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Author Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell
Title Color-based data augmentation for Reflectance Estimation Type Conference Article
Year 2018 Publication 26th Color Imaging Conference Abbreviated Journal
Volume Issue Pages 284-289
Keywords
Abstract Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods.
Address Vancouver; November 2018
Corporate Author Thesis
Publisher Place of Publication Editor
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ISSN ISBN Medium
Area Expedition Conference CIC
Notes CIC Approved no
Call Number Admin @ si @ SSB2018a Serial 3129
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Author Yaxing Wang; Chenshen Wu; Luis Herranz; Joost Van de Weijer; Abel Gonzalez-Garcia; Bogdan Raducanu
Title Transferring GANs: generating images from limited data Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11210 Issue Pages 220-236
Keywords Generative adversarial networks; Transfer learning; Domain adaptation; Image generation
Abstract ransferring knowledge of pre-trained networks to new domains by means of fine-tuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pre-trained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pre-trained model was trained without conditioning. Our results also suggest that density is more important than diversity and a dataset with one or few densely sampled classes is a better source model than more diverse datasets such as ImageNet or Places.
Address Munich; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ WWH2018a Serial 3130
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Author Yaxing Wang; Joost Van de Weijer; Luis Herranz
Title Mix and match networks: encoder-decoder alignment for zero-pair image translation Type Conference Article
Year 2018 Publication 31st IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 5467 - 5476
Keywords
Abstract We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.
Address Salt Lake City; USA; June 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes LAMP; 600.109; 600.106; 600.120 Approved no
Call Number Admin @ si @ WWH2018b Serial 3131
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Author Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title BronchoX: bronchoscopy exploration software for biopsy intervention planning Type Journal
Year 2018 Publication Healthcare Technology Letters Abbreviated Journal HTL
Volume 5 Issue 5 Pages 177–182
Keywords
Abstract Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors’ solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea.
Address
Corporate Author rank (SJR) Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.096; 600.075; 601.323; 601.337; 600.145 Approved no
Call Number Admin @ si @ RSB2018a Serial 3132
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Author Debora Gil; Ruth Aris; Agnes Borras; Esmitt Ramirez; Rafael Sebastian; Mariano Vazquez
Title Influence of fiber connectivity in simulations of cardiac biomechanics Type Journal Article
Year 2019 Publication International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 14 Issue 1 Pages 63–72
Keywords Cardiac electromechanical simulations; Diffusion tensor imaging; Fiber connectivity
Abstract PURPOSE:
Personalized computational simulations of the heart could open up new improved approaches to diagnosis and surgery assistance systems. While it is fully recognized that myocardial fiber orientation is central for the construction of realistic computational models of cardiac electromechanics, the role of its overall architecture and connectivity remains unclear. Morphological studies show that the distribution of cardiac muscular fibers at the basal ring connects epicardium and endocardium. However, computational models simplify their distribution and disregard the basal loop. This work explores the influence in computational simulations of fiber distribution at different short-axis cuts.

METHODS:
We have used a highly parallelized computational solver to test different fiber models of ventricular muscular connectivity. We have considered two rule-based mathematical models and an own-designed method preserving basal connectivity as observed in experimental data. Simulated cardiac functional scores (rotation, torsion and longitudinal shortening) were compared to experimental healthy ranges using generalized models (rotation) and Mahalanobis distances (shortening, torsion).

RESULTS:
The probability of rotation was significantly lower for ruled-based models [95% CI (0.13, 0.20)] in comparison with experimental data [95% CI (0.23, 0.31)]. The Mahalanobis distance for experimental data was in the edge of the region enclosing 99% of the healthy population.

CONCLUSIONS:
Cardiac electromechanical simulations of the heart with fibers extracted from experimental data produce functional scores closer to healthy ranges than rule-based models disregarding architecture connectivity.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.096; 601.323; 600.139; 600.145 Approved no
Call Number Admin @ si @ GAB2019a Serial 3133
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Author Marta Diez-Ferrer; Arturo Morales; Rosa Lopez Lisbona; Noelia Cubero; Cristian Tebe; Susana Padrones; Samantha Aso; Jordi Dorca; Debora Gil; Antoni Rosell
Title Ultrathin Bronchoscopy with and without Virtual Bronchoscopic Navigation: Influence of Segmentation on Diagnostic Yield Type Journal Article
Year 2019 Publication Respiration Abbreviated Journal RES
Volume 97 Issue 3 Pages 252-258
Keywords Lung cancer; Peripheral lung lesion; Diagnosis; Bronchoscopy; Ultrathin bronchoscopy; Virtual bronchoscopic navigation
Abstract Background: Bronchoscopy is a safe technique for diagnosing peripheral pulmonary lesions (PPLs), and virtual bronchoscopic navigation (VBN) helps guide the bronchoscope to PPLs. Objectives: We aimed to compare the diagnostic yield of VBN-guided and unguided ultrathin bronchoscopy (UTB) and explore clinical and technical factors associated with better results. We developed a diagnostic algorithm for deciding whether to use VBN to reach PPLs or choose an alternative diagnostic approach. Methods: We compared diagnostic yield between VBN-UTB (prospective cases) and unguided UTB (historical controls) and analyzed the VBN-UTB subgroup to identify clinical and technical variables that could predict the success of VBN-UTB. Results: Fifty-five cases and 110 controls were included. The overall diagnostic yield did not differ between the VBN-guided and unguided arms (47 and 40%, respectively; p = 0.354). Although the yield was slightly higher for PPLs ≤20 mm in the VBN-UTB arm, the difference was not significant (p = 0.069). No other clinical characteristics were associated with a higher yield in a subgroup analysis, but an 85% diagnostic yield was observed when segmentation was optimal and the PPL was endobronchial (vs. 30% when segmentation was suboptimal and 20% when segmentation was optimal but the PPL was extrabronchial). Conclusions: VBN-guided UTB is not superior to unguided UTB. A greater impact of VBN-guided over unguided UTB is highly dependent on both segmentation quality and an endobronchial location of the PPL. Segmentation quality should be considered before starting a procedure, when an alternative technique that may improve yield can be chosen, saving time and resources.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.145; 600.139 Approved no
Call Number Admin @ si @ DML2019 Serial 3134
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Author Marta Diez-Ferrer; Debora Gil; Cristian Tebe; Carles Sanchez
Title Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation Type Journal Article
Year 2018 Publication Respiration Abbreviated Journal RES
Volume 96 Issue 6 Pages 525-534
Keywords Multidetector computed tomography; Bronchoscopy; Continuous positive airway pressure; Image enhancement; Virtual bronchoscopic navigation
Abstract Abstract
RATIONALE:
Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways.

OBJECTIVES:
To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration.

METHODS:
CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol.

MEASUREMENTS AND MAIN RESULTS:
Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001).

CONCLUSIONS:
CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ DGT2018 Serial 3135
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Author Esmitt Ramirez; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
Title Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy Type Conference Article
Year 2018 Publication OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis Abbreviated Journal
Volume 11041 Issue Pages
Keywords Biopsy guiding; Bronchoscopy; Lung biopsy; Intervention guiding; Airway codification
Abstract Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer’s projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems.
Address Granada; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MICCAIW
Notes IAM; 600.096; 600.075; 601.323; 600.145 Approved no
Call Number Admin @ si @ RSB2018b Serial 3137
Permanent link to this record
 
 
Author Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera
Title Exploiting feature representations through similarity learning, post-ranking and ranking aggregation for person re-identification Type Journal Article
Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 79 Issue Pages 76-85
Keywords
Abstract Person re-identification has received special attention by the human analysis community in the last few years. To address the challenges in this field, many researchers have proposed different strategies, which basically exploit either cross-view invariant features or cross-view robust metrics. In this work, we propose to exploit a post-ranking approach and combine different feature representations through ranking aggregation. Spatial information, which potentially benefits the person matching, is represented using a 2D body model, from which color and texture information are extracted and combined. We also consider background/foreground information, automatically extracted via Deep Decompositional Network, and the usage of Convolutional Neural Network (CNN) features. To describe the matching between images we use the polynomial feature map, also taking into account local and global information. The Discriminant Context Information Analysis based post-ranking approach is used to improve initial ranking lists. Finally, the Stuart ranking aggregation method is employed to combine complementary ranking lists obtained from different feature representations. Experimental results demonstrated that we improve the state-of-the-art on VIPeR and PRID450s datasets, achieving 67.21% and 75.64% on top-1 rank recognition rate, respectively, as well as obtaining competitive results on CUHK01 dataset.
Address
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Notes HuPBA; 602.143 Approved no
Call Number Admin @ si @ JBE2018 Serial 3138
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Author Pau Rodriguez; Josep M. Gonfaus; Guillem Cucurull; Xavier Roca; Jordi Gonzalez
Title Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11212 Issue Pages 357-372
Keywords Deep Learning; Convolutional Neural Networks; Attention
Abstract We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition. It learns to attend to lower-level feature activations without requiring part annotations and uses these activations to update and rectify the output likelihood distribution. In contrast to other approaches, the proposed mechanism is modular, architecture-independent and efficient both in terms of parameters and computation required. Experiments show that networks augmented with our approach systematically improve their classification accuracy and become more robust to clutter. As a result, Wide Residual Networks augmented with our proposal surpasses the state of the art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford dogs, and UEC Food-100.
Address Munich; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes ISE; 600.098; 602.121; 600.119 Approved no
Call Number Admin @ si @ RGC2018 Serial 3139
Permanent link to this record
 
 
Author Boris N. Oreshkin; Pau Rodriguez; Alexandre Lacoste
Title TADAM: Task dependent adaptive metric for improved few-shot learning Type Conference Article
Year 2018 Publication 32nd Annual Conference on Neural Information Processing Systems Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-shot algorithm parameter updates. Metric scaling provides improvements up to 14% in accuracy for certain metrics on the mini-Imagenet 5-way 5-shot classification task. We further propose a simple and effective way of conditioning a learner on the task sample set, resulting in learning a task-dependent metric space. Moreover, we propose and empirically test a practical end-to-end optimization procedure based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based on CIFAR100.
Address Montreal; Canada; December 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference NIPS
Notes ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ ORL2018 Serial 3140
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Author Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas
Title A Social-Aware Assistant to support individuals with visual impairments during social interaction: A systematic requirements analysis Type Journal Article
Year 2019 Publication International Journal of Human-Computer Studies Abbreviated Journal IJHC
Volume 122 Issue Pages 50-60
Keywords
Abstract Visual impairment affects the normal course of activities in everyday life including mobility, education, employment, and social interaction. Most of the existing technical solutions devoted to empowering the visually impaired people are in the areas of navigation (obstacle avoidance), access to printed information and object recognition. Less effort has been dedicated so far in developing solutions to support social interactions. In this paper, we introduce a Social-Aware Assistant (SAA) that provides visually impaired people with cues to enhance their face-to-face conversations. The system consists of a perceptive component (represented by smartglasses with an embedded video camera) and a feedback component (represented by a haptic belt). When the vision system detects a head nodding, the belt vibrates, thus suggesting the user to replicate (mirror) the gesture. In our experiments, sighted persons interacted with blind people wearing the SAA. We instructed the former to mirror the noddings according to the vibratory signal, while the latter interacted naturally. After the face-to-face conversation, the participants had an interview to express their experience regarding the use of this new technological assistant. With the data collected during the experiment, we have assessed quantitatively and qualitatively the device usefulness and user satisfaction.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.109; 600.120 Approved no
Call Number Admin @ si @ MTR2019 Serial 3142
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Author Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas
Title Single Shot Scene Text Retrieval Type Conference Article
Year 2018 Publication 15th European Conference on Computer Vision Abbreviated Journal
Volume 11218 Issue Pages 728-744
Keywords Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC
Abstract Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed.
Address Munich; September 2018
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECCV
Notes DAG; 600.084; 601.338; 600.121; 600.129 Approved no
Call Number Admin @ si @ GMR2018 Serial 3143
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Author Mohammed Al Rawi; Dimosthenis Karatzas
Title On the Labeling Correctness in Computer Vision Datasets Type Conference Article
Year 2018 Publication Proceedings of the Workshop on Interactive Adaptive Learning, co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Image datasets have heavily been used to build computer vision systems.
These datasets are either manually or automatically labeled, which is a
problem as both labeling methods are prone to errors. To investigate this problem, we use a majority voting ensemble that combines the results from several Convolutional Neural Networks (CNNs). Majority voting ensembles not only enhance the overall performance, but can also be used to estimate the confidence level of each sample. We also examined Softmax as another form to estimate posterior probability. We have designed various experiments with a range of different ensembles built from one or different, or temporal/snapshot CNNs, which have been trained multiple times stochastically. We analyzed CIFAR10, CIFAR100, EMNIST, and SVHN datasets and we found quite a few incorrect
labels, both in the training and testing sets. We also present detailed confidence analysis on these datasets and we found that the ensemble is better than the Softmax when used estimate the per-sample confidence. This work thus proposes an approach that can be used to scrutinize and verify the labeling of computer vision datasets, which can later be applied to weakly/semi-supervised learning. We propose a measure, based on the Odds-Ratio, to quantify how many of these incorrectly classified labels are actually incorrectly labeled and how many of these are confusing. The proposed methods are easily scalable to larger datasets, like ImageNet, LSUN and SUN, as each CNN instance is trained for 60 epochs; or even faster, by implementing a temporal (snapshot) ensemble.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title (down) Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference ECML-PKDDW
Notes DAG; 600.121; 600.129 Approved no
Call Number Admin @ si @ RaK2018 Serial 3144
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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
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Area Expedition Conference
Notes ADAS; MSIAU; 600.086; 600.130; 600.122; 600.118 Approved no
Call Number Admin @ si @ SSH2018 Serial 3145
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