| |
Records |
Links |
|
Author |
Xavier Soria; Angel Sappa; Riad I. Hammoud |
|
| |
Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
18 |
Issue  |
7 |
Pages |
2059 |
|
| |
Keywords |
RGB-NIR sensor; multispectral imaging; deep learning; CNNs |
|
| |
Abstract |
Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
|
| |
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 |
ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SSH2018 |
Serial |
3145 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta |
|
| |
Title |
Semantic Pyramids for Gender and Action Recognition |
Type |
Journal Article |
| |
Year |
2014 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
TIP |
|
| |
Volume |
23 |
Issue |
8 |
Pages |
3633-3645 |
|
| |
Keywords |
|
|
| |
Abstract |
Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition. |
|
| |
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 |
1057-7149 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
CIC; LAMP; 601.160; 600.074; 600.079;MILAB;ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KWR2014 |
Serial |
2507 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Katerine Diaz; Francesc J. Ferri; W. Diaz |
|
| |
Title |
Incremental Generalized Discriminative Common Vectors for Image Classification |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
IEEE Transactions on Neural Networks and Learning Systems |
Abbreviated Journal |
TNNLS |
|
| |
Volume |
26 |
Issue |
8 |
Pages |
1761 - 1775 |
|
| |
Keywords |
|
|
| |
Abstract |
Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. |
|
| |
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 |
2162-237X |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ DFD2015 |
Serial |
2547 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Akhil Gurram; Ahmet Faruk Tuna; Fengyi Shen; Onay Urfalioglu; Antonio Lopez |
|
| |
Title |
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
|
| |
Volume |
23 |
Issue |
8 |
Pages |
12738-12751 |
|
| |
Keywords |
|
|
| |
Abstract |
Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences. |
|
| |
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 |
ADAS; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GTS2021 |
Serial |
3598 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Fadi Dornaika; Angel Sappa |
|
| |
Title |
A Featureless and Stochastic Approach to On-board Stereo Vision System Pose |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
|
| |
Volume |
27 |
Issue |
9 |
Pages |
1382–1393 |
|
| |
Keywords |
On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping |
|
| |
Abstract |
This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach. |
|
| |
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 |
ADAS |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ DoS2009b |
Serial |
1152 |
|
| Permanent link to this record |
