| |
Records |
Links |
|
Author |
Joan Serrat; Felipe Lumbreras; Idoia Ruiz |
|
| |
Title |
Learning to measure for preshipment garment sizing |
Type |
Journal Article |
| |
Year  |
2018 |
Publication |
Measurement |
Abbreviated Journal |
MEASURE |
|
| |
Volume |
130 |
Issue |
|
Pages |
327-339 |
|
| |
Keywords |
Apparel; Computer vision; Structured prediction; Regression |
|
| |
Abstract |
Clothing is still manually manufactured for the most part nowadays, resulting in discrepancies between nominal and real dimensions, and potentially ill-fitting garments. Hence, it is common in the apparel industry to manually perform measures at preshipment time. We present an automatic method to obtain such measures from a single image of a garment that speeds up this task. It is generic and extensible in the sense that it does not depend explicitly on the garment shape or type. Instead, it learns through a probabilistic graphical model to identify the different contour parts. Subsequently, a set of Lasso regressors, one per desired measure, can predict the actual values of the measures. We present results on a dataset of 130 images of jackets and 98 of pants, of varying sizes and styles, obtaining 1.17 and 1.22 cm of mean absolute error, respectively. |
|
| |
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; 600.122; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SLR2018 |
Serial |
3128 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
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 |
MSIAU; 600.086; 600.118;ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ ACC2018 |
Serial |
3147 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Matthieu Molinier; Jorma Laaksonen |
|
| |
Title |
Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
ISPRS Journal of Photogrammetry and Remote Sensing |
Abbreviated Journal |
ISPRS J |
|
| |
Volume |
138 |
Issue |
|
Pages |
74-85 |
|
| |
Keywords |
Remote sensing; Deep learning; Scene classification; Local Binary Patterns; Texture analysis |
|
| |
Abstract |
Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene |
|
| |
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 |
LAMP; 600.109; 600.106; 600.120;CIC;ADAS |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RKW2018 |
Serial |
3158 |
|
| 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 |
Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores |
|
| |
Title |
On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
IEEE Transactions on cybernetics |
Abbreviated Journal |
Cyber |
|
| |
Volume |
47 |
Issue |
11 |
Pages |
3980 - 3990 |
|
| |
Keywords |
Multicue; multimodal; multiview; object detection |
|
| |
Abstract |
Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. |
|
| |
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 |
ADAS; 600.085; 600.082; 600.076; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
2810 |
|
| 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 |
David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
|
| |
Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Journal of Healthcare Engineering |
Abbreviated Journal |
JHCE |
|
| |
Volume |
|
Issue |
|
Pages |
2040-2295 |
|
| |
Keywords |
Colonoscopy images; Deep Learning; Semantic Segmentation |
|
| |
Abstract |
Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. |
|
| |
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; MV; 600.075; 600.085; 600.076; 601.281; 600.118;MILAB |
Approved |
no |
|
| |
Call Number |
VBS2017b |
Serial |
2940 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Joan Serrat; Felipe Lumbreras; Francisco Blanco; Manuel Valiente; Montserrat Lopez-Mesas |
|
| |
Title |
myStone: A system for automatic kidney stone classification |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
ESA |
|
| |
Volume |
89 |
Issue |
|
Pages |
41-51 |
|
| |
Keywords |
Kidney stone; Optical device; Computer vision; Image classification |
|
| |
Abstract |
Kidney stone formation is a common disease and the incidence rate is constantly increasing worldwide. It has been shown that the classification of kidney stones can lead to an important reduction of the recurrence rate. The classification of kidney stones by human experts on the basis of certain visual color and texture features is one of the most employed techniques. However, the knowledge of how to analyze kidney stones is not widespread, and the experts learn only after being trained on a large number of samples of the different classes. In this paper we describe a new device specifically designed for capturing images of expelled kidney stones, and a method to learn and apply the experts knowledge with regard to their classification. We show that with off the shelf components, a carefully selected set of features and a state of the art classifier it is possible to automate this difficult task to a good degree. We report results on a collection of 454 kidney stones, achieving an overall accuracy of 63% for a set of eight classes covering almost all of the kidney stones taxonomy. Moreover, for more than 80% of samples the real class is the first or the second most probable class according to the system, being then the patient recommendations for the two top classes similar. This is the first attempt towards the automatic visual classification of kidney stones, and based on the current results we foresee better accuracies with the increase of the dataset size. |
|
| |
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; 603.046; 600.122; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SLB2017 |
Serial |
3026 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Zhijie Fang; David Vazquez; Antonio Lopez |
|
| |
Title |
On-Board Detection of Pedestrian Intentions |
Type |
Journal Article |
| |
Year |
2017 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
17 |
Issue |
10 |
Pages |
2193 |
|
| |
Keywords |
pedestrian intention; ADAS; self-driving |
|
| |
Abstract |
Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role.
During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors.
However, detection is just the first step towards answering the core question, namely is the vehicle going to crash with a pedestrian provided preventive actions are not taken? Therefore, knowing as soon as possible if a detected pedestrian has the intention of crossing the road ahead of the vehicle is
essential for performing safe and comfortable maneuvers that prevent a crash. However, compared to pedestrian detection, there is relatively little literature on detecting pedestrian intentions. This paper aims to contribute along this line by presenting a new vision-based approach which analyzes the
pose of a pedestrian along several frames to determine if he or she is going to enter the road or not. We present experiments showing 750 ms of anticipation for pedestrians crossing the road, which at a typical urban driving speed of 50 km/h can provide 15 additional meters (compared to a pure pedestrian detector) for vehicle automatic reactions or to warn the driver. Moreover, in contrast with state-of-the-art methods, our approach is monocular, neither requiring stereo nor optical flow information. |
|
| |
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; 600.085; 600.076; 601.223; 600.116; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FVL2017 |
Serial |
2983 |
|
| Permanent link to this record |
