|
Records |
Links |
|
Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |


|
|
Title |
An overview of incremental feature extraction methods based on linear subspaces |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Knowledge-Based Systems |
Abbreviated Journal |
KBS |
|
|
Volume |
145 |
Issue |
|
Pages |
219-235 |
|
|
Keywords |
|
|
|
Abstract |
With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
|
|
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 |
0950-7051 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes  |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DFH2018 |
Serial |
3090 |
|
Permanent link to this record |
|
|
|
|
Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil |

|
|
Title |
Continuous head pose estimation using manifold subspace embedding and multivariate regression |
Type |
Journal Article |
|
Year |
2018 |
Publication |
IEEE ACCESS |
Abbreviated Journal |
ACCESS |
|
|
Volume |
6 |
Issue |
|
Pages |
18325 - 18334 |
|
|
Keywords |
Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression |
|
|
Abstract |
In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. |
|
|
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 |
2169-3536 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes  |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ DMH2018b |
Serial |
3091 |
|
Permanent link to this record |
|
|
|
|
Author |
Adrien Gaidon; Antonio Lopez; Florent Perronnin |

|
|
Title |
The Reasonable Effectiveness of Synthetic Visual Data |
Type |
Journal Article |
|
Year |
2018 |
Publication |
International Journal of Computer Vision |
Abbreviated Journal |
IJCV |
|
|
Volume |
126 |
Issue |
9 |
Pages |
899–901 |
|
|
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  |
ADAS; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ GLP2018 |
Serial |
3180 |
|
Permanent link to this record |
|
|
|
|
Author |
Jiaolong Xu; Liang Xiao; Antonio Lopez |

|
|
Title |
Self-supervised Domain Adaptation for Computer Vision Tasks |
Type |
Journal Article |
|
Year |
2019 |
Publication |
IEEE ACCESS |
Abbreviated Journal |
ACCESS |
|
|
Volume |
7 |
Issue |
|
Pages |
156694 - 156706 |
|
|
Keywords |
|
|
|
Abstract |
Recent progress of self-supervised visual representation learning has achieved remarkable success on many challenging computer vision benchmarks. However, whether these techniques can be used for domain adaptation has not been explored. In this work, we propose a generic method for self-supervised domain adaptation, using object recognition and semantic segmentation of urban scenes as use cases. Focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction), we assess different learning strategies to improve domain adaptation effectiveness by self-supervision. Additionally, we propose two complementary strategies to further boost the domain adaptation accuracy on semantic segmentation within our method, consisting of prediction layer alignment and batch normalization calibration. The experimental results show adaptation levels comparable to most studied domain adaptation methods, thus, bringing self-supervision as a new alternative for reaching domain adaptation. The code is available at this link. https://github.com/Jiaolong/self-supervised-da. |
|
|
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 @ XXL2019 |
Serial |
3302 |
|
Permanent link to this record |
|
|
|
|
Author |
Zhijie Fang; Antonio Lopez |


|
|
Title |
Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation |
Type |
Journal Article |
|
Year |
2019 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Anticipating the intentions of vulnerable road users (VRUs) such as pedestrians and cyclists is critical for performing safe and comfortable driving maneuvers. This is the case for human driving and, thus, should be taken into account by systems providing any level of driving assistance, from advanced driver assistant systems (ADAS) to fully autonomous vehicles (AVs). In this paper, we show how the latest advances on monocular vision-based human pose estimation, i.e. those relying on deep Convolutional Neural Networks (CNNs), enable to recognize the intentions of such VRUs. In the case of cyclists, we assume that they follow traffic rules to indicate future maneuvers with arm signals. In the case of pedestrians, no indications can be assumed. Instead, we hypothesize that the walking pattern of a pedestrian allows to determine if he/she has the intention of crossing the road in the path of the ego-vehicle, so that the ego-vehicle must maneuver accordingly (e.g. slowing down or stopping). In this paper, we show how the same methodology can be used for recognizing pedestrians and cyclists' intentions. For pedestrians, we perform experiments on the JAAD dataset. For cyclists, we did not found an analogous dataset, thus, we created our own one by acquiring and annotating videos which we share with the research community. Overall, the proposed pipeline provides new state-of-the-art results on the intention recognition of VRUs. |
|
|
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 @ FaL2019 |
Serial |
3305 |
|
Permanent link to this record |
|
|
|
|
Author |
Gabriel Villalonga; Antonio Lopez |


|
|
Title |
Co-Training for On-Board Deep Object Detection |
Type |
Journal Article |
|
Year |
2020 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
|
|
Volume |
|
Issue |
|
Pages |
194441 - 194456 |
|
|
Keywords |
|
|
|
Abstract |
Providing ground truth supervision to train visual models has been a bottleneck over the years, exacerbated by domain shifts which degenerate the performance of such models. This was the case when visual tasks relied on handcrafted features and shallow machine learning and, despite its unprecedented performance gains, the problem remains open within the deep learning paradigm due to its data-hungry nature. Best performing deep vision-based object detectors are trained in a supervised manner by relying on human-labeled bounding boxes which localize class instances (i.e. objects) within the training images. Thus, object detection is one of such tasks for which human labeling is a major bottleneck. In this article, we assess co-training as a semi-supervised learning method for self-labeling objects in unlabeled images, so reducing the human-labeling effort for developing deep object detectors. Our study pays special attention to a scenario involving domain shift; in particular, when we have automatically generated virtual-world images with object bounding boxes and we have real-world images which are unlabeled. Moreover, we are particularly interested in using co-training for deep object detection in the context of driver assistance systems and/or self-driving vehicles. Thus, using well-established datasets and protocols for object detection in these application contexts, we will show how co-training is a paradigm worth to pursue for alleviating object labeling, working both alone and together with task-agnostic domain adaptation. |
|
|
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 @ ViL2020 |
Serial |
3488 |
|
Permanent link to this record |
|
|
|
|
Author |
Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo |

|
|
Title |
Cross-Spectral Local Descriptors via Quadruplet Network |
Type |
Journal Article |
|
Year |
2017 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
|
Volume |
17 |
Issue |
4 |
Pages |
873 |
|
|
Keywords |
|
|
|
Abstract |
This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. |
|
|
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; 600.086; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ ASA2017 |
Serial |
2914 |
|
Permanent link to this record |
|
|
|
|
Author |
Victor Santos; Angel Sappa; Miguel Oliveira |

|
|
Title |
Special Issue on Autonomous Driving and Driver Assistance Systems |
Type |
Journal Article |
|
Year |
2017 |
Publication |
Robotics and Autonomous Systems |
Abbreviated Journal |
RAS |
|
|
Volume |
91 |
Issue |
|
Pages |
208-209 |
|
|
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  |
ADAS; 600.086; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSO2017 |
Serial |
2915 |
|
Permanent link to this record |
|
|
|
|
Author |
Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras |

|
|
Title |
Segmentation of aerial images for plausible detail synthesis |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
|
|
Volume |
71 |
Issue |
|
Pages |
23-34 |
|
|
Keywords |
Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation |
|
|
Abstract |
The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts. |
|
|
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 |
0097-8493 |
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition |
|
Conference |
|
|
|
Notes  |
ADAS; 600.086; 600.118 |
Approved |
no |
|
|
Call Number |
Admin @ si @ ACC2018 |
Serial |
3147 |
|
Permanent link to this record |
|
|
|
|
Author |
Angel Sappa; P. Carvajal; Cristhian A. Aguilera-Carrasco; Miguel Oliveira; Dennis Romero; Boris Vintimilla |


|
|
Title |
Wavelet based visible and infrared image fusion: a comparative study |
Type |
Journal Article |
|
Year |
2016 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
|
Volume |
16 |
Issue |
6 |
Pages |
1-15 |
|
|
Keywords |
Image fusion; fusion evaluation metrics; visible and infrared imaging; discrete wavelet transform |
|
|
Abstract |
This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR). |
|
|
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; 600.086; 600.076 |
Approved |
no |
|
|
Call Number |
Admin @ si @SCA2016 |
Serial |
2807 |
|
Permanent link to this record |