| Records |
| Author |
Murad Al Haj; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
| Title |
Reactive object tracking with a single PTZ camera |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
|
Issue |
|
Pages |
1690–1693 |
| Keywords |
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| Abstract |
In this paper we describe a novel approach to reactive tracking of moving targets with a pan-tilt-zoom camera. The approach uses an extended Kalman filter to jointly track the object position in the real world, its velocity in 3D and the camera intrinsics, in addition to the rate of change of these parameters. The filter outputs are used as inputs to PID controllers which continuously adjust the camera motion in order to reactively track the object at a constant image velocity while simultaneously maintaining a desirable target scale in the image plane. We provide experimental results on simulated and real tracking sequences to show how our tracker is able to accurately estimate both 3D object position and camera intrinsics with very high precision over a wide range of focal lengths. |
| Address |
Istanbul (Turkey) |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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ISSN  |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
ISE |
Approved |
no |
| Call Number |
DAG @ dag @ ABG2010 |
Serial |
1418 |
| Permanent link to this record |
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| Author |
Anjan Dutta; Umapada Pal; Alicia Fornes; Josep Llados |
| Title |
An Efficient Staff Removal Technique from Printed Musical Documents |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1965–1968 |
| Keywords |
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| Abstract |
Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results. |
| Address |
Istanbul (Turkey) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ DPF2010 |
Serial |
1420 |
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| Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny |
| Title |
Symbol Classification using Dynamic Aligned Shape Descriptor |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1957–1960 |
| Keywords |
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| Abstract |
Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. |
| Address |
Istanbul (Turkey) |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG; HUPBA; MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ FEL2010 |
Serial |
1421 |
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| Author |
Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu |
| Title |
Perceptual color texture codebooks for retrieving in highly diverse texture datasets |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
866–869 |
| Keywords |
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| Abstract |
Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel. |
| Address |
Istanbul (Turkey) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
CIC |
Approved |
no |
| Call Number |
CAT @ cat @ ASV2010b |
Serial |
1426 |
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| Author |
Marçal Rusiñol; Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny; Josep Llados |
| Title |
Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1594–1597 |
| Keywords |
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| Abstract |
In this paper we present a method for the retrieval of images in terms of perceptual similarity. Local color information is added to the shape context descriptor in order to obtain an object description integrating both shape and color as visual cues. We use a color naming algorithm in order to represent the color information from a perceptual point of view. The proposed method has been tested in two different applications, an object retrieval scenario based on color sketch queries and a color trademark retrieval problem. Experimental results show that the addition of the color information significantly outperforms the sole use of the shape context descriptor. |
| Address |
Istanbul (Turkey) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RNK2010 |
Serial |
1435 |
| Permanent link to this record |
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| Author |
Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados |
| Title |
A Content Spotting System For Line Drawing Graphic Document Images |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
20 |
Issue |
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Pages |
3420–3423 |
| Keywords |
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| Abstract |
We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository, represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural and electronic documents. |
| Address |
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Place of Publication |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ LBR2010b |
Serial |
1460 |
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| Author |
Albert Gordo; Florent Perronnin |
| Title |
A Bag-of-Pages Approach to Unordered Multi-Page Document Classification |
Type |
Conference Article |
| Year |
2010 |
Publication |
20th International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1920–1923 |
| Keywords |
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| Abstract |
We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system. |
| Address |
Istanbul (Turkey) |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1051-4651 |
ISBN |
978-1-4244-7542-1 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GoP2010 |
Serial |
1480 |
| Permanent link to this record |
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| Author |
Mario Rojas; David Masip; A. Todorov; Jordi Vitria |
| Title |
Automatic Point-based Facial Trait Judgments Evaluation |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
2715–2720 |
| Keywords |
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| Abstract |
Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits. |
| Address |
San Francisco CA, USA |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ RMT2010 |
Serial |
1282 |
| Permanent link to this record |
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| Author |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
| Title |
Harmony Potentials for Joint Classification and Segmentation |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3280–3287 |
| Keywords |
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| Abstract |
Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
| Address |
San Francisco CA, USA |
| Corporate Author |
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Place of Publication |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS;CIC;ISE |
Approved |
no |
| Call Number |
ADAS @ adas @ GBW2010 |
Serial |
1296 |
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| Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
| Title |
3D Scene Priors for Road Detection |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
57–64 |
| Keywords |
road detection |
| Abstract |
Vision-based road detection is important in different areas of computer vision such as autonomous driving, car collision warning and pedestrian crossing detection. However, current vision-based road detection methods are usually based on low-level features and they assume structured roads, road homogeneity, and uniform lighting conditions. Therefore, in this paper, contextual 3D information is used in addition to low-level cues. Low-level photometric invariant cues are derived from the appearance of roads. Contextual cues used include horizon lines, vanishing points, 3D scene layout and 3D road stages. Moreover, temporal road cues are included. All these cues are sensitive to different imaging conditions and hence are considered as weak cues. Therefore, they are combined to improve the overall performance of the algorithm. To this end, the low-level, contextual and temporal cues are combined in a Bayesian framework to classify road sequences. Large scale experiments on road sequences show that the road detection method is robust to varying imaging conditions, road types, and scenarios (tunnels, urban and highway). Further, using the combined cues outperforms all other individual cues. Finally, the proposed method provides highest road detection accuracy when compared to state-of-the-art methods. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
Medium |
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Conference |
CVPR |
| Notes |
ADAS;ISE |
Approved |
no |
| Call Number |
ADAS @ adas @ AGL2010a |
Serial |
1302 |
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| Author |
Mohammad Rouhani; Angel Sappa |
| Title |
Relaxing the 3L Algorithm for an Accurate Implicit Polynomial Fitting |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3066-3072 |
| Keywords |
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| Abstract |
This paper presents a novel method to increase the accuracy of linear fitting of implicit polynomials. The proposed method is based on the 3L algorithm philosophy. The novelty lies on the relaxation of the additional constraints, already imposed by the 3L algorithm. Hence, the accuracy of the final solution is increased due to the proper adjustment of the expected values in the aforementioned additional constraints. Although iterative, the proposed approach solves the fitting problem within a linear framework, which is independent of the threshold tuning. Experimental results, both in 2D and 3D, showing improvements in the accuracy of the fitting are presented. Comparisons with both state of the art algorithms and a geometric based one (non-linear fitting), which is used as a ground truth, are provided. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RoS2010a |
Serial |
1303 |
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| Author |
Javier Marin; David Vazquez; David Geronimo; Antonio Lopez |
| Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
137–144 |
| Keywords |
Pedestrian Detection; Domain Adaptation |
| Abstract |
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers trained with labelled samples. This paper addresses the following question: can a pedestrian appearance model learnt in virtual scenarios work successfully for pedestrian detection in real images? (Fig. 1). Our experiments suggest a positive answer, which is a new and relevant conclusion for research in pedestrian detection. More specifically, we record training sequences in virtual scenarios and then appearance-based pedestrian classifiers are learnt using HOG and linear SVM. We test such classifiers in a publicly available dataset provided by Daimler AG for pedestrian detection benchmarking. This dataset contains real world images acquired from a moving car. The obtained result is compared with the one given by a classifier learnt using samples coming from real images. The comparison reveals that, although virtual samples were not specially selected, both virtual and real based training give rise to classifiers of similar performance. |
| Address |
San Francisco; CA; USA; June 2010 |
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Place of Publication |
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| Language |
English |
Summary Language |
English |
Original Title |
Learning Appearance in Virtual Scenarios for Pedestrian Detection |
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ MVG2010 |
Serial |
1304 |
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| Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
| Title |
Fast and Robust Object Segmentation with the Integral Linear Classifier |
Type |
Conference Article |
| Year |
2010 |
Publication |
23rd IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1046–1053 |
| Keywords |
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| Abstract |
We propose an efficient method, built on the popular Bag of Features approach, that obtains robust multiclass pixel-level object segmentation of an image in less than 500ms, with results comparable or better than most state of the art methods. We introduce the Integral Linear Classifier (ILC), that can readily obtain the classification score for any image sub-window with only 6 additions and 1 product by fusing the accumulation and classification steps in a single operation. In order to design a method as efficient as possible, our building blocks are carefully selected from the quickest in the state of the art. More precisely, we evaluate the performance of three popular local descriptors, that can be very efficiently computed using integral images, and two fast quantization methods: the Hierarchical K-Means, and the Extremely Randomized Forest. Finally, we explore the utility of adding spatial bins to the Bag of Features histograms and that of cascade classifiers to improve the obtained segmentation. Our method is compared to the state of the art in the difficult Graz-02 and PASCAL 2007 Segmentation Challenge datasets. |
| Address |
San Francisco; CA; USA; June 2010 |
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| ISSN |
1063-6919 |
ISBN |
978-1-4244-6984-0 |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ ARL2010a |
Serial |
1311 |
| Permanent link to this record |
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| Author |
David Geronimo; Angel Sappa; Daniel Ponsa; Antonio Lopez |
| Title |
2D-3D based on-board pedestrian detection system |
Type |
Journal Article |
| Year |
2010 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
114 |
Issue |
5 |
Pages |
583–595 |
| Keywords |
Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms |
| Abstract |
During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system. |
| Address |
Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), Vol. 114(5):583-595 |
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Edition |
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| ISSN |
1077-3142 |
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| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ GSP2010 |
Serial |
1341 |
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| Author |
Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
| Title |
Nonlinear synchronization for automatic learning of 3D pose variability in human motion sequences |
Type |
Journal Article |
| Year |
2010 |
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EURASIP Journal on Advances in Signal Processing |
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EURASIPJ |
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Article ID 507247
A dense matching algorithm that solves the problem of synchronizing prerecorded human motion sequences, which show different speeds and accelerations, is proposed. The approach is based on minimization of MRF energy and solves the problem by using Dynamic Programming. Additionally, an optimal sequence is automatically selected from the input dataset to be a time-scale pattern for all other sequences. The paper utilizes an action specific model which automatically learns the variability of 3D human postures observed in a set of training sequences. The model is trained using the public CMU motion capture dataset for the walking action, and a mean walking performance is automatically learnt. Additionally, statistics about the observed variability of the postures and motion direction are also computed at each time step. The synchronized motion sequences are used to learn a model of human motion for action recognition and full-body tracking purposes. |
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1110-8657 |
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ISE |
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ISE @ ise @ MRR2010 |
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1208 |
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