| |
Records |
Links |
|
Author |
Mikkel Thogersen; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund |
|
| |
Title |
Segmentation of RGB-D Indoor scenes by Stacking Random Forests and Conditional Random Fields |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
80 |
Issue |
|
Pages |
208–215 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper proposes a technique for RGB-D scene segmentation using Multi-class
Multi-scale Stacked Sequential Learning (MMSSL) paradigm. Following recent trends in state-of-the-art, a base classifier uses an initial SLIC segmentation to obtain superpixels which provide a diminution of data while retaining object boundaries. A series of color and depth features are extracted from the superpixels, and are used in a Conditional Random Field (CRF) to predict superpixel labels. Furthermore, a Random Forest (RF) classifier using random offset features is also used as an input to the CRF, acting as an initial prediction. As a stacked classifier, another Random Forest is used acting on a spatial multi-scale decomposition of the CRF confidence map to correct the erroneous labels assigned by the previous classifier. The model is tested on the popular NYU-v2 dataset.
The approach shows that simple multi-modal features with the power of the MMSSL
paradigm can achieve better performance than state of the art results on the same dataset. |
|
| |
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 |
HuPBA; ISE;MILAB; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ TEG2016 |
Serial |
2843 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Garcia-Rodriguez; Isabelle Guyon; Sergio Escalera; Alexandra Psarrou; Andrew Lewis; Miguel Cazorla |
|
| |
Title |
Editorial: Special Issue on Computational Intelligence for Vision and Robotics |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Neural Computing and Applications |
Abbreviated Journal |
Neural Computing and Applications |
|
| |
Volume |
28 |
Issue |
5 |
Pages |
853–854 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
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 |
HuPBA;MILAB; no menciona |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGE2017 |
Serial |
2845 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton |
|
| |
Title |
Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
28 |
Issue |
|
Pages |
1489 - 1491 |
|
| |
Keywords |
|
|
| |
Abstract |
The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others. |
|
| |
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 |
HuPBA; ISE;MV; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
2851 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Marc Oliu; Ciprian Corneanu; Kamal Nasrollahi; Olegs Nikisins; Sergio Escalera; Yunlian Sun; Haiqing Li; Zhenan Sun; Thomas B. Moeslund; Modris Greitans |
|
| |
Title |
Improved RGB-D-T based Face Recognition |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IET Biometrics |
Abbreviated Journal |
BIO |
|
| |
Volume |
5 |
Issue |
4 |
Pages |
297 - 303 |
|
| |
Keywords |
|
|
| |
Abstract |
Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. |
|
| |
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 |
HuPBA;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OCN2016 |
Serial |
2854 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
|
| |
Title |
Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
75 |
Issue |
22 |
Pages |
14985-14990 |
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
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; HUPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DDB2016 |
Serial |
2934 |
|
| Permanent link to this record |
