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Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu edit   pdf
url  doi
openurl 
  Title New Opportunities for Computer Vision-Based Assistive Technology Systems for the Visually Impaired Type Journal Article
  Year 2014 Publication Computer Abbreviated Journal COMP  
  Volume 47 Issue 4 Pages 52-58  
  Keywords  
  Abstract Computing advances and increased smartphone use gives technology system designers greater flexibility in exploiting computer vision to support visually impaired users. Understanding these users' needs will certainly provide insight for the development of improved usability of computing devices.  
  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 0018-9162 ISBN Medium  
  Area Expedition Conference (up)  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ TSR2014a Serial 2317  
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika edit   pdf
doi  openurl
  Title Embedding new observations via sparse-coding for non-linear manifold learning Type Journal Article
  Year 2014 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 47 Issue 1 Pages 480-492  
  Keywords  
  Abstract Non-linear dimensionality reduction techniques are affected by two critical aspects: (i) the design of the adjacency graphs, and (ii) the embedding of new test data-the out-of-sample problem. For the first aspect, the proposed solutions, in general, were heuristically driven. For the second aspect, the difficulty resides in finding an accurate mapping that transfers unseen data samples into an existing manifold. Past works addressing these two aspects were heavily parametric in the sense that the optimal performance is only achieved for a suitable parameter choice that should be known in advance. In this paper, we demonstrate that the sparse representation theory not only serves for automatic graph construction as shown in recent works, but also represents an accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. To evaluate the effectiveness of the proposed out-of-sample embedding, experiments are conducted using the K-nearest neighbor (KNN) and Kernel Support Vector Machines (KSVM) classifiers on six public face datasets. The experimental results show that the proposed model is able to achieve high categorization effectiveness as well as high consistency with non-linear embeddings/manifolds obtained in batch modes.  
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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 (up)  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ RaD2013b Serial 2316  
Permanent link to this record
 
 
Author Cesar Isaza; Joaquin Salas; Bogdan Raducanu edit   pdf
doi  openurl
  Title Rendering ground truth data sets to detect shadows cast by static objects in outdoors Type Journal Article
  Year 2014 Publication Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 70 Issue 1 Pages 557-571  
  Keywords Synthetic ground truth data set; Sun position; Shadow detection; Static objects shadow detection  
  Abstract In our work, we are particularly interested in studying the shadows cast by static objects in outdoor environments, during daytime. To assess the accuracy of a shadow detection algorithm, we need ground truth information. The collection of such information is a very tedious task because it is a process that requires manual annotation. To overcome this severe limitation, we propose in this paper a methodology to automatically render ground truth using a virtual environment. To increase the degree of realism and usefulness of the simulated environment, we incorporate in the scenario the precise longitude, latitude and elevation of the actual location of the object, as well as the sun’s position for a given time and day. To evaluate our method, we consider a qualitative and a quantitative comparison. In the quantitative one, we analyze the shadow cast by a real object in a particular geographical location and its corresponding rendered model. To evaluate qualitatively the methodology, we use some ground truth images obtained both manually and automatically.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1380-7501 ISBN Medium  
  Area Expedition Conference (up)  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ ISR2014 Serial 2229  
Permanent link to this record
 
 
Author Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu edit  doi
isbn  openurl
  Title Robust Head Gestures Recognition for Assistive Technology Type Book Chapter
  Year 2014 Publication Pattern Recognition Abbreviated Journal  
  Volume 8495 Issue Pages 152-161  
  Keywords  
  Abstract This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture.  
  Address  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-07490-0 Medium  
  Area Expedition Conference (up)  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ TSR2014b Serial 2505  
Permanent link to this record
 
 
Author Manuel Graña; Bogdan Raducanu edit  doi
openurl 
  Title Special Issue on Bioinspired and knowledge based techniques and applications Type Journal Article
  Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume Issue Pages 1-3  
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  Abstract  
  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 (up)  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ GrR2015 Serial 2598  
Permanent link to this record
 
 
Author Fadi Dornaika; Bogdan Raducanu; Alireza Bosaghzadeh edit  openurl
  Title Facial expression recognition based on multi observations with application to social robotics Type Book Chapter
  Year 2015 Publication Emotional and Facial Expressions: Recognition, Developmental Differences and Social Importance Abbreviated Journal  
  Volume Issue Pages 153-166  
  Keywords  
  Abstract Human-robot interaction is a hot topic nowadays in the social robotics
community. One crucial aspect is represented by the affective communication
which comes encoded through the facial expressions. In this chapter, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, viewand texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression.		  
  Address  
  Corporate Author Thesis  
  Publisher Nova Science publishers Place of Publication Editor Bruce Flores  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference (up)  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ DRB2015 Serial 2720  
Permanent link to this record
 
 
Author Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas edit   pdf
doi  openurl
  Title Head-gestures mirroring detection in dyadic social linteractions with computer vision-based wearable devices Type Journal Article
  Year 2016 Publication Neurocomputing Abbreviated Journal NEUCOM  
  Volume 175 Issue B Pages 866–876  
  Keywords Head gestures recognition; Mirroring detection; Dyadic social interaction analysis; Wearable devices  
  Abstract During face-to-face human interaction, nonverbal communication plays a fundamental role. A relevant aspect that takes part during social interactions is represented by mirroring, in which a person tends to mimic the non-verbal behavior (head and body gestures, vocal prosody, etc.) of the counterpart. In this paper, we introduce a computer vision-based system to detect mirroring in dyadic social interactions with the use of a wearable platform. In our context, mirroring is inferred as simultaneous head noddings displayed by the interlocutors. Our approach consists of the following steps: (1) facial features extraction; (2) facial features stabilization; (3) head nodding recognition; and (4) mirroring detection. Our system achieves a mirroring detection accuracy of 72% on a custom mirroring dataset.  
  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 (up)  
  Notes LAMP; 600.072; 600.068; Approved no  
  Call Number Admin @ si @ TRM2016 Serial 2721  
Permanent link to this record
 
 
Author Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas edit   pdf
doi  openurl
  Title Assessing the Influence of Mirroring on the Perception of Professional Competence using Wearable Technology Type Journal Article
  Year 2016 Publication IEEE Transactions on Affective Computing Abbreviated Journal TAC  
  Volume 9 Issue 2 Pages 161-175  
  Keywords Mirroring; Nodding; Competence; Perception; Wearable Technology  
  Abstract Nonverbal communication is an intrinsic part in daily face-to-face meetings. A frequently observed behavior during social interactions is mirroring, in which one person tends to mimic the attitude of the counterpart. This paper shows that a computer vision system could be used to predict the perception of competence in dyadic interactions through the automatic detection of mirroring
events. To prove our hypothesis, we developed: (1) A social assistant for mirroring detection, using a wearable device which includes a video camera and (2) an automatic classifier for the perception of competence, using the number of nodding gestures and mirroring events as predictors. For our study, we used a mixed-method approach in an experimental design where 48 participants acting as customers interacted with a confederated psychologist. We found that the number of nods or mirroring events has a significant influence on the perception of competence. Our results suggest that: (1) Customer mirroring is a better predictor than psychologist mirroring; (2) the number of psychologist’s nods is a better predictor than the number of customer’s nods; (3) except for the psychologist mirroring, the computer vision algorithm we used worked about equally well whether it was acquiring images from wearable smartglasses or fixed cameras.		  
  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 (up)  
  Notes LAMP; 600.072; Approved no  
  Call Number Admin @ si @ MTR2016 Serial 2826  
Permanent link to this record
 
 
Author Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras edit  url
openurl 
  Title 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 (up)  
  Notes MSIAU; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ ACC2018 Serial 3147  
Permanent link to this record
 
 
Author Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu edit   pdf
url  openurl
  Title Saliency for fine-grained object recognition in domains with scarce training data Type Journal Article
  Year 2019 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 94 Issue Pages 62-73  
  Keywords  
  Abstract This paper investigates the role of saliency to improve the classification accuracy of a Convolutional Neural Network (CNN) for the case when scarce training data is available. Our approach consists in adding a saliency branch to an existing CNN architecture which is used to modulate the standard bottom-up visual features from the original image input, acting as an attentional mechanism that guides the feature extraction process. The main aim of the proposed approach is to enable the effective training of a fine-grained recognition model with limited training samples and to improve the performance on the task, thereby alleviating the need to annotate a large dataset. The vast majority of saliency methods are evaluated on their ability to generate saliency maps, and not on their functionality in a complete vision pipeline. Our proposed pipeline allows to evaluate saliency methods for the high-level task of object recognition. We perform extensive experiments on various fine-grained datasets (Flowers, Birds, Cars, and Dogs) under different conditions and show that saliency can considerably improve the network’s performance, especially for the case of scarce training data. Furthermore, our experiments show that saliency methods that obtain improved saliency maps (as measured by traditional saliency benchmarks) also translate to saliency methods that yield improved performance gains when applied in an object recognition pipeline.  
  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 (up)  
  Notes LAMP; 600.109; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ FGW2019 Serial 3264  
Permanent link to this record
 
 
Author Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo edit  url
openurl 
  Title Detailed 3D face reconstruction from a single RGB image Type Journal
  Year 2019 Publication Journal of WSCG Abbreviated Journal JWSCG  
  Volume 27 Issue 2 Pages 103-112  
  Keywords 3D Wrinkle Reconstruction; Face Analysis, Optimization.  
  Abstract This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.		  
  Address 2019/11  
  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 (up)  
  Notes MSIAU; 600.086; 600.130; 600.122 Approved no  
  Call Number Admin @ si @ Serial 3708  
Permanent link to this record
 
 
Author Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika edit  openurl
  Title Facial Expression Recognition based on Multi-view Observations with Application to Social Robotics Type Conference Article
  Year 2014 Publication 1st Workshop on Computer Vision for Affective Computing Abbreviated Journal  
  Volume Issue Pages 1-8  
  Keywords  
  Abstract Human-robot interaction is a hot topic nowadays in the social robotics community. One crucial aspect is represented by the affective communication which comes encoded through the facial expressions. In this paper, we propose a novel approach for facial expression recognition, which exploits an efficient and adaptive graph-based label propagation (semi-supervised mode) in a multi-observation framework. The facial features are extracted using an appearance-based 3D face tracker, view- and texture independent. Our method has been extensively tested on the CMU dataset, and has been conveniently compared with other methods for graph construction. With the proposed approach, we developed an application for an AIBO robot, in which it mirrors the recognized facial
expression.		  
  Address Singapore; November 2014  
  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 (up) ACCV  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ RBD2014 Serial 2599  
Permanent link to this record
 
 
Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company edit  openurl
  Title An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort Type Conference Article
  Year 2015 Publication Barcelona Computational, Cognitive and Systems Neuroscience Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Barcelona; June 2015  
  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 (up) BARCCSYN  
  Notes NEUROBIT; Approved no  
  Call Number Admin @ si @ OPC2015b Serial 2634  
Permanent link to this record
 
 
Author Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer edit  url
openurl 
  Title When Deep Learners Change Their Mind: Learning Dynamics for Active Learning Type Conference Article
  Year 2021 Publication 19th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 13052 Issue 1 Pages 403-413  
  Keywords  
  Abstract Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results.  
  Address September 2021  
  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 (up) CAIP  
  Notes LAMP; Approved no  
  Call Number Admin @ si @ ZRV2021 Serial 3673  
Permanent link to this record
 
 
Author Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika edit  doi
openurl 
  Title Multi-observation Face Recognition in Videos based on Label Propagation Type Conference Article
  Year 2015 Publication 6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 Abbreviated Journal  
  Volume Issue Pages 10-17  
  Keywords  
  Abstract In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the
selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video
sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods.		  
  Address Boston; USA; June 2015  
  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 (up) CVPRW  
  Notes LAMP; 600.068; 600.072; Approved no  
  Call Number Admin @ si @ RBD2015 Serial 2627  
Permanent link to this record
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