| |
Records |
Links |
|
Author |
Carlo Gatta; Francesco Ciompi |
|
| |
Title |
Stacked Sequential Scale-Space Taylor Context |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal  |
TPAMI |
|
| |
Volume |
36 |
Issue |
8 |
Pages |
1694-1700 |
|
| |
Keywords |
|
|
| |
Abstract |
We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets. |
|
| |
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 |
0162-8828 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; MILAB; 601.160; 600.079 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GaC2014 |
Serial |
2466 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Adriana Romero; Petia Radeva; Carlo Gatta |
|
| |
Title |
Meta-parameter free unsupervised sparse feature learning |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
37 |
Issue |
8 |
Pages |
1716-1722 |
|
| |
Keywords |
|
|
| |
Abstract |
We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well. |
|
| |
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; 600.068; 600.079; 601.160 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RRG2014b |
Serial |
2594 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera |
|
| |
Title |
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
|
| |
Volume |
28 |
Issue |
8 |
Pages |
1548-1568 |
|
| |
Keywords |
Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal |
|
| |
Abstract |
Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. |
|
| |
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 @ COC2016 |
Serial |
2718 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
|
| |
Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
Type |
Journal Article |
| |
Year |
2011 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
Abbreviated Journal |
TSMCB |
|
| |
Volume |
41 |
Issue |
2 |
Pages |
497-506 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
|
| |
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 |
1083-4419 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
MILAB; DAG;HuPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ EFP2011 |
Serial |
1784 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Oscar Lopes; Miguel Reyes; Sergio Escalera; Jordi Gonzalez |
|
| |
Title |
Spherical Blurred Shape Model for 3-D Object and Pose Recognition: Quantitative Analysis and HCI Applications in Smart Environments |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) |
Abbreviated Journal |
TSMCB |
|
| |
Volume |
44 |
Issue |
12 |
Pages |
2379-2390 |
|
| |
Keywords |
|
|
| |
Abstract |
The use of depth maps is of increasing interest after the advent of cheap multisensor devices based on structured light, such as Kinect. In this context, there is a strong need of powerful 3-D shape descriptors able to generate rich object representations. Although several 3-D descriptors have been already proposed in the literature, the research of discriminative and computationally efficient descriptors is still an open issue. In this paper, we propose a novel point cloud descriptor called spherical blurred shape model (SBSM) that successfully encodes the structure density and local variabilities of an object based on shape voxel distances and a neighborhood propagation strategy. The proposed SBSM is proven to be rotation and scale invariant, robust to noise and occlusions, highly discriminative for multiple categories of complex objects like the human hand, and computationally efficient since the SBSM complexity is linear to the number of object voxels. Experimental evaluation in public depth multiclass object data, 3-D facial expressions data, and a novel hand poses data sets show significant performance improvements in relation to state-of-the-art approaches. Moreover, the effectiveness of the proposal is also proved for object spotting in 3-D scenes and for real-time automatic hand pose recognition in human computer interaction scenarios. |
|
| |
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 |
2168-2267 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA; ISE; 600.078;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LRE2014 |
Serial |
2442 |
|
| Permanent link to this record |
