| |
Records |
Links |
|
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 |
Antonio Hernandez; Sergio Escalera; Stan Sclaroff |
|
| |
Title |
Poselet-basedContextual Rescoring for Human Pose Estimation via Pictorial Structures |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
| |
Volume |
118 |
Issue |
1 |
Pages |
49–64 |
|
| |
Keywords |
Contextual rescoring; Poselets; Human pose estimation |
|
| |
Abstract |
In this paper we propose a contextual rescoring method for predicting the position of body parts in a human pose estimation framework. A set of poselets is incorporated in the model, and their detections are used to extract spatial and score-related features relative to other body part hypotheses. A method is proposed for the automatic discovery of a compact subset of poselets that covers the different poses in a set of validation images while maximizing precision. A rescoring mechanism is defined as a set-based boosting classifier that computes a new score for each body joint detection, given its relationship to detections of other body joints and mid-level parts in the image. This new score is incorporated in the pictorial structure model as an additional unary potential, following the recent work of Pishchulin et al. Experiments on two benchmarks show comparable results to Pishchulin et al. while reducing the size of the mid-level representation by an order of magnitude, reducing the execution time by 68 % accordingly. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer US |
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 |
HuPBA;MILAB; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HES2016 |
Serial |
2719 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
|
| |
Title |
Unsupervised Deep Feature Extraction for Remote Sensing Image Classification |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
IEEE Transaction on Geoscience and Remote Sensing |
Abbreviated Journal |
TGRS |
|
| |
Volume |
54 |
Issue |
3 |
Pages |
1349 - 1362 |
|
| |
Keywords |
|
|
| |
Abstract |
This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy. |
|
| |
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 |
0196-2892 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 600.079;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RGC2016 |
Serial |
2723 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan |
|
| |
Title |
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Journal of Machine Vision and Applications |
Abbreviated Journal |
MVAP |
|
| |
Volume |
27 |
Issue |
|
Pages |
511-527 |
|
| |
Keywords |
particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging |
|
| |
Abstract |
In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art. |
|
| |
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; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DJM2016 |
Serial |
2735 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva |
|
| |
Title |
Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
|
| |
Volume |
149 |
Issue |
|
Pages |
146-156 |
|
| |
Keywords |
|
|
| |
Abstract |
Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness. |
|
| |
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; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ADR2016b |
Serial |
2742 |
|
| Permanent link to this record |
