| |
Records |
Links |
|
Author |
Idoia Ruiz; Joan Serrat |
|
| |
Title |
Hierarchical Novelty Detection for Traffic Sign Recognition |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
22 |
Issue |
12 |
Pages |
4389 |
|
| |
Keywords |
Novelty detection; hierarchical classification; deep learning; traffic sign recognition; autonomous driving; computer vision |
|
| |
Abstract  |
Recent works have made significant progress in novelty detection, i.e., the problem of detecting samples of novel classes, never seen during training, while classifying those that belong to known classes. However, the only information this task provides about novel samples is that they are unknown. In this work, we leverage hierarchical taxonomies of classes to provide informative outputs for samples of novel classes. We predict their closest class in the taxonomy, i.e., its parent class. We address this problem, known as hierarchical novelty detection, by proposing a novel loss, namely Hierarchical Cosine Loss that is designed to learn class prototypes along with an embedding of discriminative features consistent with the taxonomy. We apply it to traffic sign recognition, where we predict the parent class semantics for new types of traffic signs. Our model beats state-of-the art approaches on two large scale traffic sign benchmarks, Mapillary Traffic Sign Dataset (MTSD) and Tsinghua-Tencent 100K (TT100K), and performs similarly on natural images benchmarks (AWA2, CUB). For TT100K and MTSD, our approach is able to detect novel samples at the correct nodes of the hierarchy with 81% and 36% of accuracy, respectively, at 80% known class accuracy. |
|
| |
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; 600.154 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RuS2022 |
Serial |
3684 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Naveen Onkarappa; Angel Sappa |
|
| |
Title |
Synthetic sequences and ground-truth flow field generation for algorithm validation |
Type |
Journal Article |
| |
Year |
2015 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
74 |
Issue |
9 |
Pages |
3121-3135 |
|
| |
Keywords |
Ground-truth optical flow; Synthetic sequence; Algorithm validation |
|
| |
Abstract |
Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems. |
|
| |
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 |
1380-7501 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ADAS; 600.055; 601.215; 600.076 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ OnS2014b |
Serial |
2472 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jose Luis Gomez; Gabriel Villalonga; Antonio Lopez |
|
| |
Title |
Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Sensors – Special Issue on “Machine Learning for Autonomous Driving Perception and Prediction” |
Abbreviated Journal |
SENS |
|
| |
Volume |
23 |
Issue |
2 |
Pages |
621 |
|
| |
Keywords |
Domain adaptation; semi-supervised learning; Semantic segmentation; Autonomous driving |
|
| |
Abstract |
Semantic image segmentation is a central and challenging task in autonomous driving, addressed by training deep models. Since this training draws to a curse of human-based image labeling, using synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies to address an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic
segmentation models. It consists of a self-training stage, which provides two domain-adapted models, and a model collaboration loop for the mutual improvement of these two models. These models are then used to provide the final semantic segmentation labels (pseudo-labels) for the real-world images. The overall
procedure treats the deep models as black boxes and drives their collaboration at the level of pseudo-labeled target images, i.e., neither modifying loss functions is required, nor explicit feature alignment. We test our proposal on standard synthetic and real-world datasets for on-board semantic segmentation. Our
procedure shows improvements ranging from ∼13 to ∼26 mIoU points over baselines, so establishing new state-of-the-art results. |
|
| |
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; no proj |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GVL2023 |
Serial |
3705 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
|
| |
Title |
An iterative multiresolution scheme for SFM with missing data |
Type |
Journal Article |
| |
Year |
2009 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal |
JMIV |
|
| |
Volume |
34 |
Issue |
3 |
Pages |
240–258 |
|
| |
Keywords |
|
|
| |
Abstract |
Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability 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 @ JSL2009a |
Serial |
1163 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta |
|
| |
Title |
Structure-preserving smoothing of biomedical images |
Type |
Journal Article |
| |
Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
|
| |
Volume |
44 |
Issue |
9 |
Pages |
1842-1851 |
|
| |
Keywords |
Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography |
|
| |
Abstract |
Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. |
|
| |
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 |
0031-3203 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
IAM; ADAS |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ GHB2011 |
Serial |
1526 |
|
| Permanent link to this record |
