segmentation approach working at pixel level, without need of vectorization. The image is divided into a set of patches and a set of features is extracted for every patch. Then, each patch is assigned to a visual word of a previously learned vocabulary and given a probability of belonging to each class of objects. Finally, a post-process assigns the final label for every pixel. This approach has been applied to the detection of walls on two datasets of architectural floor plans with different notations, achieving high accuracy rates.
	Address	Beiging, China
	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	1520-5363	ISBN	978-0-7695-4520-2	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ HMS2011a			Serial	1792
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	Author	Miguel Reyes; Gabriel Dominguez; Sergio Escalera
	Title	Feature Weighting in Dynamic Time Warping for Gesture Recognition in Depth Data			Type	Conference Article
	Year	2011	Publication	1st IEEE Workshop on Consumer Depth Cameras for Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	1182-1188
	Keywords
	Abstract	We present a gesture recognition approach for depth video data based on a novel Feature Weighting approach within the Dynamic Time Warping framework. Depth features from human joints are compared through video sequences using Dynamic Time Warping, and weights are assigned to features based on inter-intra class gesture variability. Feature Weighting in Dynamic Time Warping is then applied for recognizing begin-end of gestures in data sequences. The obtained results recognizing several gestures in depth data show high performance compared with classical Dynamic Time Warping approach.
	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	978-1-4673-0062-9	Medium
	Area		Expedition		Conference	CDC4CV
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ RDE2011			Serial	1893
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	Author	Jon Almazan; Alicia Fornes; Ernest Valveny
	Title	A Non-Rigid Feature Extraction Method for Shape Recognition			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	987-991
	Keywords
	Abstract	This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.
	Address	Beijing; China; September 2011
	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	978-0-7695-4520-2	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ AFV2011			Serial	1763
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	Author	Anjan Dutta; Josep Llados; Umapada Pal
	Title	Symbol Spotting in Line Drawings Through Graph Paths Hashing			Type	Conference Article
	Year	2011	Publication	11th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	982-986
	Keywords
	Abstract	In this paper we propose a symbol spotting technique through hashing the shape descriptors of graph paths (Hamiltonian paths). Complex graphical structures in line drawings can be efficiently represented by graphs, which ease the accurate localization of the model symbol. Graph paths are the factorized substructures of graphs which enable robust recognition even in the presence of noise and distortion. In our framework, the entire database of the graphical documents is indexed in hash tables by the locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. The spotting method is formulated by a spatial voting scheme to the list of locations of the paths that are decided during the hash table lookup process. We perform detailed experiments with various dataset of line drawings and the results demonstrate the effectiveness and efficiency of the technique.
	Address	Beijing, China
	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	1520-5363	ISBN	978-1-4577-1350-7	Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ DLP2011b			Serial	1791
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	Author	A. Toet; M. Henselmans; M.P. Lucassen; Theo Gevers
	Title	Emotional effects of dynamic textures			Type	Journal
	Year	2011	Publication	i-Perception	Abbreviated Journal	iPER
	Volume	2	Issue	9	Pages	969 – 991
	Keywords
	Abstract	This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures’ area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval.
	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	2041-6695	ISBN		Medium
	Area		Expedition		Conference
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @THL2011			Serial	1843
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	Author	Fadi Dornaika; Jose Manuel Alvarez; Angel Sappa; Antonio Lopez
	Title	A New Framework for Stereo Sensor Pose through Road Segmentation and Registration			Type	Journal Article
	Year	2011	Publication	IEEE Transactions on Intelligent Transportation Systems	Abbreviated Journal	TITS
	Volume	12	Issue	4	Pages	954-966
	Keywords	road detection
	Abstract	This paper proposes a new framework for real-time estimation of the onboard stereo head's position and orientation relative to the road surface, which is required for any advanced driver-assistance application. This framework can be used with all road types: highways, urban, etc. Unlike existing works that rely on feature extraction in either the image domain or 3-D space, we propose a framework that directly estimates the unknown parameters from the stream of stereo pairs' brightness. The proposed approach consists of two stages that are invoked for every stereo frame. The first stage segments the road region in one monocular view. The second stage estimates the camera pose using a featureless registration between the segmented monocular road region and the other view in the stereo pair. This paper has two main contributions. The first contribution combines a road segmentation algorithm with a registration technique to estimate the online stereo camera pose. The second contribution solves the registration using a featureless method, which is carried out using two different optimization techniques: 1) the differential evolution algorithm and 2) the Levenberg-Marquardt (LM) algorithm. We provide experiments and evaluations of performance. The results presented show the validity of our proposed framework.
	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	1524-9050	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ DAS2011; ADAS @ adas @ das2011a			Serial	1833
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	Author	Mario Rojas; David Masip; Jordi Vitria
	Title	Predicting Dominance Judgements Automatically: A Machine Learning Approach.			Type	Conference Article
	Year	2011	Publication	IEEE International Workshop on Social Behavior Analysis	Abbreviated Journal
	Volume		Issue		Pages	939-944
	Keywords
	Abstract	The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task.
	Address	Santa Barbara, CA
	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	978-1-4244-9140-7	Medium
	Area		Expedition		Conference	SBA
	Notes	OR;MV			Approved	no
	Call Number	Admin @ si @ RMV2011b			Serial	1760
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