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Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Is there a pattern of Chromosome territoriality along mice spermatogenesis? |
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Conference Article |
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2017 |
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3rd Spanish MeioNet Meeting Abstract Book |
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55-56 |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Miraflores de la Sierra; Madrid; June 2017 |
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MEIONET |
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IAM; 600.096; 600.145 |
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Admin @ si @ |
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2958 |
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Bogdan Raducanu; Jordi Vitria |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Online Learning for Human-Robot Interaction |
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Conference Article |
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2007 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshop on |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Minneapolis (USA) |
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OR; MV |
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BCNPCL @ bcnpcl @ RaV2007a |
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791 |
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Author |
Sergio Escalera; Petia Radeva; Oriol Pujol |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Complex Salient Regions for Computer Vision Problems |
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Conference Article |
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2007 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshop on |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Minneapolis (USA) |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ ERP2007 |
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908 |
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Author |
George A. Triantafyllid; Nikolaos Thomos; Cristina Cañero; P. Vieyres; Michael G. Strintzis |
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Title |
A User Interface for Mobile Robotized Tele-Echography |
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Miscellaneous |
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2005 |
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3rd International Conference on Imaging Technologies in Biomedical Sciences (ITBS 2005) |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Milos Island (Greece) |
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no |
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Admin @ si @ TTC2005 |
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587 |
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Author |
M. Campos-Taberner; Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
![goto web page url](img/www.gif)
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Title |
Shared feature representations of LiDAR and optical images: Trading sparsity for semantic discrimination |
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Conference Article |
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2015 |
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IEEE International Geoscience and Remote Sensing Symposium IGARSS2015 |
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4169 - 4172 |
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This paper studies the level of complementary information conveyed by extremely high resolution LiDAR and optical images. We pursue this goal following an indirect approach via unsupervised spatial-spectral feature extraction. We used a recently presented unsupervised convolutional neural network trained to enforce both population and lifetime spar-sity in the feature representation. We derived independent and joint feature representations, and analyzed the sparsity scores and the discriminative power. Interestingly, the obtained results revealed that the RGB+LiDAR representation is no longer sparse, and the derived basis functions merge color and elevation yielding a set of more expressive colored edge filters. The joint feature representation is also more discriminative when used for clustering and topological data visualization. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Milan; Italy; July 2015 |
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IGARSS |
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LAMP; 600.079;MILAB |
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no |
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Admin @ si @ CRG2015 |
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2724 |
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Author |
Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Perception Based Representations for Computational Colour |
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Conference Article |
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Year |
2011 |
Publication |
3rd International Workshop on Computational Color Imaging |
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6626 |
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16-30 |
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colour perception, induction, naming, psychophysical data, saliency, segmentation |
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The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Milan, Italy |
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Springer-Verlag |
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Raimondo Schettini, Shoji Tominaga, Alain Trémeau |
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LNCS |
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978-3-642-20403-6 |
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CCIW |
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CIC |
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no |
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Admin @ si @ VMB2011 |
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1733 |
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Author |
Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title |
Physics-based Edge Evaluation for Improved Color Constancy |
Type |
Conference Article |
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Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
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581 – 588 |
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Edge-based color constancy makes use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as shadow, geometry, material and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Miami, USA |
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1063-6919 |
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978-1-4244-3992-8 |
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CAT;ISE |
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no |
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CAT @ cat @ GGW2009 |
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1197 |
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Permanent link to this record |
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Author |
Sergio Escalera; R. M. Martinez; Jordi Vitria; Petia Radeva; Maria Teresa Anguera |
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Title |
Dominance Detection in Face-to-face Conversations |
Type |
Conference Article |
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Year |
2009 |
Publication |
2nd IEEE Workshop on CVPR for Human communicative Behavior analysis |
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97–102 |
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Dominance is referred to the level of influence a person has in a conversation. Dominance is an important research area in social psychology, but the problem of its automatic estimation is a very recent topic in the contexts of social and wearable computing. In this paper, we focus on dominance detection from visual cues. We estimate the correlation among observers by categorizing the dominant people in a set of face-to-face conversations. Different dominance indicators from gestural communication are defined, manually annotated, and compared to the observers opinion. Moreover, the considered indicators are automatically extracted from video sequences and learnt by using binary classifiers. Results from the three analysis shows a high correlation and allows the categorization of dominant people in public discussion video sequences. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Miami, USA |
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2160-7508 |
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978-1-4244-3994-2 |
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CVPR |
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HuPBA; OR; MILAB;MV |
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no |
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BCNPCL @ bcnpcl @ EMV2009 |
Serial |
1227 |
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Permanent link to this record |
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Author |
Jose Manuel Alvarez; Theo Gevers; Antonio Lopez |
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Title |
Learning Photometric Invariance from Diversified Color Model Ensembles |
Type |
Conference Article |
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Year |
2009 |
Publication |
22nd IEEE Conference on Computer Vision and Pattern Recognition |
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565–572 |
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road detection |
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Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Miami (USA) |
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1063-6919 |
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978-1-4244-3992-8 |
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ADAS;ISE |
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no |
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ADAS @ adas @ AGL2009 |
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1169 |
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Author |
Sergio Escalera; Eloi Puertas; Petia Radeva; Oriol Pujol |
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Title |
Multimodal laughter recognition in video conversations |
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Conference Article |
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Year |
2009 |
Publication |
2nd IEEE Workshop on CVPR for Human communicative Behavior analysis |
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110–115 |
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Abstract |
Laughter detection is an important area of interest in the Affective Computing and Human-computer Interaction fields. In this paper, we propose a multi-modal methodology based on the fusion of audio and visual cues to deal with the laughter recognition problem in face-to-face conversations. The audio features are extracted from the spectogram and the video features are obtained estimating the mouth movement degree and using a smile and laughter classifier. Finally, the multi-modal cues are included in a sequential classifier. Results over videos from the public discussion blog of the New York Times show that both types of features perform better when considered together by the classifier. Moreover, the sequential methodology shows to significantly outperform the results obtained by an Adaboost classifier. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Miami (USA) |
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2160-7508 |
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978-1-4244-3994-2 |
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CVPR |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2009c |
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1188 |
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Author |
Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal |
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Title |
Local Binary Pattern for Word Spotting in Handwritten Historical Document |
Type |
Conference Article |
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Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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574-583 |
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Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data |
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Abstract |
Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Merida; Mexico; December 2016 |
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S+SSPR |
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Notes |
DAG; 600.097; 602.006; 603.053 |
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no |
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Admin @ si @ DNL2016 |
Serial |
2876 |
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Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
Type |
Conference Article |
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Year |
2016 |
Publication |
Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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10029 |
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543-552 |
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Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
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The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Merida; Mexico; December 2016 |
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Springer International Publishing |
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LNCS |
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978-3-319-49054-0 |
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S+SSPR |
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DAG; 600.097; 602.006 |
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no |
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Admin @ si @ TSF2016 |
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2877 |
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Author |
Rahat Khan; Joost Van de Weijer; Dimosthenis Karatzas; Damien Muselet |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Towards multispectral data acquisition with hand-held devices |
Type |
Conference Article |
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Year |
2013 |
Publication |
20th IEEE International Conference on Image Processing |
Abbreviated Journal |
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2053 - 2057 |
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Multispectral; mobile devices; color measurements |
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Abstract |
We propose a method to acquire multispectral data with handheld devices with front-mounted RGB cameras. We propose to use the display of the device as an illuminant while the camera captures images illuminated by the red, green and
blue primaries of the display. Three illuminants and three response functions of the camera lead to nine response values which are used for reflectance estimation. Results are promising and show that the accuracy of the spectral reconstruction improves in the range from 30-40% over the spectral
reconstruction based on a single illuminant. Furthermore, we propose to compute sensor-illuminant aware linear basis by discarding the part of the reflectances that falls in the sensorilluminant null-space. We show experimentally that optimizing reflectance estimation on these new basis functions decreases
the RMSE significantly over basis functions that are independent to sensor-illuminant. We conclude that, multispectral data acquisition is potentially possible with consumer hand-held devices such as tablets, mobiles, and laptops, opening up applications which are currently considered to be unrealistic. |
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Melbourne; Australia; September 2013 |
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CIC; DAG; 600.048 |
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Admin @ si @ KWK2013b |
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2265 |
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Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras |
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Title |
Intrinsic Image Evaluation On Synthetic Complex Scenes |
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2013 |
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20th IEEE International Conference on Image Processing |
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285 - 289 |
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Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes. |
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Melbourne; Australia; September 2013 |
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CIC; 600.048; 600.052; 600.051 |
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Admin @ si @ BSW2013 |
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2264 |
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Xinhang Song; Shuqiang Jiang; Luis Herranz |
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Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
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2017 |
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26th International Joint Conference on Artificial Intelligence |
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4523-4529 |
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Robotics and Vision; Vision and Perception |
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Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Melbourne; Australia; August 2017 |
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IJCAI |
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LAMP; 600.120 |
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Admin @ si @ SJH2017b |
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2966 |
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