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Author Marta Diez-Ferrer; Debora Gil; Elena Carreño; Susana Padrones; Samantha Aso; Vanesa Vicens; Cubero Noelia; Rosa Lopez Lisbona; Carles Sanchez; Agnes Borras; Antoni Rosell
Title Positive Airway Pressure-Enhanced CT to Improve Virtual Bronchoscopic Navigation Type Journal Article
Year 2016 Publication Chest Journal Abbreviated Journal CHEST
Volume 150 Issue (down) 4 Pages 1003A
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Notes IAM; 600.096; 600.075 Approved no
Call Number Admin @ si @ DGC2016 Serial 3099
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Author Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli
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 (down) 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.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ CMR2018 Serial 3186
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Author Lichao Zhang; Abel Gonzalez-Garcia; Joost Van de Weijer; Martin Danelljan; Fahad Shahbaz Khan
Title Synthetic Data Generation for End-to-End Thermal Infrared Tracking Type Journal Article
Year 2019 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 28 Issue (down) 4 Pages 1837 - 1850
Keywords
Abstract The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved the performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks for tracking in thermal infrared (TIR) images. Therefore, most state-of-the-art methods on tracking for TIR data are still based on handcrafted features. To address this problem, we propose to use image-to-image translation models. These models allow us to translate the abundantly available labeled RGB data to synthetic TIR data. We explore both the usage of paired and unpaired image translation models for this purpose. These methods provide us with a large labeled dataset of synthetic TIR sequences, on which we can train end-to-end optimal features for tracking. To the best of our knowledge, we are the first to train end-to-end features for TIR tracking. We perform extensive experiments on the VOT-TIR2017 dataset. We show that a network trained on a large dataset of synthetic TIR data obtains better performance than one trained on the available real TIR data. Combining both data sources leads to further improvement. In addition, when we combine the network with motion features, we outperform the state of the art with a relative gain of over 10%, clearly showing the efficiency of using synthetic data to train end-to-end TIR trackers.
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Notes LAMP; 600.141; 600.120 Approved no
Call Number Admin @ si @ YGW2019 Serial 3228
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Author Akhil Gurram; Onay Urfalioglu; Ibrahim Halfaoui; Fahd Bouzaraa; Antonio Lopez
Title Semantic Monocular Depth Estimation Based on Artificial Intelligence Type Journal Article
Year 2020 Publication IEEE Intelligent Transportation Systems Magazine Abbreviated Journal ITSM
Volume 13 Issue (down) 4 Pages 99-103
Keywords
Abstract Depth estimation provides essential information to perform autonomous driving and driver assistance. A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels where the same raw training data is associated with both types of ground truth, i.e., depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, i.e., that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on monocular depth estimation.
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Notes ADAS; 600.124; 600.118 Approved no
Call Number Admin @ si @ GUH2019 Serial 3306
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Author Ikechukwu Ofodile; Ahmed Helmi; Albert Clapes; Egils Avots; Kerttu Maria Peensoo; Sandhra Mirella Valdma; Andreas Valdmann; Heli Valtna Lukner; Sergey Omelkov; Sergio Escalera; Cagri Ozcinar; Gholamreza Anbarjafari
Title Action recognition using single-pixel time-of-flight detection Type Journal Article
Year 2019 Publication Entropy Abbreviated Journal ENTROPY
Volume 21 Issue (down) 4 Pages 414
Keywords single pixel single photon image acquisition; time-of-flight; action recognition
Abstract Action recognition is a challenging task that plays an important role in many robotic systems, which highly depend on visual input feeds. However, due to privacy concerns, it is important to find a method which can recognise actions without using visual feed. In this paper, we propose a concept for detecting actions while preserving the test subject’s privacy. Our proposed method relies only on recording the temporal evolution of light pulses scattered back from the scene.
Such data trace to record one action contains a sequence of one-dimensional arrays of voltage values acquired by a single-pixel detector at 1 GHz repetition rate. Information about both the distance to the object and its shape are embedded in the traces. We apply machine learning in the form of recurrent neural networks for data analysis and demonstrate successful action recognition. The experimental results show that our proposed method could achieve on average 96.47% accuracy on the actions walking forward, walking backwards, sitting down, standing up and waving hand, using recurrent
neural network.
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Notes HuPBA; no proj Approved no
Call Number Admin @ si @ OHC2019 Serial 3319
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Author Ana Garcia Rodriguez; Jorge Bernal; F. Javier Sanchez; Henry Cordova; Rodrigo Garces Duran; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach
Title Polyp fingerprint: automatic recognition of colorectal polyps’ unique features Type Journal Article
Year 2020 Publication Surgical Endoscopy and other Interventional Techniques Abbreviated Journal SEND
Volume 34 Issue (down) 4 Pages 1887-1889
Keywords
Abstract BACKGROUND:
Content-based image retrieval (CBIR) is an application of machine learning used to retrieve images by similarity on the basis of features. Our objective was to develop a CBIR system that could identify images containing the same polyp ('polyp fingerprint').

METHODS:
A machine learning technique called Bag of Words was used to describe each endoscopic image containing a polyp in a unique way. The system was tested with 243 white light images belonging to 99 different polyps (for each polyp there were at least two images representing it in two different temporal moments). Images were acquired in routine colonoscopies at Hospital Clínic using high-definition Olympus endoscopes. The method provided for each image the closest match within the dataset.

RESULTS:
The system matched another image of the same polyp in 221/243 cases (91%). No differences were observed in the number of correct matches according to Paris classification (protruded: 90.7% vs. non-protruded: 91.3%) and size (< 10 mm: 91.6% vs. > 10 mm: 90%).

CONCLUSIONS:
A CBIR system can match accurately two images containing the same polyp, which could be a helpful aid for polyp image recognition.

KEYWORDS:
Artificial intelligence; Colorectal polyps; Content-based image retrieval
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Notes MV; no menciona Approved no
Call Number Admin @ si @ Serial 3403
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Author Cristina Sanchez Montes; Jorge Bernal; Ana Garcia Rodriguez; Henry Cordova; Gloria Fernandez Esparrach
Title Revisión de métodos computacionales de detección y clasificación de pólipos en imagen de colonoscopia Type Journal Article
Year 2020 Publication Gastroenterología y Hepatología Abbreviated Journal GH
Volume 43 Issue (down) 4 Pages 222-232
Keywords
Abstract Computer-aided diagnosis (CAD) is a tool with great potential to help endoscopists in the tasks of detecting and histologically classifying colorectal polyps. In recent years, different technologies have been described and their potential utility has been increasingly evidenced, which has generated great expectations among scientific societies. However, most of these works are retrospective and use images of different quality and characteristics which are analysed off line. This review aims to familiarise gastroenterologists with computational methods and the particularities of endoscopic imaging, which have an impact on image processing analysis. Finally, the publicly available image databases, needed to compare and confirm the results obtained with different methods, are presented.
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Notes MV; Approved no
Call Number Admin @ si @ SBG2020 Serial 3404
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Author Aymen Azaza; Joost Van de Weijer; Ali Douik; Javad Zolfaghari Bengar; Marc Masana
Title Saliency from High-Level Semantic Image Features Type Journal
Year 2020 Publication SN Computer Science Abbreviated Journal SN
Volume 1 Issue (down) 4 Pages 1-12
Keywords
Abstract Top-down semantic information is known to play an important role in assigning saliency. Recently, large strides have been made in improving state-of-the-art semantic image understanding in the fields of object detection and semantic segmentation. Therefore, since these methods have now reached a high-level of maturity, evaluation of the impact of high-level image understanding on saliency estimation is now feasible. We propose several saliency features which are computed from object detection and semantic segmentation results. We combine these features with a standard baseline method for saliency detection to evaluate their importance. Experiments demonstrate that the proposed features derived from object detection and semantic segmentation improve saliency estimation significantly. Moreover, they show that our method obtains state-of-the-art results on (FT, ImgSal, and SOD datasets) and obtains competitive results on four other datasets (ECSSD, PASCAL-S, MSRA-B, and HKU-IS).
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Notes LAMP; 600.120; 600.109; 600.106 Approved no
Call Number Admin @ si @ AWD2020 Serial 3503
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Author Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl
Title A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted Type Journal Article
Year 2023 Publication ACM Journal on Computing and Cultural Heritage Abbreviated Journal JOCCH
Volume 15 Issue (down) 4 Pages 1-18
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Abstract Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.
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Publisher ACM Place of Publication Editor
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Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no
Call Number Admin @ si @ SBC2023 Serial 3732
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Author Manisha Das; Deep Gupta; Petia Radeva; Ashwini M. Bakde
Title Multi-scale decomposition-based CT-MR neurological image fusion using optimized bio-inspired spiking neural model with meta-heuristic optimization Type Journal Article
Year 2021 Publication International Journal of Imaging Systems and Technology Abbreviated Journal IMA
Volume 31 Issue (down) 4 Pages 2170-2188
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Abstract Multi-modal medical image fusion plays an important role in clinical diagnosis and works as an assistance model for clinicians. In this paper, a computed tomography-magnetic resonance (CT-MR) image fusion model is proposed using an optimized bio-inspired spiking feedforward neural network in different decomposition domains. First, source images are decomposed into base (low-frequency) and detail (high-frequency) layer components. Low-frequency subbands are fused using texture energy measures to capture the local energy, contrast, and small edges in the fused image. High-frequency coefficients are fused using firing maps obtained by pixel-activated neural model with the optimized parameters using three different optimization techniques such as differential evolution, cuckoo search, and gray wolf optimization, individually. In the optimization model, a fitness function is computed based on the edge index of resultant fused images, which helps to extract and preserve sharp edges available in the source CT and MR images. To validate the fusion performance, a detailed comparative analysis is presented among the proposed and state-of-the-art methods in terms of quantitative and qualitative measures along with computational complexity. Experimental results show that the proposed method produces a significantly better visual quality of fused images meanwhile outperforms the existing methods.
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Notes MILAB; no menciona Approved no
Call Number Admin @ si @ DGR2021a Serial 3630
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Author Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio
Title A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding Type Journal
Year 2021 Publication SMPTE Motion Imaging Journal Abbreviated Journal SMPTE
Volume 130 Issue (down) 4 Pages 53 - 65
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Abstract In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components.
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Notes CIC Approved no
Call Number SCV2021 Serial 3671
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Author Aura Hernandez-Sabate; Jose Elias Yauri; Pau Folch; Daniel Alvarez; Debora Gil
Title EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment Type Journal Article
Year 2024 Publication Sensors Abbreviated Journal SENS
Volume 24 Issue (down) 4 Pages 1174
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Abstract High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
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Notes IAM Approved no
Call Number Admin @ si @ HYF2024 Serial 4019
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Author Javier Vazquez; Graham D. Finlayson; Luis Herranz
Title Improving the perception of low-light enhanced images Type Journal Article
Year 2024 Publication Optics Express Abbreviated Journal
Volume 32 Issue (down) 4 Pages 5174-5190
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Abstract Improving images captured under low-light conditions has become an important topic in computational color imaging, as it has a wide range of applications. Most current methods are either based on handcrafted features or on end-to-end training of deep neural networks that mostly focus on minimizing some distortion metric —such as PSNR or SSIM— on a set of training images. However, the minimization of distortion metrics does not mean that the results are optimal in terms of perception (i.e. perceptual quality). As an example, the perception-distortion trade-off states that, close to the optimal results, improving distortion results in worsening perception. This means that current low-light image enhancement methods —that focus on distortion minimization— cannot be optimal in the sense of obtaining a good image in terms of perception errors. In this paper, we propose a post-processing approach in which, given the original low-light image and the result of a specific method, we are able to obtain a result that resembles as much as possible that of the original method, but, at the same time, giving an improvement in the perception of the final image. More in detail, our method follows the hypothesis that in order to minimally modify the perception of an input image, any modification should be a combination of a local change in the shading across a scene and a global change in illumination color. We demonstrate the ability of our method quantitatively using perceptual blind image metrics such as BRISQUE, NIQE, or UNIQUE, and through user preference tests.
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Notes MACO Approved no
Call Number Admin @ si @ VFH2024 Serial 4018
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Author Agata Lapedriza; Santiago Segui; David Masip; Jordi Vitria
Title A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning Type Journal
Year 2008 Publication Pattern Analysis and Applications, Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications, Abbreviated Journal
Volume 11 Issue (down) 3-4 Pages 299-308
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Corporate Author Thesis
Publisher Springer Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ LSM2008 Serial 996
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Author Bogdan Raducanu; Jordi Vitria
Title Online Nonparametric Discriminant Analysis for Incremental Subspace Learning and Recognition Type Journal
Year 2008 Publication Pattern Analysis and Applications. Special Issue: Non–Parametric Distance–Based Classification Techniques and Their Applications Abbreviated Journal
Volume 11 Issue (down) 3-4 Pages 259–268
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Abstract
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Corporate Author Thesis
Publisher Springer Place of Publication Editor
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Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ RaV2008c Serial 997
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