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| Author | Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika | ||||
| 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 | ||
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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. |
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| Address | Boston; USA; June 2015 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP; 600.068; 600.072; | Approved | no | ||
| Call Number | Admin @ si @ RBD2015 | Serial | 2627 | ||
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| Author | Cesar Isaza; Joaquin Salas; Bogdan Raducanu | ||||
| 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. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1380-7501 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ ISR2014 | Serial | 2229 | ||
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| Author | Juan Ramon Terven Salinas; Bogdan Raducanu; Maria Elena Meza-de-Luna; Joaquin Salas | ||||
| Title | Evaluating Real-Time Mirroring of Head Gestures using Smart Glasses | Type | Conference Article | ||
| Year | 2015 | Publication | 16th IEEE International Conference on Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 452-460 | ||
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| Abstract |
Mirroring occurs when one person tends to mimic the non-verbal communication of their counterparts. Even though mirroring is a complex phenomenon, in this study, we focus on the detection of head-nodding as a simple non-verbal communication cue due to its significance as a gesture displayed during social interactions. This paper introduces a computer vision-based method to detect mirroring through the analysis of head gestures using wearable cameras (smart glasses). In addition, we study how such a method can be used to explore perceived competence. The proposed method has been evaluated and the experiments demonstrate how static and wearable cameras seem to be equally effective to gather the information required for the analysis. | ||||
| Address | Santiago de Chile; December 2015 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | LAMP; 600.068; 600.072; | Approved | no | ||
| Call Number | Admin @ si @ TRM2015 | Serial | 2722 | ||
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| Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
| Title | Single view facial hair 3D reconstruction | Type | Conference Article | ||
| Year | 2019 | Publication | 9th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
| Volume | 11867 | Issue | Pages | 423-436 | |
| Keywords | 3D Vision; Shape Reconstruction; Facial Hair Modeling | ||||
| Abstract |
n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. | ||||
| Address | Madrid; July 2019 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | IbPRIA | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3707 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| 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 |
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| 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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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ RaD2013b | Serial | 2316 | ||
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| Author | Maria Elena Meza-de-Luna; Juan Ramon Terven Salinas; Bogdan Raducanu; Joaquin Salas | ||||
| 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. |
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| Notes | LAMP; 600.072; | Approved | no | ||
| Call Number | Admin @ si @ MTR2016 | Serial | 2826 | ||
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| Author | Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu | ||||
| Title | Which tone-mapping is the best? A comparative study of tone-mapping perceived quality | Type | Abstract | ||
| Year | 2014 | Publication | Perception | Abbreviated Journal | |
| Volume | 43 | Issue | Pages | 106 | |
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| Abstract |
Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings. |
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT; 600.074 | Approved | no | ||
| Call Number | Admin @ si @ CPO2014 | Serial | 2527 | ||
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| Author | Oscar Argudo; Marc Comino; Antonio Chica; Carlos Andujar; Felipe Lumbreras | ||||
| 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. | ||||
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| ISSN | 0097-8493 | ISBN | Medium | ||
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| Notes | MSIAU; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ACC2018 | Serial | 3147 | ||
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| Author | Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo | ||||
| 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. |
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| Address | 2019/11 | ||||
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| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3708 | ||
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| Author | Carola Figueroa Flores; Abel Gonzalez-Garcia; Joost Van de Weijer; Bogdan Raducanu | ||||
| 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 | |
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| 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. | ||||
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| Notes | LAMP; 600.109; 600.141; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ FGW2019 | Serial | 3264 | ||
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| Author | Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu | ||||
| Title | Robust Head Gestures Recognition for Assistive Technology | Type | Book Chapter | ||
| Year | 2014 | Publication | Pattern Recognition | Abbreviated Journal | |
| Volume | 8495 | Issue | Pages | 152-161 | |
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| 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. | ||||
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| Publisher | Springer International Publishing | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-319-07490-0 | Medium | |
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| Notes | LAMP; | Approved | no | ||
| Call Number | Admin @ si @ TSR2014b | Serial | 2505 | ||
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| Author | Chenshen Wu; Joost Van de Weijer | ||||
| Title | Density Map Distillation for Incremental Object Counting | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 2505-2514 | ||
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| Abstract |
We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods. | ||||
| Address | Vancouver; Canada; June 2023 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ WuW2023 | Serial | 3916 | ||
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| Author | Mohammad N. S. Jahromi; Pau Buch Cardona; Egils Avots; Kamal Nasrollahi; Sergio Escalera; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
| Title | Privacy-Constrained Biometric System for Non-cooperative Users | Type | Journal Article | ||
| Year | 2019 | Publication | Entropy | Abbreviated Journal | ENTROPY |
| Volume | 21 | Issue | 11 | Pages | 1033 |
| Keywords | biometric recognition; multimodal-based human identification; privacy; deep learning | ||||
| Abstract |
With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject’s hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ NBA2019 | Serial | 3313 | ||
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