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Author Francesco Ciompi; Oriol Pujol; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva
Title Automatic Key Frames Detection in Intravascular Ultrasound Sequences Type Conference Article
Year 2011 Publication In MICCAI 2011 Workshop on Computing and Visualization for Intra Vascular Imaging Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) We present a method for the automatic detection of key frames in Intravascular Ultrasound (IVUS) sequences. The key frames are markers delimiting morphological changes along the vessel. The aim of defining key frames is two-fold: (1) they allow to summarize the content of the pullback into few representative frames; (2) they represent the basis for the automatic detection of clinical events in IVUS. The proposed approach achieved a compression ratio of 0.016 with respect to the original sequence and an average inter-frame distance of 61.76 frame, minimizing the number of missed clinical events.
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 CVII
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPB2011 Serial 1767
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Author Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard
Title Subgraph Spotting Through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images Type Conference Article
Year 2011 Publication 11th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 870-874
Keywords
Abstract (down) We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images.
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 @ LRL2011 Serial 1790
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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 (down) 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 Olivier Penacchio
Title Mixed Hodge Structures and Equivariant Sheaves on the Projective Plane Type Journal Article
Year 2011 Publication Mathematische Nachrichten Abbreviated Journal MN
Volume 284 Issue 4 Pages 526-542
Keywords Mixed Hodge structures, equivariant sheaves, MSC (2010) Primary: 14C30, Secondary: 14F05, 14M25
Abstract (down) We describe an equivalence of categories between the category of mixed Hodge structures and a category of equivariant vector bundles on a toric model of the complex projective plane which verify some semistability condition. We then apply this correspondence to define an invariant which generalizes the notion of R-split mixed Hodge structure and give calculations for the first group of cohomology of possibly non smooth or non-complete curves of genus 0 and 1. Finally, we describe some extension groups of mixed Hodge structures in terms of equivariant extensions of coherent sheaves. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Address
Corporate Author Thesis
Publisher WILEY-VCH Verlag Place of Publication Editor R. Mennicken
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-2616 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Pen2011 Serial 1721
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell
Title Portmanteau Vocabularies for Multi-Cue Image Representation Type Conference Article
Year 2011 Publication 25th Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation
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 NIPS
Notes CIC Approved no
Call Number Admin @ si @ KWB2011 Serial 1865
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Author Jürgen Brauer; Wenjuan Gong; Jordi Gonzalez; Michael Arens
Title On the Effect of Temporal Information on Monocular 3D Human Pose Estimation Type Conference Article
Year 2011 Publication 2nd IEEE International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams Abbreviated Journal
Volume Issue Pages 906 - 913
Keywords
Abstract (down) We address the task of estimating 3D human poses from monocular camera sequences. Many works make use of multiple consecutive frames for the estimation of a 3D pose in a frame. Although such an approach should ease the pose estimation task substantially since multiple consecutive frames allow to solve for 2D projection ambiguities in principle, it has not yet been investigated systematically how much we can improve the 3D pose estimates when using multiple consecutive frames opposed to single frame information. In this paper we analyze the difference in quality of 3D pose estimates based on different numbers of consecutive frames from which 2D pose estimates are available. We validate the use of temporal information on two major different approaches for human pose estimation – modeling and learning approaches. The results of our experiments show that both learning and modeling approaches benefit from using multiple frames opposed to single frame input but that the benefit is small when the 2D pose estimates show a high quality in terms of precision.
Address Barcelona
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 ARTEMIS
Notes ISE Approved no
Call Number Admin @ si @BGG 2011 Serial 1860
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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 (down) 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 Koen E.A. van de Sande; Theo Gevers; Cees G.M. Snoek
Title Empowering Visual Categorization with the GPU Type Journal Article
Year 2011 Publication IEEE Transactions on Multimedia Abbreviated Journal TMM
Volume 13 Issue 1 Pages 60-70
Keywords
Abstract (down) Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe drawback of this model is its high computational cost. As the trend to increase computational power in newer CPU and GPU architectures is to increase their level of parallelism, exploiting this parallelism becomes an important direction to handle the computational cost of the bag-of-words approach. When optimizing a system based on the bag-of-words approach, the goal is to minimize the time it takes to process batches of images. Additionally, we also consider power usage as an evaluation metric. In this paper, we analyze the bag-of-words model for visual categorization in terms of computational cost and identify two major bottlenecks: the quantization step and the classification step. We address these two bottlenecks by proposing two efficient algorithms for quantization and classification by exploiting the GPU hardware and the CUDA parallel programming model. The algorithms are designed to (1) keep categorization accuracy intact, (2) decompose the problem and (3) give the same numerical results. In the experiments on large scale datasets it is shown that, by using a parallel implementation on the Geforce GTX260 GPU, classifying unseen images is 4.8 times faster than a quad-core CPU version on the Core i7 920, while giving the exact same numerical results. In addition, we show how the algorithms can be generalized to other applications, such as text retrieval and video retrieval. Moreover, when the obtained speedup is used to process extra video frames in a video retrieval benchmark, the accuracy of visual categorization is improved by 29%.
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 @ SGS2011b Serial 1729
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Author G.D. Evangelidis; Ferran Diego; Joan Serrat; Antonio Lopez
Title Slice Matching for Accurate Spatio-Temporal Alignment Type Conference Article
Year 2011 Publication In ICCV Workshop on Visual Surveillance Abbreviated Journal
Volume Issue Pages
Keywords video alignment
Abstract (down) Video synchronization and alignment is a rather recent topic in computer vision. It usually deals with the problem of aligning sequences recorded simultaneously by static, jointly- or independently-moving cameras. In this paper, we investigate the more difficult problem of matching videos captured at different times from independently-moving cameras, whose trajectories are approximately coincident or parallel. To this end, we propose a novel method that pixel-wise aligns videos and allows thus to automatically highlight their differences. This primarily aims at visual surveillance but the method can be adopted as is by other related video applications, like object transfer (augmented reality) or high dynamic range video. We build upon a slice matching scheme to first synchronize the sequences, while we develop a spatio-temporal alignment scheme to spatially register corresponding frames and refine the temporal mapping. We investigate the performance of the proposed method on videos recorded from vehicles driven along different types of roads and compare with related previous works.
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 VS
Notes ADAS Approved no
Call Number Admin @ si @ EDS2011; ADAS @ adas @ eds2011a Serial 1861
Permanent link to this record
 
 
Author Ferran Diego
Title Probabilistic Alignment of Video Sequences Recorded by Moving Cameras Type Book Whole
Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract (down) Video alignment consists of integrating multiple video sequences recorded independently into a single video sequence. This means to register both in time (synchronize
frames) and space (image registration) so that the two videos sequences can be fused
or compared pixel–wise. In spite of being relatively unknown, many applications today may benefit from the availability of robust and efficient video alignment methods.
For instance, video surveillance requires to integrate video sequences that are recorded
of the same scene at different times in order to detect changes. The problem of aligning videos has been addressed before, but in the relatively simple cases of fixed or rigidly attached cameras and simultaneous acquisition. In addition, most works rely
on restrictive assumptions which reduce its difficulty such as linear time correspondence or the knowledge of the complete trajectories of corresponding scene points on the images; to some extent, these assumptions limit the practical applicability of the solutions developed until now. In this thesis, we focus on the challenging problem of aligning sequences recorded at different times from independent moving cameras following similar but not coincident trajectories. More precisely, this thesis covers four studies that advance the state-of-the-art in video alignment. First, we focus on analyzing and developing a probabilistic framework for video alignment, that is, a principled way to integrate multiple observations and prior information. In this way, two different approaches are presented to exploit the combination of several purely visual features (image–intensities, visual words and dense motion field descriptor), and
global positioning system (GPS) information. Second, we focus on reformulating the
problem into a single alignment framework since previous works on video alignment
adopt a divide–and–conquer strategy, i.e., first solve the synchronization, and then
register corresponding frames. This also generalizes the ’classic’ case of fixed geometric transform and linear time mapping. Third, we focus on exploiting directly the
time domain of the video sequences in order to avoid exhaustive cross–frame search.
This provides relevant information used for learning the temporal mapping between
pairs of video sequences. Finally, we focus on adapting these methods to the on–line
setting for road detection and vehicle geolocation. The qualitative and quantitative
results presented in this thesis on a variety of real–world pairs of video sequences show that the proposed method is: robust to varying imaging conditions, different image
content (e.g., incoming and outgoing vehicles), variations on camera velocity, and
different scenarios (indoor and outdoor) going beyond the state–of–the–art. Moreover, the on–line video alignment has been successfully applied for road detection and
vehicle geolocation achieving promising results.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Joan Serrat
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ Die2011 Serial 1787
Permanent link to this record
 
 
Author Andreas Fischer; Volkmar Frinken; Alicia Fornes; Horst Bunke
Title Transcription Alignment of Latin Manuscripts Using Hidden Markov Models Type Conference Article
Year 2011 Publication Proceedings of the 2011 Workshop on Historical Document Imaging and Processing Abbreviated Journal
Volume Issue Pages 29-36
Keywords
Abstract (down) Transcriptions of historical documents are a valuable source for extracting labeled handwriting images that can be used for training recognition systems. In this paper, we introduce the Saint Gall database that includes images as well as the transcription of a Latin manuscript from the 9th century written in Carolingian script. Although the available transcription is of high quality for a human reader, the spelling of the words is not accurate when compared with the handwriting image. Hence, the transcription poses several challenges for alignment regarding, e.g., line breaks, abbreviations, and capitalization. We propose an alignment system based on character Hidden Markov Models that can cope with these challenges and efficiently aligns complete document pages. On the Saint Gall database, we demonstrate that a considerable alignment accuracy can be achieved, even with weakly trained character models.
Address
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 Medium
Area Expedition Conference HIP
Notes DAG Approved no
Call Number Admin @ si @ FFF2011b Serial 1824
Permanent link to this record
 
 
Author Volkmar Frinken; Andreas Fischer; Horst Bunke; Alicia Fornes
Title Co-training for Handwritten Word Recognition Type Conference Article
Year 2011 Publication 11th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 314-318
Keywords
Abstract (down) To cope with the tremendous variations of writing styles encountered between different individuals, unconstrained automatic handwriting recognition systems need to be trained on large sets of labeled data. Traditionally, the training data has to be labeled manually, which is a laborious and costly process. Semi-supervised learning techniques offer methods to utilize unlabeled data, which can be obtained cheaply in large amounts in order, to reduce the need for labeled data. In this paper, we propose the use of Co-Training for improving the recognition accuracy of two weakly trained handwriting recognition systems. The first one is based on Recurrent Neural Networks while the second one is based on Hidden Markov Models. On the IAM off-line handwriting database we demonstrate a significant increase of the recognition accuracy can be achieved with Co-Training for single word recognition.
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 Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ FFB2011 Serial 1789
Permanent link to this record
 
 
Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy Type Conference Article
Year 2011 Publication In MICCAI 2011 Workshop on Computational and Clinical Applications in Abdominal Imaging Abbreviated Journal
Volume 6668 Issue Pages 76-83
Keywords
Abstract (down) This work presents a region descriptor based on the integration of the information that the depth of valleys image provides. The depth of valleys image is based on the presence of intensity valleys around polyps due to the image acquisition. Our proposed method consists of defining, for each point, a series of radial sectors around it and then accumulates the maxima of the depth of valleys image only if the orientation of the intensity valley coincides with the orientation of the sector above. We apply our descriptor to a prior segmentation of the images and we present promising results on polyp detection, outperforming other approaches that also integrate depth of valleys information.
Address Toronto, Canada
Corporate Author Thesis
Publisher Springer Link Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference ABI
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BSV2011d Serial 1698
Permanent link to this record
 
 
Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title A Region Segmentation Method for Colonoscopy Images Using a Model of Polyp Appearance Type Conference Article
Year 2011 Publication 5th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal
Volume 6669 Issue Pages 134-143   
Keywords Colonoscopy, Polyp Detection, Region Merging, Region Segmentation.
Abstract (down) This work aims at the segmentation of colonoscopy images into a minimum number of informative regions. Our method performs in a way such, if a polyp is present in the image, it will be exclusively and totally contained in a single region. This result can be used in later stages to classify regions as polyp-containing candidates. The output of the algorithm also defines which regions can be considered as non-informative. The algorithm starts with a high number of initial regions and merges them taking into account the model of polyp appearance obtained from available data. The results show that our segmentations of polyp regions are more accurate than state-of-the-art methods.
Address Las Palmas de Gran Canaria, June 2011
Corporate Author SpringerLink Thesis
Publisher Place of Publication Editor Vitrià, Jordi and Sanches, João and Hernández, Mario
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-21256-7 Medium
Area 800 Expedition Conference IbPRIA
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BSV2011c Serial 1696
Permanent link to this record
 
 
Author Jorge Bernal; F. Javier Sanchez; Fernando Vilariño
Title Depth of Valleys Accumulation Algorithm for Object Detection Type Conference Article
Year 2011 Publication 14th Congrès Català en Intel·ligencia Artificial Abbreviated Journal
Volume 1 Issue 1 Pages 71-80
Keywords Object Recognition, Object Region Identification, Image Analysis, Image Processing
Abstract (down) This work aims at detecting in which regions the objects in the image are by using information about the intensity of valleys, which appear to surround ob- jects in images where the source of light is in the line of direction than the camera. We present our depth of valleys accumulation method, which consists of two stages: first, the definition of the depth of valleys image which combines the output of a ridges and valleys detector with the morphological gradient to measure how deep is a point inside a valley and second, an algorithm that denotes points of the image as interior to objects those which are inside complete or incomplete boundaries in the depth of valleys image. To evaluate the performance of our method we have tested it on several application domains. Our results on object region identification are promising, specially in the field of polyp detection in colonoscopy videos, and we also show its applicability in different areas.
Address Lleida
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 978-1-60750-841-0 Medium
Area 800 Expedition Conference CCIA
Notes MV;SIAI Approved no
Call Number IAM @ iam @ BSV2011b Serial 1699
Permanent link to this record