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Author |
A. M. Here; B. C. Lopez; Debora Gil; J. J. Camarero; Jordi Martinez-Vilalta |
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Title |
A new software to analyse wood anatomical features in conifer species |
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Conference Article |
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2013 |
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International Symposium on Wood Structure in Plant Biology and Ecology |
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International Symposium on Wood Structure in Plant Biology and Ecology |
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Naples; Italy; March 2013 |
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WSE |
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IAM |
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no |
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Admin @ si @ HLG2013 |
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2303 |
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Author |
S.Grau; Anna Puig; Sergio Escalera; Maria Salamo; Oscar Amoros |
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Title |
Efficient complementary viewpoint selection in volume rendering |
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Conference Article |
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2013 |
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21st WSCG Conference on Computer Graphics, |
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Dual camera; Visualization; Interactive Interfaces; Dynamic Time Warping. |
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A major goal of visualization is to appropriately express knowledge of scientific data. Generally, gathering visual information contained in the volume data often requires a lot of expertise from the final user to setup the parameters of the visualization. One way of alleviating this problem is to provide the position of inner structures with different viewpoint locations to enhance the perception and construction of the mental image. To this end, traditional illustrations use two or three different views of the regions of interest. Similarly, with the aim of assisting the users to easily place a good viewpoint location, this paper proposes an automatic and interactive method that locates different complementary viewpoints from a reference camera in volume datasets. Specifically, the proposed method combines the quantity of information each camera provides for each structure and the shape similarity of the projections of the remaining viewpoints based on Dynamic Time Warping. The selected complementary viewpoints allow a better understanding of the focused structure in several applications. Thus, the user interactively receives feedback based on several viewpoints that helps him to understand the visual information. A live-user evaluation on different data sets show a good convergence to useful complementary viewpoints. |
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978-808694374-9 |
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WSCG |
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HuPBA; 600.046;MILAB |
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Admin @ si @ GPE2013a |
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2255 |
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Author |
Arnau Baro; Pau Riba; Alicia Fornes |
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Title |
A Starting Point for Handwritten Music Recognition |
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Conference Article |
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2018 |
Publication |
1st International Workshop on Reading Music Systems |
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5-6 |
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Optical Music Recognition; Long Short-Term Memory; Convolutional Neural Networks; MUSCIMA++; CVCMUSCIMA |
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In the last years, the interest in Optical Music Recognition (OMR) has reawakened, especially since the appearance of deep learning. However, there are very few works addressing handwritten scores. In this work we describe a full OMR pipeline for handwritten music scores by using Convolutional and Recurrent Neural Networks that could serve as a baseline for the research community. |
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Paris; France; September 2018 |
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DAG; 600.097; 601.302; 601.330; 600.121 |
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Admin @ si @ BRF2018 |
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3223 |
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Author |
Arnau Baro; Carles Badal; Pau Torras; Alicia Fornes |
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Title |
Handwritten Historical Music Recognition through Sequence-to-Sequence with Attention Mechanism |
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Conference Article |
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2022 |
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3rd International Workshop on Reading Music Systems (WoRMS2021) |
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55-59 |
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Optical Music Recognition; Digits; Image Classification |
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Despite decades of research in Optical Music Recognition (OMR), the recognition of old handwritten music scores remains a challenge because of the variabilities in the handwriting styles, paper degradation, lack of standard notation, etc. Therefore, the research in OMR systems adapted to the particularities of old manuscripts is crucial to accelerate the conversion of music scores existing in archives into digital libraries, fostering the dissemination and preservation of our music heritage. In this paper we explore the adaptation of sequence-to-sequence models with attention mechanism (used in translation and handwritten text recognition) and the generation of specific synthetic data for recognizing old music scores. The experimental validation demonstrates that our approach is promising, especially when compared with long short-term memory neural networks. |
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July 23, 2021, Alicante (Spain) |
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WoRMS |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ BBT2022 |
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3734 |
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Author |
Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang |
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Title |
Improving Handwritten Music Recognition through Language Model Integration |
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Conference Article |
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Year |
2022 |
Publication |
4th International Workshop on Reading Music Systems (WoRMS2022) |
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42-46 |
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Keywords |
optical music recognition; historical sources; diversity; music theory; digital humanities |
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Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. |
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November 18, 2022 |
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WoRMS |
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DAG; 600.121; 600.162; 602.230 |
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no |
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Admin @ si @ TBF2022 |
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3735 |
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Author |
Vacit Oguz Yazici; Joost Van de Weijer; Arnau Ramisa |
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Title |
Color Naming for Multi-Color Fashion Items |
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Conference Article |
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Year |
2018 |
Publication |
6th World Conference on Information Systems and Technologies |
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747 |
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64-73 |
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Deep learning; Color; Multi-label |
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There exists a significant amount of research on color naming of single colored objects. However in reality many fashion objects consist of multiple colors. Currently, searching in fashion datasets for multi-colored objects can be a laborious task. Therefore, in this paper we focus on color naming for images with multi-color fashion items. We collect a dataset, which consists of images which may have from one up to four colors. We annotate the images with the 11 basic colors of the English language. We experiment with several designs for deep neural networks with different losses. We show that explicitly estimating the number of colors in the fashion item leads to improved results. |
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Naples; March 2018 |
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WORLDCIST |
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LAMP; 600.109; 601.309; 600.120 |
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no |
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Admin @ si @ YWR2018 |
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3161 |
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Author |
Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer |
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Title |
Does Multimodality Help Human and Machine for Translation and Image Captioning? |
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Conference Article |
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2016 |
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1st conference on machine translation |
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This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. |
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Berlin; Germany; August 2016 |
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WMT |
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LAMP; 600.106 ; 600.068 |
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no |
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Admin @ si @ CAW2016 |
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2761 |
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Author |
Ozan Caglayan; Walid Aransa; Adrien Bardet; Mercedes Garcia-Martinez; Fethi Bougares; Loic Barrault; Marc Masana; Luis Herranz; Joost Van de Weijer |
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Title |
LIUM-CVC Submissions for WMT17 Multimodal Translation Task |
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Conference Article |
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2017 |
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2nd Conference on Machine Translation |
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This paper describes the monomodal and multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT17 Shared Task on Multimodal Translation. We mainly explored two multimodal architectures where either global visual features or convolutional feature maps are integrated in order to benefit from visual context. Our final systems ranked first for both En-De and En-Fr language pairs according to the automatic evaluation metrics METEOR and BLEU. |
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LAMP; 600.106; 600.120 |
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no |
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Admin @ si @ CAB2017 |
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3035 |
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Ozan Caglayan; Adrien Bardet; Fethi Bougares; Loic Barrault; Kai Wang; Marc Masana; Luis Herranz; Joost Van de Weijer |
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LIUM-CVC Submissions for WMT18 Multimodal Translation Task |
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Conference Article |
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2018 |
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3rd Conference on Machine Translation |
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This paper describes the multimodal Neural Machine Translation systems developed by LIUM and CVC for WMT18 Shared Task on Multimodal Translation. This year we propose several modifications to our previou multimodal attention architecture in order to better integrate convolutional features and refine them using encoder-side information. Our final constrained submissions
ranked first for English→French and second for English→German language pairs among the constrained submissions according to the automatic evaluation metric METEOR. |
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Brussels; Belgium; October 2018 |
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WMT |
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LAMP; 600.106; 600.120 |
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no |
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Admin @ si @ CBB2018 |
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3240 |
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Author |
Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
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Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
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Conference Article |
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2018 |
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4th Women in Computer Vision Workshop |
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1941-19412 |
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Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
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We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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Salt Lake City; USA; June 2018 |
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WiCV |
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DAG; 600.121; 600.129 |
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no |
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Admin @ si @ DBR2018 |
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3222 |
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Adriana Romero; Carlo Gatta; Gustavo Camps-Valls |
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Title |
Unsupervised Deep Feature Extraction Of Hyperspectral Images |
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2014 |
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6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing |
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Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification |
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This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features. |
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Lausanne; Switzerland; June 2014 |
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WHISPERS |
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MILAB; LAMP; 600.079 |
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Admin @ si @ RGC2014 |
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2513 |
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Miguel Angel Bautista; Antonio Hernandez; Victor Ponce; Xavier Perez Sala; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
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Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data |
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2012 |
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21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis |
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7854 |
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126-135 |
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Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40302-6 |
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WDIA |
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MILAB; OR;HuPBA;MV |
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no |
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Admin @ si @ BHP2012 |
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2120 |
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Miguel Reyes; Albert Clapes; Luis Felipe Mejia; Jose Ramirez; Juan R Revilla; Sergio Escalera |
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Title |
Posture Analysis and Range of Movement Estimation using Depth Maps |
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2012 |
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21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis |
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7854 |
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97-105 |
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World Health Organization estimates that 80% of the world population is affected of back pain during his life. Current practices to analyze back problems are expensive, subjective, and invasive. In this work, we propose a novel tool for posture and range of movement estimation based on the analysis of 3D information from depth maps. Given a set of keypoints defined by the user, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matching using a novel point-to-point fitting procedure, and accurate measurements about posture, spinal curvature, and range of movement are computed. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent musculoskeletal disorders, such as back pain, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40302-6 |
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WDIA |
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HuPBA;MILAB |
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no |
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Admin @ si @ RCM2012 |
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2121 |
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Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera |
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Title |
Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal |
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Conference Article |
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2016 |
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4th Congreso WCPT-SAR |
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Buenos Aires; Argentina; June 2016 |
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WCPT-SAR |
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HuPBA;MILAB |
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no |
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Admin @ si @ RRR2016 |
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2853 |
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Author |
Henry Velesaca; Patricia Suarez; Angel Sappa; Dario Carpio; Rafael E. Rivadeneira; Angel Sanchez |
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Title |
Review on Common Techniques for Urban Environment Video Analytics |
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Conference Article |
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2022 |
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Anais do III Workshop Brasileiro de Cidades Inteligentes |
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107-118 |
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Video Analytics; Review; Urban Environments; Smart Cities |
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Abstract |
This work compiles the different computer vision-based approaches
from the state-of-the-art intended for video analytics in urban environments.
The manuscript groups the different approaches according to the typical modules present in video analysis, including image preprocessing, object detection,
classification, and tracking. This proposed pipeline serves as a basic guide to
representing these most representative approaches in this topic of video analysis
that will be addressed in this work. Furthermore, the manuscript is not intended
to be an exhaustive review of the most advanced approaches, but only a list of
common techniques proposed to address recurring problems in this field. |
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WBCI |
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MSIAU; 601.349 |
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no |
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Admin @ si @ VSS2022 |
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3773 |
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