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Author Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell
Title Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer Type Conference Article
Year 2017 Publication 6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging Abbreviated Journal
Volume 10550 Issue Pages 151-159
Keywords
Abstract Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.

The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.

We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.
Address (down) Quebec; Canada; September 2017
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 Conference CLIP
Notes IAM; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ GRM2017 Serial 2957
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Author Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva
Title Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study Type Conference Article
Year 2017 Publication International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) Abbreviated Journal
Volume Issue Pages
Keywords
Abstract An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identication of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores.
Address (down) Quebec; Canada; September 2017
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 Conference MICCAIW
Notes MILAB; no proj Approved no
Call Number Admin @ si @ BCR2017 Serial 2968
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Author Quentin Angermann; Jorge Bernal; Cristina Sanchez Montes; Gloria Fernandez Esparrach; Xavier Gray; Olivier Romain; F. Javier Sanchez; Aymeric Histace
Title Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis Type Conference Article
Year 2017 Publication 4th International Workshop on Computer Assisted and Robotic Endoscopy Abbreviated Journal
Volume Issue Pages 29-41
Keywords Polyp detection; colonoscopy; real time; spatio temporal coherence
Abstract Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our
adaptation strategy.
Address (down) Quebec; Canada; September 2017
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 CARE
Notes MV; 600.096; 600.075 Approved no
Call Number Admin @ si @ ABS2017b Serial 2977
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Author Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo
Title RGBN Multispectral Images: a Novel Color Restoration Approach Type Conference Article
Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal
Volume Issue Pages
Keywords Multispectral Imaging; Free Sensor Model; Neural Network
Abstract This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided.
Address (down) Porto; Portugal; June 2017
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 PAAMS
Notes ADAS; MSIAU; 600.118; 600.122 Approved no
Call Number Admin @ si @ ASS2017 Serial 2918
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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla
Title Learning to Colorize Infrared Images Type Conference Article
Year 2017 Publication 15th International Conference on Practical Applications of Agents and Multi-Agent System Abbreviated Journal
Volume Issue Pages
Keywords CNN in multispectral imaging; Image colorization
Abstract This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.
Address (down) Porto; Portugal; June 2017
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 PAAMS
Notes ADAS; MSIAU; 600.086; 600.122; 600.118 Approved no
Call Number Admin @ si @ Serial 2919
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Author Carles Sanchez; Antonio Esteban Lansaque; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title Towards a Videobronchoscopy Localization System from Airway Centre Tracking Type Conference Article
Year 2017 Publication 12th International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 352-359
Keywords Video-bronchoscopy; Lung cancer diagnosis; Airway lumen detection; Region tracking; Guided bronchoscopy navigation
Abstract Bronchoscopists use fluoroscopy to guide flexible bronchoscopy to the lesion to be biopsied without any kind of incision. Being fluoroscopy an imaging technique based on X-rays, the risk of developmental problems and cancer is increased in those subjects exposed to its application, so minimizing radiation is crucial. Alternative guiding systems such as electromagnetic navigation require specific equipment, increase the cost of the clinical procedure and still require fluoroscopy. In this paper we propose an image based guiding system based on the extraction of airway centres from intra-operative videos. Such anatomical landmarks are matched to the airway centreline extracted from a pre-planned CT to indicate the best path to the nodule. We present a
feasibility study of our navigation system using simulated bronchoscopic videos and a multi-expert validation of landmarks extraction in 3 intra-operative ultrathin explorations.
Address (down) Porto; Portugal; February 2017
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 VISAPP
Notes IAM; 600.096; 600.075; 600.145 Approved no
Call Number Admin @ si @ SEB2017 Serial 2943
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Author Bojana Gajic; Eduard Vazquez; Ramon Baldrich
Title Evaluation of Deep Image Descriptors for Texture Retrieval Type Conference Article
Year 2017 Publication Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) Abbreviated Journal
Volume Issue Pages 251-257
Keywords Texture Representation; Texture Retrieval; Convolutional Neural Networks; Psychophysical Evaluation
Abstract The increasing complexity learnt in the layers of a Convolutional Neural Network has proven to be of great help for the task of classification. The topic has received great attention in recently published literature.
Nonetheless, just a handful of works study low-level representations, commonly associated with lower layers. In this paper, we explore recent findings which conclude, counterintuitively, the last layer of the VGG convolutional network is the best to describe a low-level property such as texture. To shed some light on this issue, we are proposing a psychophysical experiment to evaluate the adequacy of different layers of the VGG network for texture retrieval. Results obtained suggest that, whereas the last convolutional layer is a good choice for a specific task of classification, it might not be the best choice as a texture descriptor, showing a very poor performance on texture retrieval. Intermediate layers show the best performance, showing a good combination of basic filters, as in the primary visual cortex, and also a degree of higher level information to describe more complex textures.
Address (down) Porto, Portugal; 27 February – 1 March 2017
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 VISIGRAPP
Notes CIC; 600.087 Approved no
Call Number Admin @ si @ Serial 3710
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Author Meysam Madadi
Title Human Segmentation, Pose Estimation and Applications Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Automatic analyzing humans in photographs or videos has great potential applications in computer vision, including medical diagnosis, sports, entertainment, movie editing and surveillance, just to name a few. Body, face and hand are the most studied components of humans. Body has many variabilities in shape and clothing along with high degrees of freedom in pose. Face has many muscles causing many visible deformity, beside variable shape and hair style. Hand is a small object, moving fast and has high degrees of freedom. Adding human characteristics to all aforementioned variabilities makes human analysis quite a challenging task.
In this thesis, we developed human segmentation in different modalities. In a first scenario, we segmented human body and hand in depth images using example-based shape warping. We developed a shape descriptor based on shape context and class probabilities of shape regions to extract nearest neighbors. We then considered rigid affine alignment vs. nonrigid iterative shape warping. In a second scenario, we segmented face in RGB images using convolutional neural networks (CNN). We modeled conditional random field with recurrent neural networks. In our model pair-wise kernels are not fixed and learned during training. We trained the network end-to-end using adversarial networks which improved hair segmentation by a high margin.
We also worked on 3D hand pose estimation in depth images. In a generative approach, we fitted a finger model separately for each finger based on our example-based rigid hand segmentation. We minimized an energy function based on overlapping area, depth discrepancy and finger collisions. We also applied linear models in joint trajectory space to refine occluded joints based on visible joints error and invisible joints trajectory smoothness. In a CNN-based approach, we developed a tree-structure network to train specific features for each finger and fused them for global pose consistency. We also formulated physical and appearance constraints as loss functions.
Finally, we developed a number of applications consisting of human soft biometrics measurement and garment retexturing. We also generated some datasets in this thesis consisting of human segmentation, synthetic hand pose, garment retexturing and Italian gestures.
Address (down) October 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Sergio Escalera;Jordi Gonzalez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-3-2 Medium
Area Expedition Conference
Notes HUPBA Approved no
Call Number Admin @ si @ Mad2017 Serial 3017
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Author Arash Akbarinia
Title Computational Model of Visual Perception: From Colour to Form Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The original idea of this project was to study the role of colour in the challenging task of object recognition. We started by extending previous research on colour naming showing that it is feasible to capture colour terms through parsimonious ellipsoids. Although, the results of our model exceeded state-of-the-art in two benchmark datasets, we realised that the two phenomena of metameric lights and colour constancy must be addressed prior to any further colour processing. Our investigation of metameric pairs reached the conclusion that they are infrequent in real world scenarios. Contrary to that, the illumination of a scene often changes dramatically. We addressed this issue by proposing a colour constancy model inspired by the dynamical centre-surround adaptation of neurons in the visual cortex. This was implemented through two overlapping asymmetric Gaussians whose variances and heights are adjusted according to the local contrast of pixels. We complemented this model with a generic contrast-variant pooling mechanism that inversely connect the percentage of pooled signal to the local contrast of a region. The results of our experiments on four benchmark datasets were indeed promising: the proposed model, although simple, outperformed even learning-based approaches in many cases. Encouraged by the success of our contrast-variant surround modulation, we extended this approach to detect boundaries of objects. We proposed an edge detection model based on the first derivative of the Gaussian kernel. We incorporated four types of surround: full, far, iso- and orthogonal-orientation. Furthermore, we accounted for the pooling mechanism at higher cortical areas and the shape feedback sent to lower areas. Our results in three benchmark datasets showed significant improvement over non-learning algorithms.
To summarise, we demonstrated that biologically-inspired models offer promising solutions to computer vision problems, such as, colour naming, colour constancy and edge detection. We believe that the greatest contribution of this Ph.D dissertation is modelling the concept of dynamic surround modulation that shows the significance of contrast-variant surround integration. The models proposed here are grounded on only a portion of what we know about the human visual system. Therefore, it is only natural to complement them accordingly in future works.
Address (down) October 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor C. Alejandro Parraga
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-4-9 Medium
Area Expedition Conference
Notes NEUROBIT Approved no
Call Number Admin @ si @ Akb2017 Serial 3019
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Author Cristhian Aguilera
Title Local feature description in cross-spectral imagery Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Over the last few years, the number of consumer computer vision applications has increased dramatically. Today, computer vision solutions can be found in video game consoles, smartphone applications, driving assistance – just to name a few. Ideally, we require the performance of those applications, particularly those that are safety critical to remain constant under any external environment factors, such as changes in illumination or weather conditions. However, this is not always possible or very difficult to obtain by only using visible imagery, due to the inherent limitations of the images from that spectral band. For that reason, the use of images from different or multiple spectral bands is becoming more appealing.
The aforementioned possible advantages of using images from multiples spectral bands on various vision applications make multi-spectral image processing a relevant topic for research and development. Like in visible image processing, multi-spectral image processing needs tools and algorithms to handle information from various spectral bands. Furthermore, traditional tools such as local feature detection, which is the basis of many vision tasks such as visual odometry, image registration, or structure from motion, must be adjusted or reformulated to operate under new conditions. Traditional feature detection, description, and matching methods tend to underperform in multi-spectral settings, in comparison to mono-spectral settings, due to the natural differences between each spectral band.
The work in this thesis is focused on the local feature description problem when cross-spectral images are considered. In this context, this dissertation has three main contributions. Firstly, the work starts by proposing the usage of a combination of frequency and spatial information, in a multi-scale scheme, as feature description. Evaluations of this proposal, based on classical hand-made feature descriptors, and comparisons with state of the art cross-spectral approaches help to find and understand limitations of such strategy. Secondly, different convolutional neural network (CNN) based architectures are evaluated when used to describe cross-spectral image patches. Results showed that CNN-based methods, designed to work with visible monocular images, could be successfully applied to the description of images from two different spectral bands, with just minor modifications. In this framework, a novel CNN-based network model, specifically intended to describe image patches from two different spectral bands, is proposed. This network, referred to as Q-Net, outperforms state of the art in the cross-spectral domain, including both previous hand-made solutions as well as L2 CNN-based architectures. The third contribution of this dissertation is in the cross-spectral feature description application domain. The multispectral odometry problem is tackled showing a real application of cross-spectral descriptors
In addition to the three main contributions mentioned above, in this dissertation, two different multi-spectral datasets are generated and shared with the community to be used as benchmarks for further studies.
Address (down) October 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Angel Sappa
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-6-3 Medium
Area Expedition Conference
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ Agu2017 Serial 3020
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Author Ivet Rafegas
Title Color in Visual Recognition: from flat to deep representations and some biological parallelisms Type Book Whole
Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.

Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations.

Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias.
Address (down) November 2017
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Maria Vanrell
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-945373-7-0 Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Raf2017 Serial 3100
Permanent link to this record
 
 
Author Laura Lopez-Fuentes; Sebastia Massanet; Manuel Gonzalez-Hidalgo
Title Image vignetting reduction via a maximization of fuzzy entropy Type Conference Article
Year 2017 Publication IEEE International Conference on Fuzzy Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view.
Address (down) Napoles; Italia; July 2017
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 FUZZ-IEEE
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ LMG2017 Serial 2972
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Author Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun
Title CARLA: An Open Urban Driving Simulator Type Conference Article
Year 2017 Publication 1st Annual Conference on Robot Learning. Proceedings of Machine Learning Abbreviated Journal
Volume 78 Issue Pages 1-16
Keywords Autonomous driving; sensorimotor control; simulation
Abstract We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via
reinforcement learning. The approaches are evaluated in controlled scenarios of
increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research.
Address (down) Mountain View; CA; USA; November 2017
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 CORL
Notes ADAS; 600.085; 600.118 Approved no
Call Number Admin @ si @ DRC2017 Serial 2988
Permanent link to this record
 
 
Author Xavier Soria; Angel Sappa; Arash Akbarinia
Title Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities Type Conference Article
Year 2017 Publication 7th International Conference on Image Processing Theory, Tools & Applications Abbreviated Journal
Volume Issue Pages
Keywords Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset
Abstract Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images.
Address (down) Montreal; Canada; November 2017
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 IPTA
Notes NEUROBIT; MSIAU; 600.122 Approved no
Call Number Admin @ si @ SSA2017 Serial 3074
Permanent link to this record
 
 
Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
Title Is there a pattern of Chromosome territoriality along mice spermatogenesis? Type Conference Article
Year 2017 Publication 3rd Spanish MeioNet Meeting Abstract Book Abbreviated Journal
Volume Issue Pages 55-56
Keywords
Abstract
Address (down) Miraflores de la Sierra; Madrid; June 2017
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 MEIONET
Notes IAM; 600.096; 600.145 Approved no
Call Number Admin @ si @ Serial 2958
Permanent link to this record