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| Author | Aura Hernandez-Sabate; Debora Gil;Eduard Fernandez-Nofrerias;Petia Radeva; Enric Marti | ||||
| Title | Approaching Artery Rigid Dynamics in IVUS | Type | Journal Article | ||
| Year | 2009 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal | TMI |
| Volume | 28 | Issue | 11 | Pages | 1670-1680 |
Keywords  |
Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation. | ||||
| Abstract | Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0278-0062 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | IAM; MILAB | Approved | no | ||
| Call Number | IAM @ iam @ HGF2009 | Serial | 1545 | ||
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| Author | Andre Litvin; Kamal Nasrollahi; Sergio Escalera; Cagri Ozcinar; Thomas B. Moeslund; Gholamreza Anbarjafari | ||||
| Title | A Novel Deep Network Architecture for Reconstructing RGB Facial Images from Thermal for Face Recognition | Type | Journal Article | ||
| Year | 2019 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
| Volume | 78 | Issue | 18 | Pages | 25259–25271 |
| Keywords |
Fully convolutional networks; FusionNet; Thermal imaging; Face recognition | ||||
| Abstract | This work proposes a fully convolutional network architecture for RGB face image generation from a given input thermal face image to be applied in face recognition scenarios. The proposed method is based on the FusionNet architecture and increases robustness against overfitting using dropout after bridge connections, randomised leaky ReLUs (RReLUs), and orthogonal regularization. Furthermore, we propose to use a decoding block with resize convolution instead of transposed convolution to improve final RGB face image generation. To validate our proposed network architecture, we train a face classifier and compare its face recognition rate on the reconstructed RGB images from the proposed architecture, to those when reconstructing images with the original FusionNet, as well as when using the original RGB images. As a result, we are introducing a new architecture which leads to a more accurate network. | ||||
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LNE2019 | Serial | 3318 | ||
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| Author | Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados | ||||
| Title | Vers une approche foue of encapsulation de graphes: application a la reconnaissance de symboles | Type | Conference Article | ||
| Year | 2010 | Publication | Colloque International Francophone sur l'Écrit et le Document | Abbreviated Journal | |
| Volume | Issue | Pages | 169-184 | ||
| Keywords |
Fuzzy interval; Graph embedding; Bayesian network; Symbol recognition | ||||
| Abstract | We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented. | ||||
| Address | Sousse, Tunisia | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CIFED | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ LBR2010a | Serial | 1293 | ||
| Permanent link to this record | |||||
| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud | ||||
| Title | Deep Learning based Single Image Dehazing | Type | Conference Article | ||
| Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop | Abbreviated Journal | |
| Volume | Issue | Pages | 1250 - 12507 | ||
| Keywords |
Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis | ||||
| Abstract | This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently.
A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images. |
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| Address | Salt Lake City; USA; June 2018 | ||||
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| Area | Expedition | Conference | CVPRW | ||
| Notes | MSIAU; 600.086; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ SSV2018d | Serial | 3197 | ||
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| Author | Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria | ||||
| Title | Automatic garment retexturing based on infrared information | Type | Journal Article | ||
| Year | 2016 | Publication | Computers & Graphics | Abbreviated Journal | CG |
| Volume | 59 | Issue | Pages | 28-38 | |
| Keywords |
Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading | ||||
| Abstract | This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ ADT2016 | Serial | 2759 | ||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga | ||||
| Title | Saliency Estimation Using a Non-Parametric Low-Level Vision Model | Type | Conference Article | ||
| Year | 2011 | Publication | IEEE conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 433-440 | ||
| Keywords |
Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms | ||||
| Abstract | Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. | ||||
| Address | Colorado Springs | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4577-0394-2 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MVO2011 | Serial | 1757 | ||
| Permanent link to this record | |||||
| Author | Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer | ||||
| Title | Generative Multi-Label Zero-Shot Learning | Type | Journal Article | ||
| Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 45 | Issue | 12 | Pages | 14611-14624 |
| Keywords |
Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis | ||||
| Abstract | Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. | ||||
| Address | December 2023 | ||||
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| Notes | LAMP; PID2021-128178OB-I00 | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 3853 | ||
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| Author | Yaxing Wang; Chenshen Wu; Luis Herranz; Joost Van de Weijer; Abel Gonzalez-Garcia; Bogdan Raducanu | ||||
| Title | Transferring GANs: generating images from limited data | Type | Conference Article | ||
| Year | 2018 | Publication | 15th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 11210 | Issue | Pages | 220-236 | |
| Keywords |
Generative adversarial networks; Transfer learning; Domain adaptation; Image generation | ||||
| Abstract | ransferring knowledge of pre-trained networks to new domains by means of fine-tuning is a widely used practice for applications based on discriminative models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of target domain size, the relative distance between source and target domain, and the initialization of conditional GANs. Our results show that using knowledge from pre-trained networks can shorten the convergence time and can significantly improve the quality of the generated images, especially when target data is limited. We show that these conclusions can also be drawn for conditional GANs even when the pre-trained model was trained without conditioning. Our results also suggest that density is more important than diversity and a dataset with one or few densely sampled classes is a better source model than more diverse datasets such as ImageNet or Places. | ||||
| Address | Munich; September 2018 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCV | ||
| Notes | LAMP; 600.109; 600.106; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ WWH2018a | Serial | 3130 | ||
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| Author | Mikhail Mozerov; Joost Van de Weijer | ||||
| Title | Improved Recursive Geodesic Distance Computation for Edge Preserving Filter | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 26 | Issue | 8 | Pages | 3696 - 3706 |
| Keywords |
Geodesic distance filter; color image filtering; image enhancement | ||||
| Abstract | All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application, we consider a geodesic distance-based filter for image denoising. Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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| Notes | LAMP; ISE; 600.120; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ Moz2017 | Serial | 2921 | ||
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| Author | Debora Gil; Petia Radeva | ||||
| Title | A Regularized Curvature Flow Designed for a Selective Shape Restoration | Type | Journal Article | ||
| Year | 2004 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | |
| Volume | 13 | Issue | Pages | 1444–1458 | |
| Keywords |
Geometric flows, nonlinear filtering, shape recovery. | ||||
| Abstract | Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization. | ||||
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| Notes | IAM;MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ GiR2004b | Serial | 491 | ||
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| Author | Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera | ||||
| Title | A Gesture Recognition System for Detecting Behavioral Patterns of ADHD | Type | Journal Article | ||
| Year | 2016 | Publication | IEEE Transactions on System, Man and Cybernetics, Part B | Abbreviated Journal | TSMCB |
| Volume | 46 | Issue | 1 | Pages | 136-147 |
| Keywords |
Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data | ||||
| Abstract | We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context. | ||||
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| Notes | HuPBA; MILAB; | Approved | no | ||
| Call Number | Admin @ si @ BHE2016 | Serial | 2566 | ||
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| Author | Gerard Canal; Sergio Escalera; Cecilio Angulo | ||||
| Title | A Real-time Human-Robot Interaction system based on gestures for assistive scenarios | Type | Journal Article | ||
| Year | 2016 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 149 | Issue | Pages | 65-77 | |
| Keywords |
Gesture recognition; Human Robot Interaction; Dynamic Time Warping; Pointing location estimation | ||||
| Abstract | Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a KinectTM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. | ||||
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| Publisher | Elsevier B.V. | Place of Publication | Editor | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ CEA2016 | Serial | 2768 | ||
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| Author | Sergio Escalera; Vassilis Athitsos; Isabelle Guyon | ||||
| Title | Challenges in Multi-modal Gesture Recognition | Type | Book Chapter | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
| Volume | Issue | Pages | 1-60 | ||
| Keywords |
Gesture recognition; Time series analysis; Multimodal data analysis; Computer vision; Pattern recognition; Wearable sensors; Infrared cameras; Kinect TMTM | ||||
| Abstract | This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011–2015. We began right at the start of the Kinect TMTM revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. | ||||
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| Notes | HuPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ EAG2017 | Serial | 3008 | ||
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| Author | Sergio Escalera; Vassilis Athitsos; Isabelle Guyon | ||||
| Title | Challenges in multimodal gesture recognition | Type | Journal Article | ||
| Year | 2016 | Publication | Journal of Machine Learning Research | Abbreviated Journal | JMLR |
| Volume | 17 | Issue | Pages | 1-54 | |
| Keywords |
Gesture Recognition; Time Series Analysis; Multimodal Data Analysis; Computer Vision; Pattern Recognition; Wearable sensors; Infrared Cameras; KinectTM | ||||
| Abstract | This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the KinectTMrevolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands
of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research. |
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| Publisher | Place of Publication | Editor | Zhuowen Tu | ||
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| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ EAG2016 | Serial | 2764 | ||
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| Author | Maurizio Mencuccini; Jordi Martinez-Vilalta; Josep Piñol; Lasse Loepfe; Mireia Burnat ; Xavier Alvarez; Juan Camacho; Debora Gil | ||||
| Title | A quantitative and statistically robust method for the determination of xylem conduit spatial distribution | Type | Journal Article | ||
| Year | 2010 | Publication | American Journal of Botany | Abbreviated Journal | AJB |
| Volume | 97 | Issue | 8 | Pages | 1247-1259 |
| Keywords |
Geyer; hydraulic conductivity; point pattern analysis; Ripley; Spatstat; vessel clusters; xylem anatomy; xylem network | ||||
| Abstract | Premise of the study: Because of their limited length, xylem conduits need to connect to each other to maintain water transport from roots to leaves. Conduit spatial distribution in a cross section plays an important role in aiding this connectivity. While indices of conduit spatial distribution already exist, they are not well defined statistically. * Methods: We used point pattern analysis to derive new spatial indices. One hundred and five cross-sectional images from different species were transformed into binary images. The resulting point patterns, based on the locations of the conduit centers-of-area, were analyzed to determine whether they departed from randomness. Conduit distribution was then modeled using a spatially explicit stochastic model. * Key results: The presence of conduit randomness, uniformity, or aggregation depended on the spatial scale of the analysis. The large majority of the images showed patterns significantly different from randomness at least at one spatial scale. A strong phylogenetic signal was detected in the spatial variables. * Conclusions: Conduit spatial arrangement has been largely conserved during evolution, especially at small spatial scales. Species in which conduits were aggregated in clusters had a lower conduit density compared to those with uniform distribution. Statistically sound spatial indices must be employed as an aid in the characterization of distributional patterns across species and in models of xylem water transport. Point pattern analysis is a very useful tool in identifying spatial patterns. | ||||
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| Notes | IAM; | Approved | no | ||
| Call Number | IAM @ iam @ MMG2010 | Serial | 1623 | ||
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