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Author Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil
Title Continuous head pose estimation using manifold subspace embedding and multivariate regression Type Journal Article
Year 2018 Publication IEEE Access Abbreviated Journal ACCESS
Volume 6 Issue Pages 18325 - 18334
Keywords Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression
Abstract In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees.
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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 2169-3536 ISBN Medium
Area Expedition Conference (down)
Notes ADAS; 600.118 Approved no
Call Number Admin @ si @ DMH2018b Serial 3091
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Author Hugo Jair Escalante; Heysem Kaya; Albert Ali Salah; Sergio Escalera; Yagmur Gucluturk; Umut Guclu; Xavier Baro; Isabelle Guyon; Julio C. S. Jacques Junior; Meysam Madadi; Stephane Ayache; Evelyne Viegas; Furkan Gurpinar; Achmadnoer Sukma Wicaksana; Cynthia C. S. Liem; Marcel A. J. van Gerven; Rob van Lier
Title Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Explainability and interpretability are two critical aspects of decision support systems. Within computer vision, they are critical in certain tasks related to human behavior analysis such as in health care applications. Despite their importance, it is only recently that researchers are starting to explore these aspects. This paper provides an introduction to explainability and interpretability in the context of computer vision with an emphasis on looking at people tasks. Specifically, we review and study those mechanisms in the context of first impressions analysis. To the best of our knowledge, this is the first effort in this direction. Additionally, we describe a challenge we organized on explainability in first impressions analysis from video. We analyze in detail the newly introduced data set, the evaluation protocol, and summarize the results of the challenge. Finally, derived from our study, we outline research opportunities that we foresee will be decisive in the near future for the development of the explainable computer vision field.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference (down)
Notes HUPBA Approved no
Call Number Admin @ si @ JKS2018 Serial 3095
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Author Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu
Title 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.
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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 (down)
Notes DAG; 600.097; 600.121 Approved no
Call Number Admin @ si @ RSJ2018 Serial 3096
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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 4 Pages 1003A
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Abstract
Address
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Series Editor Series Title Abbreviated Series Title
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Notes IAM; 600.096; 600.075 Approved no
Call Number Admin @ si @ DGC2016 Serial 3099
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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 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 (down)
Notes CIC Approved no
Call Number Admin @ si @ Raf2017 Serial 3100
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Author C. Alejandro Parraga
Title Colours and Colour Vision: An Introductory Survey Type Journal Article
Year 2017 Publication Perception Abbreviated Journal PER
Volume 46 Issue 5 Pages 640-641
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Address
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Notes NEUROBIT; no menciona Approved no
Call Number Par2017 Serial 3101
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma
Title 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.
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Notes LAMP; 600.068; 600.079; 600.106; 600.120 Approved no
Call Number Admin @ si @ KWR2018 Serial 3107
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Author Marc Bolaños; Alvaro Peris; Francisco Casacuberta; Sergi Solera; Petia Radeva
Title Egocentric video description based on temporally-linked sequences Type Journal Article
Year 2018 Publication Journal of Visual Communication and Image Representation Abbreviated Journal JVCIR
Volume 50 Issue Pages 205-216
Keywords egocentric vision; video description; deep learning; multi-modal learning
Abstract Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures.
In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also release the EDUB-SegDesc dataset. This is the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Finally, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ BPC2018 Serial 3109
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Author Stefan Lonn; Petia Radeva; Mariella Dimiccoli
Title A picture is worth a thousand words but how to organize thousands of pictures? Type Miscellaneous
Year 2018 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization.
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Notes MILAB; no proj Approved no
Call Number Admin @ si @ LRD2018 Serial 3111
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Author Ivet Rafegas; Maria Vanrell
Title Color encoding in biologically-inspired convolutional neural networks Type Journal Article
Year 2018 Publication Vision Research Abbreviated Journal VR
Volume 151 Issue Pages 7-17
Keywords Color coding; Computer vision; Deep learning; Convolutional neural networks
Abstract Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations.
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Notes CIC; 600.051; 600.087 Approved no
Call Number Admin @ si @RaV2018 Serial 3114
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Author Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli
Title 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.
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Publisher Place of Publication Editor
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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 Pau Rodriguez; Miguel Angel Bautista; Sergio Escalera; Jordi Gonzalez
Title Beyond Oneshot Encoding: lower dimensional target embedding Type Journal Article
Year 2018 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 75 Issue Pages 21-31
Keywords Error correcting output codes; Output embeddings; Deep learning; Computer vision
Abstract Target encoding plays a central role when learning Convolutional Neural Networks. In this realm, one-hot encoding is the most prevalent strategy due to its simplicity. However, this so widespread encoding schema assumes a flat label space, thus ignoring rich relationships existing among labels that can be exploited during training. In large-scale datasets, data does not span the full label space, but instead lies in a low-dimensional output manifold. Following this observation, we embed the targets into a low-dimensional space, drastically improving convergence speed while preserving accuracy. Our contribution is two fold: (i) We show that random projections of the label space are a valid tool to find such lower dimensional embeddings, boosting dramatically convergence rates at zero computational cost; and (ii) we propose a normalized eigenrepresentation of the class manifold that encodes the targets with minimal information loss, improving the accuracy of random projections encoding while enjoying the same convergence rates. Experiments on CIFAR-100, CUB200-2011, Imagenet, and MIT Places demonstrate that the proposed approach drastically improves convergence speed while reaching very competitive accuracy rates.
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Notes ISE; HuPBA; 600.098; 602.133; 602.121; 600.119 Approved no
Call Number Admin @ si @ RBE2018 Serial 3120
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Author Jianzhy Guo; Zhen Lei; Jun Wan; Egils Avots; Noushin Hajarolasvadi; Boris Knyazev; Artem Kuharenko; Julio C. S. Jacques Junior; Xavier Baro; Hasan Demirel; Sergio Escalera; Juri Allik; Gholamreza Anbarjafari
Title Dominant and Complementary Emotion Recognition from Still Images of Faces Type Journal Article
Year 2018 Publication IEEE Access Abbreviated Journal ACCESS
Volume 6 Issue Pages 26391 - 26403
Keywords
Abstract Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ GLW2018 Serial 3122
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Author Pichao Wang; Wanqing Li; Philip Ogunbona; Jun Wan; Sergio Escalera
Title 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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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ WLO2018 Serial 3123
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Author Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari
Title Integrating Vision and Language for First Impression Personality Analysis Type Journal Article
Year 2018 Publication IEEE Multimedia Abbreviated Journal MULTIMEDIA
Volume 25 Issue 2 Pages 24 - 33
Keywords
Abstract The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness.
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Notes HUPBA; 602.133 Approved no
Call Number Admin @ si @ GAL2018 Serial 3124
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