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| Author | Murad Al Haj; Jordi Gonzalez; Larry S. Davis | ||||
| Title | On Partial Least Squares in Head Pose Estimation: How to simultaneously deal with misalignment | Type | Conference Article | ||
| Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages  |
2602-2609 | ||
| Keywords | |||||
| Abstract | Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors. | ||||
| Address | Providence, Rhode Island | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ HGD2012 | Serial | 2029 | ||
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| Author | Shanxin Yuan; Guillermo Garcia-Hernando; Bjorn Stenger; Gyeongsik Moon; Ju Yong Chang; Kyoung Mu Lee; Pavlo Molchanov; Jan Kautz; Sina Honari; Liuhao Ge; Junsong Yuan; Xinghao Chen; Guijin Wang; Fan Yang; Kai Akiyama; Yang Wu; Qingfu Wan; Meysam Madadi; Sergio Escalera; Shile Li; Dongheui Lee; Iason Oikonomidis; Antonis Argyros; Tae-Kyun Kim | ||||
| Title | Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals | Type | Conference Article | ||
| Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
2636 - 2645 | ||
| Keywords | Three-dimensional displays; Task analysis; Pose estimation; Two dimensional displays; Joints; Training; Solid modeling | ||||
| Abstract | In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints. | ||||
| Address | Salt Lake City; USA; June 2018 | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ YGS2018 | Serial | 3115 | ||
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| Author | Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios | ||||
| Title | Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs | Type | Conference Article | ||
| Year | 2012 | Publication | 21st International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
2664 - 2667 | ||
| Keywords | |||||
| Abstract | We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. | ||||
| Address | Tsukuba Science City, Japan | ||||
| 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 | 1051-4651 | ISBN | 978-1-4673-2216-4 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ RSL2012a; | Serial | 2032 | ||
| Permanent link to this record | |||||
| Author | J. Stöttinger; A. Hanbury; N. Sebe; Theo Gevers | ||||
| Title | Spars Color Interest Points for Image Retrieval and Object Categorization | Type | Journal Article | ||
| Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 5 | Pages |
2681-2692 |
| Keywords | |||||
| Abstract | Impact factor 2010: 2.92
IF 2011/2012?: 3.32 Interest point detection is an important research area in the field of image processing and computer vision. In particular, image retrieval and object categorization heavily rely on interest point detection from which local image descriptors are computed for image matching. In general, interest points are based on luminance, and color has been largely ignored. However, the use of color increases the distinctiveness of interest points. The use of color may therefore provide selective search reducing the total number of interest points used for image matching. This paper proposes color interest points for sparse image representation. To reduce the sensitivity to varying imaging conditions, light-invariant interest points are introduced. Color statistics based on occurrence probability lead to color boosted points, which are obtained through saliency-based feature selection. Furthermore, a principal component analysis-based scale selection method is proposed, which gives a robust scale estimation per interest point. From large-scale experiments, it is shown that the proposed color interest point detector has higher repeatability than a luminance-based one. Furthermore, in the context of image retrieval, a reduced and predictable number of color features show an increase in performance compared to state-of-the-art interest points. Finally, in the context of object recognition, for the Pascal VOC 2007 challenge, our method gives comparable performance to state-of-the-art methods using only a small fraction of the features, reducing the computing time considerably. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ SHS2012 | Serial | 1847 | ||
| Permanent link to this record | |||||
| Author | Parichehr Behjati Ardakani; Pau Rodriguez; Armin Mehri; Isabelle Hupont; Carles Fernandez; Jordi Gonzalez | ||||
| Title | OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages |
2693-2702 | ||
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| Abstract | Super-resolution (SR) has achieved great success due to the development of deep convolutional neural networks (CNNs). However, as the depth and width of the networks increase, CNN-based SR methods have been faced with the challenge of computational complexity in practice. More- over, most SR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. To address these limitations we introduce OverNet, a deep but lightweight convolutional network to solve SISR at arbitrary scale factors with a single model. We make the following contributions: first, we introduce a lightweight feature extractor that enforces efficient reuse of information through a novel recursive structure of skip and dense connections. Second, to maximize the performance of the feature extractor, we propose a model agnostic reconstruction module that generates accurate high-resolution images from overscaled feature maps obtained from any SR architecture. Third, we introduce a multi-scale loss function to achieve generalization across scales. Experiments show that our proposal outperforms previous state-of-the-art approaches in standard benchmarks, while maintaining relatively low computation and memory requirements. | ||||
| Address | Virtual; January 2021 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WACV | ||
| Notes | ISE; 600.119; 600.098 | Approved | no | ||
| Call Number | Admin @ si @ BRM2021 | Serial | 3512 | ||
| Permanent link to this record | |||||
| Author | Armin Mehri; Parichehr Behjati Ardakani; Angel Sappa | ||||
| Title | MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages |
2703-2712 | ||
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| Abstract | Lightweight super resolution networks have extremely importance for real-world applications. In recent years several SR deep learning approaches with outstanding achievement have been introduced by sacrificing memory and computational cost. To overcome this problem, a novel lightweight super resolution network is proposed, which improves the SOTA performance in lightweight SR and performs roughly similar to computationally expensive networks. Multi-Path Residual Network designs with a set of Residual concatenation Blocks stacked with Adaptive Residual Blocks: ($i$) to adaptively extract informative features and learn more expressive spatial context information; ($ii$) to better leverage multi-level representations before up-sampling stage; and ($iii$) to allow an efficient information and gradient flow within the network. The proposed architecture also contains a new attention mechanism, Two-Fold Attention Module, to maximize the representation ability of the model. Extensive experiments show the superiority of our model against other SOTA SR approaches. | ||||
| Address | Virtual; January 2021 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WACV | ||
| Notes | MSIAU; 600.130; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ MAS2021b | Serial | 3582 | ||
| Permanent link to this record | |||||
| Author | Hugo Berti; Angel Sappa; Osvaldo Agamennoni | ||||
| Title | Autonomous robot navigation with a global and asymptotic convergence | Type | Conference Article | ||
| Year | 2007 | Publication | IEEE International Conference on Robotics and Automation | Abbreviated Journal | |
| Volume | Issue | Pages |
2712–2717 | ||
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| Abstract | |||||
| Address | Roma (Italy) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICRA | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ BSA2007 | Serial | 796 | ||
| Permanent link to this record | |||||
| Author | Mario Rojas; David Masip; A. Todorov; Jordi Vitria | ||||
| Title | Automatic Point-based Facial Trait Judgments Evaluation | Type | Conference Article | ||
| Year | 2010 | Publication | 23rd IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
2715–2720 | ||
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| Abstract | Humans constantly evaluate the personalities of other people using their faces. Facial trait judgments have been studied in the psychological field, and have been determined to influence important social outcomes of our lives, such as elections outcomes and social relationships. Recent work on textual descriptions of faces has shown that trait judgments are highly correlated. Further, behavioral studies suggest that two orthogonal dimensions, valence and dominance, can describe the basis of the human judgments from faces. In this paper, we used a corpus of behavioral data of judgments on different trait dimensions to automatically learn a trait predictor from facial pixel images. We study whether trait evaluations performed by humans can be learned using machine learning classifiers, and used later in automatic evaluations of new facial images. The experiments performed using local point-based descriptors show promising results in the evaluation of the main traits. | ||||
| Address | San Francisco CA, USA | ||||
| 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 | 1063-6919 | ISBN | 978-1-4244-6984-0 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RMT2010 | Serial | 1282 | ||
| Permanent link to this record | |||||
| Author | Xinhang Song; Shuqiang Jiang; Luis Herranz | ||||
| Title | Multi-Scale Multi-Feature Context Modeling for Scene Recognition in the Semantic Manifold | Type | Journal Article | ||
| Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 26 | Issue | 6 | Pages |
2721-2735 |
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| Abstract | Before the big data era, scene recognition was often approached with two-step inference using localized intermediate representations (objects, topics, and so on). One of such approaches is the semantic manifold (SM), in which patches and images are modeled as points in a semantic probability simplex. Patch models are learned resorting to weak supervision via image labels, which leads to the problem of scene categories co-occurring in this semantic space. Fortunately, each category has its own co-occurrence patterns that are consistent across the images in that category. Thus, discovering and modeling these patterns are critical to improve the recognition performance in this representation. Since the emergence of large data sets, such as ImageNet and Places, these approaches have been relegated in favor of the much more powerful convolutional neural networks (CNNs), which can automatically learn multi-layered representations from the data. In this paper, we address many limitations of the original SM approach and related works. We propose discriminative patch representations using neural networks and further propose a hybrid architecture in which the semantic manifold is built on top of multiscale CNNs. Both representations can be computed significantly faster than the Gaussian mixture models of the original SM. To combine multiple scales, spatial relations, and multiple features, we formulate rich context models using Markov random fields. To solve the optimization problem, we analyze global and local approaches, where a top-down hierarchical algorithm has the best performance. Experimental results show that exploiting different types of contextual relations jointly consistently improves the recognition accuracy. | ||||
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| Area | Expedition | Conference | |||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ SJH2017a | Serial | 2963 | ||
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| Author | M. Bressan; Jordi Vitria | ||||
| Title | Nonparametric Discriminant Analysis and Nearest Neighbor Classification | Type | Journal Article | ||
| Year | 2003 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 24 | Issue | 15 | Pages |
2743–2749 |
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| Abstract | IF: 0.809 | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BrV2003b | Serial | 367 | ||
| Permanent link to this record | |||||
| Author | Fernando Barrera; Felipe Lumbreras; Angel Sappa | ||||
| Title | Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space | Type | Conference Article | ||
| Year | 2010 | Publication | 17th IEEE International Conference on Image Processing | Abbreviated Journal | |
| Volume | Issue | Pages |
2749–2752 | ||
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| Abstract | This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach. | ||||
| Address | Hong-Kong | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1522-4880 | ISBN | 978-1-4244-7992-4 | Medium | |
| Area | Expedition | Conference | ICIP | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ BLS2010 | Serial | 1358 | ||
| Permanent link to this record | |||||
| Author | Yunan Li; Jun Wan; Qiguang Miao; Sergio Escalera; Huijuan Fang; Huizhou Chen; Xiangda Qi; Guodong Guo | ||||
| Title | CR-Net: A Deep Classification-Regression Network for Multimodal Apparent Personality Analysis | Type | Journal Article | ||
| Year | 2020 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 128 | Issue | Pages |
2763–2780 | |
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| Abstract | First impressions strongly influence social interactions, having a high impact in the personal and professional life. In this paper, we present a deep Classification-Regression Network (CR-Net) for analyzing the Big Five personality problem and further assisting on job interview recommendation in a first impressions setup. The setup is based on the ChaLearn First Impressions dataset, including multimodal data with video, audio, and text converted from the corresponding audio data, where each person is talking in front of a camera. In order to give a comprehensive prediction, we analyze the videos from both the entire scene (including the person’s motions and background) and the face of the person. Our CR-Net first performs personality trait classification and applies a regression later, which can obtain accurate predictions for both personality traits and interview recommendation. Furthermore, we present a new loss function called Bell Loss to address inaccurate predictions caused by the regression-to-the-mean problem. Extensive experiments on the First Impressions dataset show the effectiveness of our proposed network, outperforming the state-of-the-art. | ||||
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| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ LWM2020 | Serial | 3413 | ||
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| Author | Joakim Bruslund Haurum; Meysam Madadi; Sergio Escalera; Thomas B. Moeslund | ||||
| Title | Multi-Task Classification of Sewer Pipe Defects and Properties Using a Cross-Task Graph Neural Network Decoder | Type | Conference Article | ||
| Year | 2022 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages |
2806-2817 | ||
| Keywords | Vision Systems; Applications Multi-Task Classification | ||||
| Abstract | The sewerage infrastructure is one of the most important and expensive infrastructures in modern society. In order to efficiently manage the sewerage infrastructure, automated sewer inspection has to be utilized. However, while sewer
defect classification has been investigated for decades, little attention has been given to classifying sewer pipe properties such as water level, pipe material, and pipe shape, which are needed to evaluate the level of sewer pipe deterioration. In this work we classify sewer pipe defects and properties concurrently and present a novel decoder-focused multi-task classification architecture Cross-Task Graph Neural Network (CT-GNN), which refines the disjointed per-task predictions using cross-task information. The CT-GNN architecture extends the traditional disjointed task-heads decoder, by utilizing a cross-task graph and unique class node embeddings. The cross-task graph can either be determined a priori based on the conditional probability between the task classes or determined dynamically using self-attention. CT-GNN can be added to any backbone and trained end-toend at a small increase in the parameter count. We achieve state-of-the-art performance on all four classification tasks in the Sewer-ML dataset, improving defect classification and water level classification by 5.3 and 8.0 percentage points, respectively. We also outperform the single task methods as well as other multi-task classification approaches while introducing 50 times fewer parameters than previous modelfocused approaches. |
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WACV | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ BME2022 | Serial | 3638 | ||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga | ||||
| Title | Low-level SpatioChromatic Grouping for Saliency Estimation | Type | Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 35 | Issue | 11 | Pages |
2810-2816 |
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| Abstract | We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; 600.051; 600.052; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ MVO2013 | Serial | 2289 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding | Type | Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 46 | Issue | 10 | Pages |
2830-2839 |
| Keywords | Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification | ||||
| Abstract | Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2013a | Serial | 2247 | ||
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