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Yagmur Gucluturk; Umut Guclu; Marc Perez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon; Carlos Andujar; Julio C. S. Jacques Junior; Meysam Madadi; Sergio Escalera |
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Title |
Visualizing Apparent Personality Analysis with Deep Residual Networks |
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Conference Article |
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2017 |
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Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV |
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3101-3109 |
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Automatic prediction of personality traits is a subjective task that has recently received much attention. Specifically, automatic apparent personality trait prediction from multimodal data has emerged as a hot topic within the filed of computer vision and, more particularly, the so called “looking
at people” sub-field. Considering “apparent” personality traits as opposed to real ones considerably reduces the subjectivity of the task. The real world applications are encountered in a wide range of domains, including entertainment, health, human computer interaction, recruitment and security. Predictive models of personality traits are useful for individuals in many scenarios (e.g., preparing for job interviews, preparing for public speaking). However, these predictions in and of themselves might be deemed to be untrustworthy without human understandable supportive evidence. Through a series of experiments on a recently released benchmark dataset for automatic apparent personality trait prediction, this paper characterizes the audio and
visual information that is used by a state-of-the-art model while making its predictions, so as to provide such supportive evidence by explaining predictions made. Additionally, the paper describes a new web application, which gives feedback on apparent personality traits of its users by combining
model predictions with their explanations. |
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Venice; Italy; October 2017 |
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ICCVW |
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HUPBA; 6002.143 |
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Admin @ si @ GGP2017 |
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3067 |
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Author |
Xose M. Pardo; Petia Radeva; Juan J. Villanueva |
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Title |
Self-Training Statistic Snake for Image Segmentation and Tracking. |
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Miscellaneous |
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1999 |
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Proceedings 10th International Conference on Image Analysis and Processing |
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406-411 |
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Venice |
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ICIAP |
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MILAB |
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BCNPCL @ bcnpcl @ PRV1999 |
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26 |
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Author |
Xinhang Song; Luis Herranz; Shuqiang Jiang |
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Title |
Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs |
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2017 |
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31st AAAI Conference on Artificial Intelligence |
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RGB-D scene recognition; weakly supervised; fine tune; CNN |
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Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data. |
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San Francisco CA; February 2017 |
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AAAI |
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LAMP; 600.120 |
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Admin @ si @ SHJ2017 |
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2967 |
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Author |
Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu |
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Title |
Which tone-mapping is the best? A comparative study of tone-mapping perceived quality |
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2014 |
Publication |
Perception |
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43 |
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106 |
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Abstract |
Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality
of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm
was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings. |
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ECVP |
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NEUROBIT; 600.074 |
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no |
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Admin @ si @ CPO2014 |
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2527 |
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Author |
Xialei Liu; Joost Van de Weijer; Andrew Bagdanov |
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Title |
RankIQA: Learning from Rankings for No-reference Image Quality Assessment |
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Conference Article |
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2017 |
Publication |
17th IEEE International Conference on Computer Vision |
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We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our approach can be made significantly more efficient than traditional Siamese Networks by forward propagating a batch of images through a single network and backpropagating gradients derived from all pairs of images in the batch. Experiments on the TID2013 benchmark show that we improve the state-of-the-art by over 5%. Furthermore, on the LIVE benchmark we show that our approach is superior to existing NR-IQA techniques and that we even outperform the state-of-the-art in full-reference IQA (FR-IQA) methods without having to resort to high-quality reference images to infer IQA. |
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Venice; Italy; October 2017 |
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ICCV |
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LAMP; 600.106; 600.109; 600.120 |
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no |
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Call Number |
Admin @ si @ LWB2017b |
Serial |
3036 |
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Author |
Xavier Soria; Angel Sappa; Arash Akbarinia |
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Title |
Multispectral Single-Sensor RGB-NIR Imaging: New Challenges and Opportunities |
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Conference Article |
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2017 |
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7th International Conference on Image Processing Theory, Tools & Applications |
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Color restoration; Neural networks; Singlesensor cameras; Multispectral images; RGB-NIR dataset |
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Abstract |
Multispectral images captured with a single sensor camera have become an attractive alternative for numerous computer vision applications. However, in order to fully exploit their potentials, the color restoration problem (RGB representation) should be addressed. This problem is more evident in outdoor scenarios containing vegetation, living beings, or specular materials. The problem of color distortion emerges from the sensitivity of sensors due to the overlap of visible and near infrared spectral bands. This paper empirically evaluates the variability of the near infrared (NIR) information with respect to the changes of light throughout the day. A tiny neural network is proposed to restore the RGB color representation from the given RGBN (Red, Green, Blue, NIR) images. In order to evaluate the proposed algorithm, different experiments on a RGBN outdoor dataset are conducted, which include various challenging cases. The obtained result shows the challenge and the importance of addressing color restoration in single sensor multispectral images. |
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Montreal; Canada; November 2017 |
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IPTA |
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NEUROBIT; MSIAU; 600.122 |
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no |
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Admin @ si @ SSA2017 |
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3074 |
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Author |
Xavier Roca; X. Binefa; Jordi Vitria |
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Title |
A New Accomodation Algorithm for a Microscopy Environment. |
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Conference Article |
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1997 |
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(SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis |
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66-67 |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ RBV1997 |
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37 |
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Xavier Roca; X. Binefa; Jordi Vitria |
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Title |
Multiscale Structure Extraction using Morphological Tools. Applications to Edge Detection and to Depth Perception. |
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1993 |
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Technical Workshop on Mathematical Morphology and its Applications to Signal Processing. |
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Barcelona |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ RBV1993 |
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247 |
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Author |
Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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Title |
Gaze control in a binocular robot systems |
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1999 |
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7th IEEE International Conference on Emerging Technologies and Factory Automation. Proceedings ETFA '99 |
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Barcelona |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ RVV1999b |
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41 |
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Xavier Roca; Jordi Vitria |
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Multiscale Structure Extraction using Morphological Tools. Applications to Edge Detection. |
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1993 |
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SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). |
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San Diego; CA; USA |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ RoV1993 |
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176 |
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Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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Title |
Brightness and colour induction through contextual influences in V1 |
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2015 |
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Scottish Vision Group 2015 SGV2015 |
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12 |
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9 |
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1208-2012 |
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Carnoustie; Scotland; March 2015 |
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NEUROBIT; |
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Admin @ si @ OPC2015a |
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2632 |
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Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort |
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2015 |
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Barcelona Computational, Cognitive and Systems Neuroscience |
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Barcelona; June 2015 |
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NEUROBIT; |
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Admin @ si @ OPC2015b |
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2634 |
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X. Orriols; Lluis Barcelo; X. Binefa |
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An Appearance-Based Method for Parametric Video Registration. |
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2003 |
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Electronic Letters on Computer Vision and Image Analysis |
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2 |
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1-11 |
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Paris |
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Admin @ si @ OBB2001b |
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145 |
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Author |
X. Binefa; Xavier Roca; Jordi Vitria |
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A Contrast Approach to Depth from Focus. |
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1997 |
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(SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis |
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Barcelona |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ BRV1997 |
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63 |
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X. Binefa; Jordi Vitria; Xavier Roca |
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Deteccion de profundidad en imagenes monoculares mediante vision activa. |
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1993 |
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Revista de Optica Pura y Aplicada |
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26 |
Issue |
3 |
Pages |
636-648 |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ BVR1993 |
Serial |
144 |
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