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Author |
Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Title |
An Application for Efficient Error-Free Labeling of Medical Images |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
Abbreviated Journal |
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Volume |
48 |
Issue |
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Pages |
1-16 |
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Abstract |
In this chapter we describe an application for efficient error-free labeling of medical images. In this scenario, the compilation of a complete training set for building a realistic model of a given class of samples is not an easy task, making the process tedious and time consuming. For this reason, there is a need for interactive labeling applications that minimize the effort of the user while providing error-free labeling. We propose a new algorithm that is based on data similarity in feature space. This method actively explores data in order to find the best label-aligned clustering and exploits it to reduce the labeler effort, that is measured by the number of “clicks. Moreover, error-free labeling is guaranteed by the fact that all data and their labels proposals are visually revised by en expert. |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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MILAB; OR;MV |
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no |
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Admin @ si @ DSR2013 |
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2235 |
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Author |
Marc Castello; Jordi Gonzalez; Ariel Amato; Pau Baiget; Carles Fernandez; Josep M. Gonfaus; Ramon Mollineda; Marco Pedersoli; Nicolas Perez de la Blanca; Xavier Roca |
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Title |
Exploiting Multimodal Interaction Techniques for Video-Surveillance |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications Intelligent Systems Reference Library |
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Volume |
48 |
Issue |
8 |
Pages |
135-151 |
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Abstract |
In this paper we present an example of a video surveillance application that exploits Multimodal Interactive (MI) technologies. The main objective of the so-called VID-Hum prototype was to develop a cognitive artificial system for both the detection and description of a particular set of human behaviours arising from real-world events. The main procedure of the prototype described in this chapter entails: (i) adaptation, since the system adapts itself to the most common behaviours (qualitative data) inferred from tracking (quantitative data) thus being able to recognize abnormal behaviors; (ii) feedback, since an advanced interface based on Natural Language understanding allows end-users the communicationwith the prototype by means of conceptual sentences; and (iii) multimodality, since a virtual avatar has been designed to describe what is happening in the scene, based on those textual interpretations generated by the prototype. Thus, the MI methodology has provided an adequate framework for all these cooperating processes. |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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ISE; 605.203; 600.049 |
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CGA2013 |
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2222 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Interactive Training of Human Detectors |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Multiodal Interaction in Image and Video Applications |
Abbreviated Journal |
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Volume |
48 |
Issue |
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Pages |
169-182 |
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Keywords |
Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation |
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Abstract |
Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations. |
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Springer Heidelberg New York Dordrecht London |
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Springer Berlin Heidelberg |
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English |
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1868-4394 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-35931-6 |
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ADAS; 600.057; 600.054; 605.203 |
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VLP2013; ADAS @ adas @ vlp2013 |
Serial |
2193 |
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Author |
Angel Sappa; Jordi Vitria |
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Title |
Multimodal Interaction in Image and Video Applications |
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Book Whole |
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Year |
2013 |
Publication |
Multimodal Interaction in Image and Video Applications |
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Volume |
48 |
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Abstract |
Book Series Intelligent Systems Reference Library |
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Springer Berlin Heidelberg |
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1868-4394 |
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978-3-642-35931-6 |
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Notes |
ADAS; OR;MV |
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Call Number |
Admin @ si @ SaV2013 |
Serial |
2199 |
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Author |
Joost Van de Weijer; Fahad Shahbaz Khan |
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Title |
Fusing Color and Shape for Bag-of-Words Based Object Recognition |
Type |
Conference Article |
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Year |
2013 |
Publication |
4th Computational Color Imaging Workshop |
Abbreviated Journal |
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Volume |
7786 |
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Pages |
25-34 |
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Keywords |
Object Recognition; color features; bag-of-words; image classification |
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Abstract |
In this article we provide an analysis of existing methods for the incorporation of color in bag-of-words based image representations. We propose a list of desired properties on which bases fusing methods can be compared. We discuss existing methods and indicate shortcomings of the two well-known fusing methods, namely early and late fusion. Several recent works have addressed these shortcomings by exploiting top-down information in the bag-of-words pipeline: color attention which is motivated from human vision, and Portmanteau vocabularies which are based on information theoretic compression of product vocabularies. We point out several remaining challenges in cue fusion and provide directions for future research. |
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Chiba; Japan; March 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-36699-4 |
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CCIW |
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CIC; 600.048 |
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Call Number |
Admin @ si @ WeK2013 |
Serial |
2283 |
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Author |
Anjan Dutta; Josep Llados; Umapada Pal |
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Title |
Bag-of-GraphPaths Descriptors for Symbol Recognition and Spotting in Line Drawings |
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Conference Article |
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Year |
2011 |
Publication |
In proceedings of 9th IAPR Workshop on Graphic Recognition |
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Graphical symbol recognition and spotting recently have become an important research activity. In this work we present a descriptor for symbols, especially for line drawings. The descriptor is based on the graph representation of graphical objects. We construct graphs from the vectorized information of the binarized images, where the critical points detected by the vectorization algorithm are considered as nodes and the lines joining them are considered as edges. Graph paths between two nodes in a graph are the finite sequences of nodes following the order from the starting to the final node. The occurrences of different graph paths in a given graph is an important feature, as they capture the geometrical and structural attributes of a graph. So the graph representing a symbol can efficiently be represent by the occurrences of its different paths. Their occurrences in a symbol can be obtained in terms of a histogram counting the number of some fixed prototype paths, we call the histogram as the Bag-of-GraphPaths (BOGP). These BOGP histograms are used as a descriptor to measure the distance among the symbols in vector space. We use the descriptor for three applications, they are: (1) classification of the graphical symbols, (2) spotting of the architectural symbols on floorplans, (3) classification of the historical handwritten words. |
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Seoul, Korea |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-36823-3 |
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GREC |
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DAG |
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no |
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Call Number |
Admin @ si @ DLP2011c |
Serial |
1825 |
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Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
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Title |
Notation-invariant patch-based wall detector in architectural floor plans |
Type |
Book Chapter |
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Year |
2013 |
Publication |
Graphics Recognition. New Trends and Challenges |
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Volume |
7423 |
Issue |
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Pages |
79--88 |
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Abstract |
Architectural floor plans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed in [1], showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. This paper presents an evolution of that previous work, after analyzing and testing several alternatives for each of the different steps of the method: Firstly, an automatic plan-size normalization process is done. Secondly we evaluate different features to obtain the description of every patch. Thirdly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These variations of the method have been tested for wall detection on two datasets of architectural floor plans with different notations. After studying in deep each of the steps in the process pipeline, we are able to find the best system configuration, which highly outperforms the results on wall segmentation obtained by the original paper. |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-36823-3 |
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Notes |
DAG; 600.045; 600.056; 605.203 |
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no |
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Call Number |
Admin @ si @ HMS2013 |
Serial |
2322 |
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Author |
Ferran Poveda; Debora Gil;Enric Marti |
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Title |
Multi-resolution DT-MRI cardiac tractography |
Type |
Conference Article |
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Year |
2012 |
Publication |
Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges |
Abbreviated Journal |
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Volume |
7746 |
Issue |
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Pages |
270-277 |
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Abstract |
Even using objective measures from DT-MRI no consensus about myocardial architecture has been achieved so far. Streamlining provides good reconstructions at low level of detail, but falls short to give global abstract interpretations. In this paper, we present a multi-resolution methodology that is able to produce simplified representations of cardiac architecture. Our approach produces a reduced set of tracts that are representative of the main geometric features of myocardial anatomical structure. Experiments show that fiber geometry is preserved along reductions, which validates the simplified model for interpretation of cardiac architecture. |
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Address |
Nice, France |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-36960-5 |
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STACOM |
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IAM |
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no |
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Call Number |
IAM @ iam @ PGM2012 |
Serial |
1986 |
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Permanent link to this record |
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Author |
Debora Gil;Agnes Borras;Ruth Aris;Mariano Vazquez;Pierre Lafortune; Guillame Houzeaux |
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Title |
What a difference in biomechanics cardiac fiber makes |
Type |
Conference Article |
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Year |
2012 |
Publication |
Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges |
Abbreviated Journal |
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Volume |
7746 |
Issue |
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Pages |
253-260 |
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Abstract |
Computational simulations of the heart are a powerful tool for a comprehensive understanding of cardiac function and its intrinsic relationship with its muscular architecture. Cardiac biomechanical models require a vector field representing the orientation of cardiac fibers. A wrong orientation of the fibers can lead to a
non-realistic simulation of the heart functionality. In this paper we explore the impact of the fiber information on the simulated biomechanics of cardiac muscular anatomy. We have used the John Hopkins database to perform a biomechanical simulation using both a synthetic benchmark fiber distribution and the data obtained experimentally from DTI. Results illustrate how differences in fiber orientation affect heart deformation along cardiac cycle. |
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Address |
Nice, France |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-36960-5 |
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STACOM |
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IAM |
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no |
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Call Number |
IAM @ iam @ GBA2012 |
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1987 |
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Author |
Simeon Petkov; Adriana Romero; Xavier Carrillo; Petia Radeva; Carlo Gatta |
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Title |
Robust and accurate diaphragm border detection in cardiac X-Ray angiographies |
Type |
Conference Article |
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Year |
2012 |
Publication |
Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges |
Abbreviated Journal |
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Volume |
7746 |
Issue |
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225-234 |
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Abstract |
Workshop STACOM, dins del MICCAI
X-ray angiography is the most common imaging modality employed in the diagnosis of coronary diseases prior to or during a catheter-based intervention. The analysis of the patient X-Ray sequence can provide useful information about the degree of arterial stenosis, the myocardial perfusion and other clinical parameters. If the sequence has been acquired to evaluate the perfusion grade, the opacity due to the diaphragm could potentially hinder any kind of visual inspection and make more difficult a computer aided measurements. In this paper we propose an accurate and robust method to automatically identify the diaphragm border in each frame. Quantitative evaluation on a set of 11 sequences shows that the proposed algorithm outperforms previous methods. |
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Nice, France |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-36960-5 |
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STACOM |
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MILAB |
Approved |
no |
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Call Number |
Admin @ si @ PRC2012 |
Serial |
2028 |
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Permanent link to this record |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
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Title |
Video Co-segmentation |
Type |
Conference Article |
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Year |
2012 |
Publication |
11th Asian Conference on Computer Vision |
Abbreviated Journal |
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Volume |
7725 |
Issue |
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Pages |
13-24 |
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Abstract |
Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos. |
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Daejeon, Korea |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-37443-2 |
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ACCV |
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ADAS |
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no |
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Call Number |
Admin @ si @ RSL2012d |
Serial |
2153 |
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Permanent link to this record |
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Author |
Ariel Amato; Ivan Huerta; Mikhail Mozerov; Xavier Roca; Jordi Gonzalez |
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Title |
Moving Cast Shadows Detection Methods for Video Surveillance Applications |
Type |
Book Chapter |
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Year |
2014 |
Publication |
Augmented Vision and Reality |
Abbreviated Journal |
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Volume |
6 |
Issue |
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23-47 |
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Abstract |
Moving cast shadows are a major concern in today’s performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object (‘shape from shadows’) as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). |
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Springer Berlin Heidelberg |
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2190-5916 |
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978-3-642-37840-9 |
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ISE; 605.203; 600.049; 302.018; 302.012; 600.078 |
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Admin @ si @ AHM2014 |
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2223 |
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Author |
Andreas Fischer; Ching Y. Suen; Volkmar Frinken; Kaspar Riesen; Horst Bunke |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Fast Matching Algorithm for Graph-Based Handwriting Recognition |
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Conference Article |
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2013 |
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9th IAPR – TC15 Workshop on Graph-based Representation in Pattern Recognition |
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7877 |
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194-203 |
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The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy. |
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Vienna; Austria; May 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38220-8 |
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GBR |
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DAG; 600.045; 605.203 |
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Admin @ si @ FSF2013 |
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2294 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Logo recognition Based on the Dempster-Shafer Fusion of Multiple Classifiers |
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Conference Article |
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2013 |
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26th Canadian Conference on Artificial Intelligence |
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7884 |
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1-12 |
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Logo recognition; ensemble classification; Dempster-Shafer fusion; Zernike moments; generic Fourier descriptor; shape signature |
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Best paper award
The performance of different feature extraction and shape description methods in trademark image recognition systems have been studied by several researchers. However, the potential improvement in classification through feature fusion by ensemble-based methods has remained unattended. In this work, we evaluate the performance of an ensemble of three classifiers, each trained on different feature sets. Three promising shape description techniques, including Zernike moments, generic Fourier descriptors, and shape signature are used to extract informative features from logo images, and each set of features is fed into an individual classifier. In order to reduce recognition error, a powerful combination strategy based on the Dempster-Shafer theory is utilized to fuse the three classifiers trained on different sources of information. This combination strategy can effectively make use of diversity of base learners generated with different set of features. The recognition results of the individual classifiers are compared with those obtained from fusing the classifiers’ output, showing significant performance improvements of the proposed methodology. |
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Canada; May 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-38456-1 |
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AI |
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HuPBA;MILAB |
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Admin @ si @ BGE2013b |
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2249 |
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Author |
Francesco Ciompi; Rui Hua; Simone Balocco; Marina Alberti; Oriol Pujol; Carles Caus; J. Mauri; Petia Radeva |
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Title |
Learning to Detect Stent Struts in Intravascular Ultrasound |
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Conference Article |
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2013 |
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6th Iberian Conference on Pattern Recognition and Image Analysis |
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7887 |
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575-583 |
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In this paper we tackle the automatic detection of struts elements (metallic braces of a stent device) in Intravascular Ultrasound (IVUS) sequences. The proposed method is based on context-aware classification of IVUS images, where we use Multi-Class Multi-Scale Stacked Sequential Learning (M2SSL). Additionally, we introduce a novel technique to reduce the amount of required contextual features. The comparison with binary and multi-class learning is also performed, using a dataset of IVUS images with struts manually annotated by an expert. The best performing configuration reaches a F-measure F = 63.97% . |
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Madeira; Portugal; June 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
ISBN ![sorted by ISBN field, ascending order (up)](img/sort_asc.gif) |
978-3-642-38627-5 |
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IbPRIA |
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MILAB; HuPBA; 605.203; 600.046 |
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Admin @ si @ CHB2013 |
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2349 |
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