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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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Year |
2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent System |
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CNN in multispectral imaging; Image colorization |
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This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very di erent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. |
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Porto; Portugal; June 2017 |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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Admin @ si @ |
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2919 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture |
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Conference Article |
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2017 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time. |
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Honolulu; Hawaii; USA; July 2017 |
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CVPRW |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017b |
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2920 |
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Author |
Mikhail Mozerov; Joost Van de Weijer |
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Title |
Improved Recursive Geodesic Distance Computation for Edge Preserving Filter |
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Journal Article |
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Year |
2017 |
Publication |
IEEE Transactions on Image Processing |
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TIP |
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26 |
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8 |
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3696 - 3706 |
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Geodesic distance filter; color image filtering; image enhancement |
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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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LAMP; ISE; 600.120; 600.098; 600.119 |
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Admin @ si @ Moz2017 |
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2921 |
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Author |
Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Julio C. S. Jacques Junior; Xavier Baro; Evelyne Viegas; Yagmur Gucluturk; Umut Guclu; Marcel A. J. van Gerven; Rob van Lier; Meysam Madadi; Stephane Ayache |
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Title |
Design of an Explainable Machine Learning Challenge for Video Interviews |
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Conference Article |
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Year |
2017 |
Publication |
International Joint Conference on Neural Networks |
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This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration. |
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Anchorage; Alaska; USA; May 2017 |
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IJCNN |
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HUPBA; no proj |
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no |
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Admin @ si @ EGE2017 |
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2922 |
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Author |
Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera |
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Title |
Exploiting feature representations through similarity learning and ranking aggregation for person re-identification |
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Conference Article |
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Year |
2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
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Person re-identification has received special attentionby the human analysis community in the last few years.To address the challenges in this field, many researchers haveproposed different strategies, which basically exploit eithercross-view invariant features or cross-view robust metrics. Inthis work we propose to combine different feature representationsthrough ranking aggregation. Spatial information, whichpotentially benefits the person matching, is represented usinga 2D body model, from which color and texture informationare extracted and combined. We also consider contextualinformation (background and foreground data), automaticallyextracted via Deep Decompositional Network, and the usage ofConvolutional Neural Network (CNN) features. To describe thematching between images we use the polynomial feature map,also taking into account local and global information. Finally,the Stuart ranking aggregation method is employed to combinecomplementary ranking lists obtained from different featurerepresentations. Experimental results demonstrated that weimprove the state-of-the-art on VIPeR and PRID450s datasets,achieving 58.77% and 71.56% on top-1 rank recognitionrate, respectively, as well as obtaining competitive results onCUHK01 dataset. |
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Washington; DC; USA; May 2017 |
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HUPBA; 602.143 |
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no |
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Admin @ si @ JBE2017 |
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2923 |
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Author |
Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari |
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Title |
Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases |
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Conference Article |
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Year |
2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
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In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists. |
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Washington; DC; USA; May 2017 |
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HUPBA; no menciona |
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no |
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Call Number |
Admin @ si @ LJG2017 |
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2924 |
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Author |
Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari |
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Title |
Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification |
Type |
Conference Article |
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Year |
2017 |
Publication |
12th IEEE International Conference on Automatic Face and Gesture Recognition |
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We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions. |
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Washington; DC; USA; May 2017 |
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HUPBA; no menciona |
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no |
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Admin @ si @ LRL2017 |
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2925 |
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Author |
Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca |
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Title |
Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification |
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Journal Article |
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Year |
2017 |
Publication |
IEEE Transactions on cybernetics |
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Cyber |
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1-11 |
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Pain is an unpleasant feeling that has been shown to be an important factor for the recovery of patients. Since this is costly in human resources and difficult to do objectively, there is the need for automatic systems to measure it. In this paper, contrary to current state-of-the-art techniques in pain assessment, which are based on facial features only, we suggest that the performance can be enhanced by feeding the raw frames to deep learning models, outperforming the latest state-of-the-art results while also directly facing the problem of imbalanced data. As a baseline, our approach first uses convolutional neural networks (CNNs) to learn facial features from VGG_Faces, which are then linked to a long short-term memory to exploit the temporal relation between video frames. We further compare the performances of using the so popular schema based on the canonically normalized appearance versus taking into account the whole image. As a result, we outperform current state-of-the-art area under the curve performance in the UNBC-McMaster Shoulder Pain Expression Archive Database. In addition, to evaluate the generalization properties of our proposed methodology on facial motion recognition, we also report competitive results in the Cohn Kanade+ facial expression database. |
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ISE; 600.119; 600.098 |
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Admin @ si @ RCG2017a |
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2926 |
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Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca |
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Title |
Regularizing CNNs with Locally Constrained Decorrelations |
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2017 |
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5th International Conference on Learning Representations |
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Toulon; France; April 2017 |
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ICLR |
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ISE; 602.143; 600.119; 600.098 |
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Admin @ si @ RGC2017 |
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2927 |
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Hana Jarraya; Muhammad Muzzamil Luqman; Jean-Yves Ramel |
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Improving Fuzzy Multilevel Graph Embedding Technique by Employing Topological Node Features: An Application to Graphics Recognition |
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2017 |
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Graphics Recognition. Current Trends and Challenges |
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9657 |
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Springer |
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B. Lamiroy; R Dueire Lins |
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GREC |
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DAG; 600.097; 600.121 |
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Admin @ si @ JLR2017 |
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2928 |
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Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Product graph-based higher order contextual similarities for inexact subgraph matching |
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2018 |
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Pattern Recognition |
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76 |
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596-611 |
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Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. |
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DAG; 602.167; 600.097; 600.121 |
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Admin @ si @ DLB2018 |
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3083 |
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Karim Lekadir; Alfiia Galimzianova; Angels Betriu; Maria del Mar Vila; Laura Igual; Daniel L. Rubin; Elvira Fernandez-Giraldez; Petia Radeva; Sandy Napel |
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A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound |
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2017 |
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IEEE Journal Biomedical and Health Informatics |
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J-BHI |
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21 |
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1 |
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48-55 |
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Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network (CNN) that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90 000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed CNN. The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound. |
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MILAB; no menciona |
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Admin @ si @ LGB2017 |
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2931 |
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Umut Guclu; Yagmur Gucluturk; Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez; Rob van Lier; Marcel A. J. van Gerven |
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End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks |
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Miscellaneous |
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2017 |
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Arxiv |
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arXiv:1703.03305
Recent years have seen a sharp increase in the number of related yet distinct advances in semantic segmentation. Here, we tackle this problem by leveraging the respective strengths of these advances. That is, we formulate a conditional random field over a four-connected graph as end-to-end trainable convolutional and recurrent networks, and estimate them via an adversarial process. Importantly, our model learns not only unary potentials but also pairwise
potentials, while aggregating multi-scale contexts and controlling higher-order inconsistencies.
We evaluate our model on two standard benchmark datasets for semantic face segmentation, achieving state-of-the-art results on both of them. |
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HuPBA; ISE; 600.098; 600.119 |
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Admin @ si @ GGM2017 |
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2932 |
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Wenjuan Gong; Xuena Zhang; Jordi Gonzalez; Andrews Sobral; Thierry Bouwmans; Changhe Tu; El-hadi Zahzah |
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Title |
Human Pose Estimation from Monocular Images: A Comprehensive Survey |
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Journal Article |
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2016 |
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Sensors |
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SENS |
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16 |
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12 |
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1966 |
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human pose estimation; human bodymodels; generativemethods; discriminativemethods; top-down methods; bottom-up methods |
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Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling
methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used. |
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ISE; 600.098; 600.119 |
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Admin @ si @ GZG2016 |
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2933 |
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Anastasios Doulamis; Nikolaos Doulamis; Marco Bertini; Jordi Gonzalez; Thomas B. Moeslund |
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Introduction to the Special Issue on the Analysis and Retrieval of Events/Actions and Workflows in Video Streams |
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2016 |
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Multimedia Tools and Applications |
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MTAP |
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75 |
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22 |
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14985-14990 |
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ISE; HUPBA |
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Admin @ si @ DDB2016 |
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2934 |
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