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Author | Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas | ||||
Title | Improving patch‐based scene text script identification with ensembles of conjoined networks | Type | Journal Article | ||
Year | 2017 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 67 | Issue | Pages | 85-96 | |
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Notes | DAG; 600.084; 600.121; 600.129 | Approved | no | ||
Call Number | Admin @ si @ GNK2017 | Serial | 2887 | ||
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Author | Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas | ||||
Title | Self‐supervised learning of visual features through embedding images into text topic spaces | Type | Conference Article | ||
Year | 2017 | Publication | 30th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
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Abstract | End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches. | ||||
Address | Honolulu; Hawaii; July 2017 | ||||
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Area | Expedition | Conference | CVPR | ||
Notes | DAG; 600.084; 600.121 | Approved | no | ||
Call Number | Admin @ si @ GPR2017 | Serial | 2889 | ||
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Author | Marçal Rusiñol; Josep Llados | ||||
Title | Flowchart Recognition in Patent Information Retrieval | Type | Book Chapter | ||
Year | 2017 | Publication | Current Challenges in Patent Information Retrieval | Abbreviated Journal | |
Volume | 37 | Issue | Pages | 351-368 | |
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | M. Lupu; K. Mayer; N. Kando; A.J. Trippe | |
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Notes | DAG; 600.097; 600.121 | Approved | no | ||
Call Number | Admin @ si @ RuL2017 | Serial | 2896 | ||
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Author | Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu | ||||
Title | Joint coarse-and-fine reasoning for deep optical flow | Type | Conference Article | ||
Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal | |
Volume | Issue | Pages | 2558-2562 | ||
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Abstract | We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. | ||||
Address | Beijing; China; September 2017 | ||||
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Area | Expedition | Conference | ICIP | ||
Notes | ADAS; 600.118 | Approved | no | ||
Call Number | Admin @ si @ VRM2017 | Serial | 2898 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Cross-Spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
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Abstract | The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
classical approaches, showing improvements, and a state of the art CNN based approach. |
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Address | San Sebastian; Spain; May 2017 | ||||
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Area | Expedition | Conference | ECMSM | ||
Notes | ADAS; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSV2017a | Serial | 2916 | ||
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Author | Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa | ||||
Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | Type | Conference Article | ||
Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
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Abstract | In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point that minimizes the vacuum force needed to guarantee a grasp. Experimental results in real scenarios are presented to show the validity of the proposed approach. |
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Address | San Sebastian; Spain; May 2017 | ||||
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Area | Expedition | Conference | ECMSM | ||
Notes | ADAS; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ VIS2017 | Serial | 2917 | ||
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Author | Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo | ||||
Title | RGBN Multispectral Images: a Novel Color Restoration Approach | Type | Conference Article | ||
Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Multispectral Imaging; Free Sensor Model; Neural Network | ||||
Abstract | This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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Address | Porto; Portugal; June 2017 | ||||
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Language | Summary Language | Original Title | |||
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Area | Expedition | Conference | PAAMS | ||
Notes | ADAS; MSIAU; 600.118; 600.122 | Approved | no | ||
Call Number | Admin @ si @ ASS2017 | Serial | 2918 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | CNN in multispectral imaging; Image colorization | ||||
Abstract | 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 dierent 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. | ||||
Address | Porto; Portugal; June 2017 | ||||
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Area | Expedition | Conference | PAAMS | ||
Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 2919 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Infrared Image Colorization based on a Triplet DCGAN Architecture | Type | Conference Article | ||
Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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Abstract | 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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Address | Honolulu; Hawaii; USA; July 2017 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | ADAS; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSV2017b | Serial | 2920 | ||
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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 | ||||
Title | Design of an Explainable Machine Learning Challenge for Video Interviews | Type | Conference Article | ||
Year | 2017 | Publication | International Joint Conference on Neural Networks | Abbreviated Journal | |
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Abstract | 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. | ||||
Address | Anchorage; Alaska; USA; May 2017 | ||||
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Area | Expedition | Conference | IJCNN | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EGE2017 | Serial | 2922 | ||
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Author | Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera | ||||
Title | Exploiting feature representations through similarity learning and ranking aggregation for person re-identification | Type | Conference Article | ||
Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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Abstract | 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. | ||||
Address | Washington; DC; USA; May 2017 | ||||
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Area | Expedition | Conference | FG | ||
Notes | HUPBA; 602.143 | Approved | no | ||
Call Number | Admin @ si @ JBE2017 | Serial | 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 | ||||
Title | Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases | Type | Conference Article | ||
Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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Abstract | 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. | ||||
Address | Washington; DC; USA; May 2017 | ||||
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Area | Expedition | Conference | FG | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ LJG2017 | Serial | 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 | ||||
Title | Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification | Type | Conference Article | ||
Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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Abstract | 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. | ||||
Address | Washington; DC; USA; May 2017 | ||||
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Area | Expedition | Conference | FG | ||
Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ LRL2017 | Serial | 2925 | ||
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Author | Pau Rodriguez; Guillem Cucurull; Jordi Gonzalez; Josep M. Gonfaus; Kamal Nasrollahi; Thomas B. Moeslund; Xavier Roca | ||||
Title | Deep Pain: Exploiting Long Short-Term Memory Networks for Facial Expression Classification | Type | Journal Article | ||
Year | 2017 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | Cyber |
Volume | Issue | Pages | 1-11 | ||
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Abstract | 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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Notes | ISE; 600.119; 600.098 | Approved | no | ||
Call Number | Admin @ si @ RCG2017a | Serial | 2926 | ||
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Author | Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca | ||||
Title | Regularizing CNNs with Locally Constrained Decorrelations | Type | Conference Article | ||
Year | 2017 | Publication | 5th International Conference on Learning Representations | Abbreviated Journal | |
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Address | Toulon; France; April 2017 | ||||
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Area | Expedition | Conference | ICLR | ||
Notes | ISE; 602.143; 600.119; 600.098 | Approved | no | ||
Call Number | Admin @ si @ RGC2017 | Serial | 2927 | ||
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