| |
Records |
Links |
|
Author |
Dani Rowe; Jordi Gonzalez; Marco Pedersoli; Juan J. Villanueva |
|
| |
Title |
On Tracking Inside Groups |
Type |
Journal Article |
| |
Year |
2010 |
Publication |
Machine Vision and Applications |
Abbreviated Journal |
MVA |
|
| |
Volume |
21 |
Issue |
2 |
Pages  |
113–127 |
|
| |
Keywords |
|
|
| |
Abstract |
This work develops a new architecture for multiple-target tracking in unconstrained dynamic scenes, which consists of a detection level which feeds a two-stage tracking system. A remarkable characteristic of the system is its ability to track several targets while they group and split, without using 3D information. Thus, special attention is given to the feature-selection and appearance-computation modules, and to those modules involved in tracking through groups. The system aims to work as a stand-alone application in complex and dynamic scenarios. No a-priori knowledge about either the scene or the targets, based on a previous training period, is used. Hence, the scenario is completely unknown beforehand. Successful tracking has been demonstrated in well-known databases of both indoor and outdoor scenarios. Accurate and robust localisations have been yielded during long-term target merging and occlusions. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer-Verlag |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0932-8092 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE |
Approved |
no |
|
| |
Call Number |
ISE @ ise @ RGP2010 |
Serial |
1158 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
|
| |
Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
Expert Systems |
Abbreviated Journal |
EXSY |
|
| |
Volume |
30 |
Issue |
2 |
Pages |
101-114 |
|
| |
Keywords |
Human action recognition;body-part detection;hidden Markov model |
|
| |
Abstract |
This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
|
| |
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 @ CBG2013 |
Serial |
1809 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Josep M. Gonfaus; Marco Pedersoli; Jordi Gonzalez; Andrea Vedaldi; Xavier Roca |
|
| |
Title |
Factorized appearances for object detection |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
|
| |
Volume |
138 |
Issue |
|
Pages |
92–101 |
|
| |
Keywords |
Object recognition; Deformable part models; Learning and sharing parts; Discovering discriminative parts |
|
| |
Abstract |
Deformable object models capture variations in an object’s appearance that can be represented as image deformations. Other effects such as out-of-plane rotations, three-dimensional articulations, and self-occlusions are often captured by considering mixture of deformable models, one per object aspect. A more scalable approach is representing instead the variations at the level of the object parts, applying the concept of a mixture locally. Combining a few part variations can in fact cheaply generate a large number of global appearances.
A limited version of this idea was proposed by Yang and Ramanan [1], for human pose dectection. In this paper we apply it to the task of generic object category detection and extend it in several ways. First, we propose a model for the relationship between part appearances more general than the tree of Yang and Ramanan [1], which is more suitable for generic categories. Second, we treat part locations as well as their appearance as latent variables so that training does not need part annotations but only the object bounding boxes. Third, we modify the weakly-supervised learning of Felzenszwalb et al. and Girshick et al. [2], [3] to handle a significantly more complex latent structure.
Our model is evaluated on standard object detection benchmarks and is found to improve over existing approaches, yielding state-of-the-art results for several object categories. |
|
| |
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; 600.063; 600.078 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GPG2015 |
Serial |
2705 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Enric Marti; Jordi Regincos;Jaime Lopez-Krahe; Juan J.Villanueva |
|
| |
Title |
Hand line drawing interpretation as three-dimensional objects |
Type |
Journal Article |
| |
Year |
1993 |
Publication |
Signal Processing – Intelligent systems for signal and image understanding |
Abbreviated Journal |
|
|
| |
Volume |
32 |
Issue |
1-2 |
Pages |
91-110 |
|
| |
Keywords |
Line drawing interpretation; line labelling; scene analysis; man-machine interaction; CAD input; line extraction |
|
| |
Abstract |
In this paper we present a technique to interpret hand line drawings as objects in a three-dimensional space. The object domain considered is based on planar surfaces with straight edges, concretely, on ansextension of Origami world to hidden lines. The line drawing represents the object under orthographic projection and it is sensed using a scanner. Our method is structured in two modules: feature extraction and feature interpretation. In the first one, image processing techniques are applied under certain tolerance margins to detect lines and junctions on the hand line drawing. Feature interpretation module is founded on line labelling techniques using a labelled junction dictionary. A labelling algorithm is here proposed. It uses relaxation techniques to reduce the number of incompatible labels with the junction dictionary so that the convergence of solutions can be accelerated. We formulate some labelling hypotheses tending to eliminate elements in two sets of labelled interpretations. That is, those which are compatible with the dictionary but do not correspond to three-dimensional objects and those which represent objects not very probable to be specified by means of a line drawing. New entities arise on the line drawing as a result of the extension of Origami world. These are defined to enunciate the assumptions of our method as well as to clarify the algorithms proposed. This technique is framed in a project aimed to implement a system to create 3D objects to improve man-machine interaction in CAD systems. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier North-Holland, Inc. |
Place of Publication |
Amsterdam, The Netherlands, The Netherlands |
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0165-1684 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
IAM;ISE; |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ MRL1993 |
Serial |
1611 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
|
| |
Title |
Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
| |
Volume |
96 |
Issue |
1 |
Pages |
83-102 |
|
| |
Keywords |
|
|
| |
Abstract |
The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21. |
|
| |
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 |
0920-5691 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE;CIC;ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BGW2012 |
Serial |
1718 |
|
| Permanent link to this record |
