|
Records |
Links |
|
Author |
Alejandro Cartas; Estefania Talavera; Petia Radeva; Mariella Dimiccoli |
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
On the Role of Event Boundaries in Egocentric Activity Recognition from Photostreams |
Type |
Miscellaneous |
|
Year |
2018 |
Publication |
Arxiv |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
|
|
|
Abstract |
Event boundaries play a crucial role as a pre-processing step for detection, localization, and recognition tasks of human activities in videos. Typically, although their intrinsic subjectiveness, temporal bounds are provided manually as input for training action recognition algorithms. However, their role for activity recognition in the domain of egocentric photostreams has been so far neglected. In this paper, we provide insights of how automatically computed boundaries can impact activity recognition results in the emerging domain of egocentric photostreams. Furthermore, we collected a new annotated dataset acquired by 15 people by a wearable photo-camera and we used it to show the generalization capabilities of several deep learning based architectures to unseen users. |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ CTR2018 |
Serial |
3184 |
|
Permanent link to this record |
|
|
|
|
Author |
Md. Mostafa Kamal Sarker; Hatem A. Rashwan; Hatem A. Rashwan; Estefania Talavera; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
MACNet: Multi-scale Atrous Convolution Networks for Food Places Classification in Egocentric Photo-streams |
Type |
Conference Article |
|
Year |
2018 |
Publication |
European Conference on Computer Vision workshops |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
423-433 |
|
|
Keywords |
|
|
|
Abstract |
First-person (wearable) camera continually captures unscripted interactions of the camera user with objects, people, and scenes reflecting his personal and relational tendencies. One of the preferences of people is their interaction with food events. The regulation of food intake and its duration has a great importance to protect against diseases. Consequently, this work aims to develop a smart model that is able to determine the recurrences of a person on food places during a day. This model is based on a deep end-to-end model for automatic food places recognition by analyzing egocentric photo-streams. In this paper, we apply multi-scale Atrous convolution networks to extract the key features related to food places of the input images. The proposed model is evaluated on an in-house private dataset called “EgoFoodPlaces”. Experimental results shows promising results of food places classification recognition in egocentric photo-streams. |
|
|
Address |
|
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LCNS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
ECCVW |
|
|
Notes |
MILAB; no menciona |
Approved |
no |
|
|
Call Number |
Admin @ si @ SRR2018b |
Serial |
3185 |
|
Permanent link to this record |
|
|
|
|
Author |
Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Batch-based activity recognition from egocentric photo-streams revisited |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Pattern Analysis and Applications |
Abbreviated Journal |
PAA |
|
|
Volume |
21 |
Issue |
4 |
Pages |
953–965 |
|
|
Keywords |
Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks |
|
|
Abstract |
Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ CMR2018 |
Serial |
3186 |
|
Permanent link to this record |
|
|
|
|
Author |
Mariella Dimiccoli; Cathal Gurrin; David J. Crandall; Xavier Giro; Petia Radeva |
![goto web page url](img/www.gif)
|
|
Title |
Introduction to the special issue: Egocentric Vision and Lifelogging |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Journal of Visual Communication and Image Representation |
Abbreviated Journal |
JVCIR |
|
|
Volume |
55 |
Issue |
|
Pages |
352-353 |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ DGC2018 |
Serial |
3187 |
|
Permanent link to this record |
|
|
|
|
Author |
Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri |
![goto web page url](img/www.gif)
|
|
Title |
Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review. |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Computers in Biology and Medicine |
Abbreviated Journal |
CBM |
|
|
Volume |
101 |
Issue |
|
Pages |
184-198 |
|
|
Keywords |
Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification |
|
|
Abstract |
Purpose of review
Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins.
Recent finding
Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes. |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
MILAB; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ BLA2018 |
Serial |
3188 |
|
Permanent link to this record |
|
|
|
|
Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Automatic Verification of Properly Signed Multi-page Document Images |
Type |
Conference Article |
|
Year |
2015 |
Publication |
Proceedings of the Eleventh International Symposium on Visual Computing |
Abbreviated Journal |
|
|
|
Volume |
9475 |
Issue |
|
Pages |
327-336 |
|
|
Keywords |
Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
|
|
Abstract |
In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
|
|
Address |
Las Vegas, Nevada, USA; December 2015 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
9475 |
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
ISVC |
|
|
Notes |
DAG; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ |
Serial |
3189 |
|
Permanent link to this record |
|
|
|
|
Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
![goto web page url](img/www.gif)
|
|
Title |
A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
Type |
Journal |
|
Year |
2014 |
Publication |
Manuscript Cultures |
Abbreviated Journal |
|
|
|
Volume |
7 |
Issue |
|
Pages |
47-58 |
|
|
Keywords |
|
|
|
Abstract |
With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline. |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
DAG; 600.061; 600.077 |
Approved |
no |
|
|
Call Number |
Admin @ si @ |
Serial |
3190 |
|
Permanent link to this record |
|
|
|
|
Author |
Cristhian A. Aguilera-Carrasco; C. Aguilera; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Melamine Faced Panels Defect Classification beyond the Visible Spectrum |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
|
Volume |
18 |
Issue |
11 |
Pages |
1-10 |
|
|
Keywords |
industrial application; infrared; machine learning |
|
|
Abstract |
In this work, we explore the use of images from different spectral bands to classify defects in melamine faced panels, which could appear through the production process. Through experimental evaluation, we evaluate the use of images from the visible (VS), near-infrared (NIR), and long wavelength infrared (LWIR), to classify the defects using a feature descriptor learning approach together with a support vector machine classifier. Two descriptors were evaluated, Extended Local Binary Patterns (E-LBP) and SURF using a Bag of Words (BoW) representation. The evaluation was carried on with an image set obtained during this work, which contained five different defect categories that currently occurs in the industry. Results show that using images from beyond the visual spectrum helps to improve classification performance in contrast with a single visible spectrum solution. |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
MSIAU; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ AAS2018 |
Serial |
3191 |
|
Permanent link to this record |
|
|
|
|
Author |
Xavier Soria; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Improving Edge Detection in RGB Images by Adding NIR Channel |
Type |
Conference Article |
|
Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Edge detection; Contour detection; VGG; CNN; RGB-NIR; Near infrared images |
|
|
Abstract |
The edge detection is yet a critical problem in many computer vision and image processing tasks. The manuscript presents an Holistically-Nested Edge Detection based approach to study the inclusion of Near-Infrared in the Visible spectrum
images. To do so, a Single Sensor based dataset has been acquired in the range of 400nm to 1100nm wavelength spectral band. Prominent results have been obtained even when the ground truth (annotated edge-map) is based in the visible wavelength spectrum. |
|
|
Address |
Las Palmas de Gran Canaria; November 2018 |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
SITIS |
|
|
Notes |
MSIAU; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SoS2018 |
Serial |
3192 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Cross-spectral image dehaze through a dense stacked conditional GAN based approach |
Type |
Conference Article |
|
Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Infrared imaging; Dense; Stacked CGAN; Crossspectral; Convolutional networks |
|
|
Abstract |
This paper proposes a novel approach to remove haze from RGB images using a near infrared images based on a dense stacked conditional Generative Adversarial Network (CGAN). The architecture of the deep network implemented
receives, besides the images with haze, its corresponding image in the near infrared spectrum, which serve to accelerate the learning process of the details of the characteristics of the images. The model uses a triplet layer that allows the independence learning of each channel of the visible spectrum image to remove the haze on each color channel separately. A multiple loss function scheme is proposed, which ensures balanced learning between the colors
and the structure of the images. Experimental results have shown that the proposed method effectively removes the haze from the images. Additionally, the proposed approach is compared with a state of the art approach showing better results. |
|
|
Address |
Las Palmas de Gran Canaria; November 2018 |
|
|
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 |
978-1-5386-9385-8 |
Medium |
|
|
|
Area |
|
Expedition ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
SITIS |
|
|
Notes |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSV2018a |
Serial |
3193 |
|
Permanent link to this record |
|
|
|
|
Author |
Jorge Charco; Boris X. Vintimilla; Angel Sappa |
![download PDF file pdf](img/file_PDF.gif)
|
|
Title |
Deep learning based camera pose estimation in multi-view environment |
Type |
Conference Article |
|
Year |
2018 |
Publication |
14th IEEE International Conference on Signal Image Technology & Internet Based System |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Deep learning; Camera pose estimation; Multiview environment; Siamese architecture |
|
|
Abstract |
This paper proposes to use a deep learning network architecture for relative camera pose estimation on a multi-view environment. The proposed network is a variant architecture of AlexNet to use as regressor for prediction the relative translation and rotation as output. The proposed approach is trained from
scratch on a large data set that takes as input a pair of imagesfrom the same scene. This new architecture is compared with a previous approach using standard metrics, obtaining better results on the relative camera pose. |
|
|
Address |
Las Palmas de Gran Canaria; November 2018 |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
SITIS |
|
|
Notes |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ CVS2018 |
Serial |
3194 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Near InfraRed Imagery Colorization |
Type |
Conference Article |
|
Year |
2018 |
Publication |
25th International Conference on Image Processing |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
2237 - 2241 |
|
|
Keywords |
Convolutional Neural Networks (CNN), Generative Adversarial Network (GAN), Infrared Imagery colorization |
|
|
Abstract |
This paper proposes a stacked conditional Generative Adversarial Network-based method for Near InfraRed (NIR) imagery colorization. We propose a variant architecture of Generative Adversarial Network (GAN) that uses multiple
loss functions over a conditional probabilistic generative model. We show that this new architecture/loss-function yields better generalization and representation of the generated colored IR images. The proposed approach is evaluated on a large test dataset and compared to recent state of the art methods using standard metrics. |
|
|
Address |
Athens; Greece; October 2018 |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
ICIP |
|
|
Notes |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSV2018b |
Serial |
3195 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Vegetation Index Estimation from Monospectral Images |
Type |
Conference Article |
|
Year |
2018 |
Publication |
15th International Conference on Images Analysis and Recognition |
Abbreviated Journal |
|
|
|
Volume |
10882 |
Issue |
|
Pages |
353-362 |
|
|
Keywords |
|
|
|
Abstract |
This paper proposes a novel approach to estimate Normalized Difference Vegetation Index (NDVI) from just the red channel of a RGB image. The NDVI index is defined as the ratio of the difference of the red and infrared radiances over their sum. In other words, information from the red channel of a RGB image and the corresponding infrared spectral band are required for its computation. In the current work the NDVI index is estimated just from the red channel by training a Conditional Generative Adversarial Network (CGAN). The architecture proposed for the generative network consists of a single level structure, which combines at the final layer results from convolutional operations together with the given red channel with Gaussian noise to enhance
details, resulting in a sharp NDVI image. Then, the discriminative model
estimates the probability that the NDVI generated index came from the training dataset, rather than the index automatically generated. Experimental results with a large set of real images are provided showing that a Conditional GAN single level model represents an acceptable approach to estimate NDVI index. |
|
|
Address |
Povoa de Varzim; Portugal; June 2018 |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
|
Place of Publication |
|
Editor |
|
|
|
Language |
|
Summary Language |
|
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
|
ISBN |
|
Medium |
|
|
|
Area |
|
Expedition ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
ICIAR |
|
|
Notes |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSV2018c |
Serial |
3196 |
|
Permanent link to this record |
|
|
|
|
Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud |
![download PDF file pdf](img/file_PDF.gif)
![find record details (via OpenURL) openurl](img/xref.gif)
|
|
Title |
Deep Learning based Single Image Dehazing |
Type |
Conference Article |
|
Year |
2018 |
Publication |
31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop |
Abbreviated Journal |
|
|
|
Volume |
|
Issue |
|
Pages |
1250 - 12507 |
|
|
Keywords |
Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis |
|
|
Abstract |
This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear
images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
|
|
Address |
Salt Lake City; USA; June 2018 |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
CVPRW |
|
|
Notes |
MSIAU; 600.086; 600.130; 600.122 |
Approved |
no |
|
|
Call Number |
Admin @ si @ SSV2018d |
Serial |
3197 |
|
Permanent link to this record |
|
|
|
|
Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
![goto web page (via DOI) doi](img/doi.gif)
|
|
Title |
Multi-Modal Deep Hand Sign Language Recognition in Still Images Using Restricted Boltzmann Machine |
Type |
Journal Article |
|
Year |
2018 |
Publication |
Entropy |
Abbreviated Journal |
ENTROPY |
|
|
Volume |
20 |
Issue |
11 |
Pages |
809 |
|
|
Keywords |
hand sign language; deep learning; restricted Boltzmann machine (RBM); multi-modal; profoundly deaf; noisy image |
|
|
Abstract |
In this paper, a deep learning approach, Restricted Boltzmann Machine (RBM), is used to perform automatic hand sign language recognition from visual data. We evaluate how RBM, as a deep generative model, is capable of generating the distribution of the input data for an enhanced recognition of unseen data. Two modalities, RGB and Depth, are considered in the model input in three forms: original image, cropped image, and noisy cropped image. Five crops of the input image are used and the hand of these cropped images are detected using Convolutional Neural Network (CNN). After that, three types of the detected hand images are generated for each modality and input to RBMs. The outputs of the RBMs for two modalities are fused in another RBM in order to recognize the output sign label of the input image. The proposed multi-modal model is trained on all and part of the American alphabet and digits of four publicly available datasets. We also evaluate the robustness of the proposal against noise. Experimental results show that the proposed multi-modal model, using crops and the RBM fusing methodology, achieves state-of-the-art results on Massey University Gesture Dataset 2012, American Sign Language (ASL). and Fingerspelling Dataset from the University of Surrey’s Center for Vision, Speech and Signal Processing, NYU, and ASL Fingerspelling A datasets. |
|
|
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 ![sorted by Expedition field, ascending order (up)](img/sort_asc.gif) |
|
Conference |
|
|
|
Notes |
HUPBA; no proj |
Approved |
no |
|
|
Call Number |
Admin @ si @ RKE2018 |
Serial |
3198 |
|
Permanent link to this record |