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Author Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras
Title (down) Segmentation of aerial images for plausible detail synthesis Type Journal Article
Year 2018 Publication Computers & Graphics Abbreviated Journal CG
Volume 71 Issue Pages 23-34
Keywords Terrain editing; Detail synthesis; Vegetation synthesis; Terrain rendering; Image segmentation
Abstract The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0097-8493 ISBN Medium
Area Expedition Conference
Notes MSIAU; 600.086; 600.118 Approved no
Call Number Admin @ si @ ACC2018 Serial 3147
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma
Title (down) Scale coding bag of deep features for human attribute and action recognition Type Journal Article
Year 2018 Publication Machine Vision and Applications Abbreviated Journal MVAP
Volume 29 Issue 1 Pages 55-71
Keywords Action recognition; Attribute recognition; Bag of deep features
Abstract Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.068; 600.079; 600.106; 600.120 Approved no
Call Number Admin @ si @ KWR2018 Serial 3107
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Author Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu
Title (down) Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video Type Journal Article
Year 2018 Publication Pattern Recognition Abbreviated Journal PR
Volume 80 Issue Pages 64-82
Keywords Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition
Abstract Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ RSJ2018 Serial 3096
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Author Xialei Liu; Marc Masana; Luis Herranz; Joost Van de Weijer; Antonio Lopez; Andrew Bagdanov
Title (down) Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting Type Conference Article
Year 2018 Publication 24th International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2262-2268
Keywords
Abstract In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of
a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and
Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICPR
Notes LAMP; ADAS; 601.305; 601.109; 600.124; 600.106; 602.200; 600.120; 600.118 Approved no
Call Number Admin @ si @ LMH2018 Serial 3160
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Author Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera
Title (down) RGB-D-based Human Motion Recognition with Deep Learning: A Survey Type Journal Article
Year 2018 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 171 Issue Pages 118-139
Keywords Human motion recognition; RGB-D data; Deep learning; Survey
Abstract Human motion recognition is one of the most important branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development in artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data. In this paper, a detailed overview of recent advances in RGB-D-based motion recognition is presented. The reviewed methods are broadly categorized into four groups, depending on the modality adopted for recognition: RGB-based, depth-based, skeleton-based and RGB+D-based. As a survey focused on the application of deep learning to RGB-D-based motion recognition, we explicitly discuss the advantages and limitations of existing techniques. Particularly, we highlighted the methods of encoding spatial-temporal-structural information inherent in video sequence, and discuss potential directions for future research.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ WLO2018 Serial 3123
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Author Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari
Title (down) Review on Emotion Recognition Databases Type Book Chapter
Year 2018 Publication Human-Robot Interaction: Theory and Application Abbreviated Journal
Volume Issue Pages
Keywords emotion; computer vision; databases
Abstract Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-78923-316-2 Medium
Area Expedition Conference
Notes HUPBA; 602.133 Approved no
Call Number Admin @ si @ HRL2018 Serial 3212
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Author Laura Lopez-Fuentes; Joost Van de Weijer; Manuel Gonzalez-Hidalgo; Harald Skinnemoen; Andrew Bagdanov
Title (down) Review on computer vision techniques in emergency situations Type Journal Article
Year 2018 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 77 Issue 13 Pages 17069–17107
Keywords Emergency management; Computer vision; Decision makers; Situational awareness; Critical situation
Abstract In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in better understanding them and making decisions faster. Cameras are almost everywhere these days, either in terms of smartphones, installed CCTV cameras, UAVs or others. However, this poses challenges in big data and information overflow. Moreover, most of the time there are no disasters at any given location, so humans aiming to detect sudden situations may not be as alert as needed at any point in time. Consequently, computer vision tools can be an excellent decision support. The number of emergencies where computer vision tools has been considered or used is very wide, and there is a great overlap across related emergency research. Researchers tend to focus on state-of-the-art systems that cover the same emergency as they are studying, obviating important research in other fields. In order to unveil this overlap, the survey is divided along four main axes: the types of emergencies that have been studied in computer vision, the objective that the algorithms can address, the type of hardware needed and the algorithms used. Therefore, this review provides a broad overview of the progress of computer vision covering all sorts of emergencies.
Address
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.068; 600.120 Approved no
Call Number Admin @ si @ LWG2018 Serial 3041
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Author Ana Maria Ares; Jorge Bernal; Maria Jesus Nozal; F. Javier Sanchez; Jose Bernal
Title (down) Results of the use of Kahoot! gamification tool in a course of Chemistry Type Conference Article
Year 2018 Publication 4th International Conference on Higher Education Advances Abbreviated Journal
Volume Issue Pages 1215-1222
Keywords
Abstract The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
Address Valencia; 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 Conference HEAD
Notes MV; no proj Approved no
Call Number Admin @ si @ ABN2018 Serial 3246
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Author Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro
Title (down) Residual Stacked RNNs for Action Recognition Type Conference Article
Year 2018 Publication 9th International Workshop on Human Behavior Understanding Abbreviated Journal
Volume Issue Pages 534-548
Keywords Action recognition; Deep residual learning; Two-stream RNN
Abstract Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset.
Address Munich; September 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 Conference ECCVW
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LCE2018b Serial 3206
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Author Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli
Title (down) Recurrent Neural Networks for Remote Sensing Image Classification Type Journal Article
Year 2018 Publication IET Computer Vision Abbreviated Journal IETCV
Volume 12 Issue 7 Pages 1040 - 1045
Keywords
Abstract Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset.
Address
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LÇE2018 Serial 3119
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Author Cristina Palmero; Javier Selva; Mohammad Ali Bagheri; Sergio Escalera
Title (down) Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues Type Conference Article
Year 2018 Publication 29th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included.
Address Newcastle; UK; September 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 Conference BMVC
Notes HUPBA; no proj Approved no
Call Number Admin @ si @ PSB2018 Serial 3208
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Author Jon Almazan; Bojana Gajic; Naila Murray; Diane Larlus
Title (down) Re-ID done right: towards good practices for person re-identification Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Training a deep architecture using a ranking loss has become standard for the person re-identification task. Increasingly, these deep architectures include additional components that leverage part detections, attribute predictions, pose estimators and other auxiliary information, in order to more effectively localize and align discriminative image regions. In this paper we adopt a different approach and carefully design each component of a simple deep architecture and, critically, the strategy for training it effectively for person re-identification. We extensively evaluate each design choice, leading to a list of good practices for person re-identification. By following these practices, our approach outperforms the state of the art, including more complex methods with auxiliary components, by large margins on four benchmark datasets. We also provide a qualitative analysis of our trained representation which indicates that, while compact, it is able to capture information from localized and discriminative regions, in a manner akin to an implicit attention mechanism.
Address January 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 Conference
Notes Approved no
Call Number Admin @ si @ Serial 3711
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Author Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal
Title (down) Product graph-based higher order contextual similarities for inexact subgraph matching Type Journal Article
Year 2018 Publication Pattern Recognition Abbreviated Journal PR
Volume 76 Issue Pages 596-611
Keywords
Abstract Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem.
Address
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Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG; 602.167; 600.097; 600.121 Approved no
Call Number Admin @ si @ DLB2018 Serial 3083
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Author F.Negin; Pau Rodriguez; M.Koperski; A.Kerboua; Jordi Gonzalez; J.Bourgeois; E.Chapoulie; P.Robert; F.Bremond
Title (down) PRAXIS: Towards automatic cognitive assessment using gesture recognition Type Journal Article
Year 2018 Publication Expert Systems with Applications Abbreviated Journal ESWA
Volume 106 Issue Pages 21-35
Keywords
Abstract Praxis test is a gesture-based diagnostic test which has been accepted as diagnostically indicative of cortical pathologies such as Alzheimer’s disease. Despite being simple, this test is oftentimes skipped by the clinicians. In this paper, we propose a novel framework to investigate the potential of static and dynamic upper-body gestures based on the Praxis test and their potential in a medical framework to automatize the test procedures for computer-assisted cognitive assessment of older adults.

In order to carry out gesture recognition as well as correctness assessment of the performances we have recollected a novel challenging RGB-D gesture video dataset recorded by Kinect v2, which contains 29 specific gestures suggested by clinicians and recorded from both experts and patients performing the gesture set. Moreover, we propose a framework to learn the dynamics of upper-body gestures, considering the videos as sequences of short-term clips of gestures. Our approach first uses body part detection to extract image patches surrounding the hands and then, by means of a fine-tuned convolutional neural network (CNN) model, it learns deep hand features which are then linked to a long short-term memory to capture the temporal dependencies between video frames.
We report the results of four developed methods using different modalities. The experiments show effectiveness of our deep learning based approach in gesture recognition and performance assessment tasks. Satisfaction of clinicians from the assessment reports indicates the impact of framework corresponding to the diagnosis.
Address
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
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Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ NRK2018 Serial 3669
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Author Marta Diez-Ferrer; Debora Gil; Cristian Tebe; Carles Sanchez
Title (down) 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 Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ DGT2018 Serial 3135
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