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| Author | Anjan Dutta; Umapada Pal; Alicia Fornes; Josep Llados | ||||
| Title | An Efficient Staff Removal Technique from Printed Musical Documents | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1965–1968 | ||
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| Abstract | Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results. | ||||
Address  |
Istanbul (Turkey) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DPF2010 | Serial | 1420 | ||
| Permanent link to this record | |||||
| Author | Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny | ||||
| Title | Symbol Classification using Dynamic Aligned Shape Descriptor | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1957–1960 | ||
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| Abstract | Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. | ||||
| Address |
Istanbul (Turkey) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG; HUPBA; MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ FEL2010 | Serial | 1421 | ||
| Permanent link to this record | |||||
| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu | ||||
| Title | Perceptual color texture codebooks for retrieving in highly diverse texture datasets | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 866–869 | ||
| Keywords | |||||
| Abstract | Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel. | ||||
| Address |
Istanbul (Turkey) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ ASV2010b | Serial | 1426 | ||
| Permanent link to this record | |||||
| Author | Marçal Rusiñol; Farshad Nourbakhsh; Dimosthenis Karatzas; Ernest Valveny; Josep Llados | ||||
| Title | Perceptual Image Retrieval by Adding Color Information to the Shape Context Descriptor | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1594–1597 | ||
| Keywords | |||||
| Abstract | In this paper we present a method for the retrieval of images in terms of perceptual similarity. Local color information is added to the shape context descriptor in order to obtain an object description integrating both shape and color as visual cues. We use a color naming algorithm in order to represent the color information from a perceptual point of view. The proposed method has been tested in two different applications, an object retrieval scenario based on color sketch queries and a color trademark retrieval problem. Experimental results show that the addition of the color information significantly outperforms the sole use of the shape context descriptor. | ||||
| Address |
Istanbul (Turkey) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ RNK2010 | Serial | 1435 | ||
| Permanent link to this record | |||||
| Author | Albert Gordo; Florent Perronnin | ||||
| Title | A Bag-of-Pages Approach to Unordered Multi-Page Document Classification | Type | Conference Article | ||
| Year | 2010 | Publication | 20th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 1920–1923 | ||
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| Abstract | We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly outperforms a baseline system. | ||||
| Address |
Istanbul (Turkey) | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1051-4651 | ISBN | 978-1-4244-7542-1 | Medium | |
| Area | Expedition | Conference | ICPR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GoP2010 | Serial | 1480 | ||
| Permanent link to this record | |||||
| Author | David Masip; Jordi Vitria | ||||
| Title | Boosted Linear Projections for Discriminant Analysis | Type | Miscellaneous | ||
| Year | 2004 | Publication | CCIA 2004, 45–52, ISBN: 1–58603–466–9 | Abbreviated Journal | |
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| Address |
IOS Press | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MaV2004c | Serial | 510 | ||
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| Author | Eduard Vazquez; Francesc Tous; Ramon Baldrich; Maria Vanrell | ||||
| Title | n-Dimensional Distribution Reduction Preserving its Structure | Type | Book Chapter | ||
| Year | 2006 | Publication | Artificial Intelligence Research and Development, M. Polit et al. (Eds.), 146: 167–175 | Abbreviated Journal | |
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| Address |
IOS Press | ||||
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| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ VTB2006a | Serial | 681 | ||
| Permanent link to this record | |||||
| Author | Dimosthenis Karatzas;Ch. Lioutas | ||||
| Title | Software Package Development for Electron Diffraction Image Analysis | Type | Conference Article | ||
| Year | 1998 | Publication | Proceedings of the XIV Solid State Physics National Conference | Abbreviated Journal | |
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| Address |
Ioannina, Greece | ||||
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| Publisher | Place of Publication | Editor | |||
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| Notes | DAG | Approved | no | ||
| Call Number | IAM @ iam @ KaL1998 | Serial | 2045 | ||
| Permanent link to this record | |||||
| Author | Marc Oliu; Sarah Adel Bargal; Stan Sclaroff; Xavier Baro; Sergio Escalera | ||||
| Title | Multi-varied Cumulative Alignment for Domain Adaptation | Type | Conference Article | ||
| Year | 2022 | Publication | 6th International Conference on Image Analysis and Processing | Abbreviated Journal | |
| Volume | 13232 | Issue | Pages | 324–334 | |
| Keywords | Domain Adaptation; Computer vision; Neural networks | ||||
| Abstract | Domain Adaptation methods can be classified into two basic families of approaches: non-parametric and parametric. Non-parametric approaches depend on statistical indicators such as feature covariances to minimize the domain shift. Non-parametric approaches tend to be fast to compute and require no additional parameters, but they are unable to leverage probability density functions with complex internal structures. Parametric approaches, on the other hand, use models of the probability distributions as surrogates in minimizing the domain shift, but they require additional trainable parameters to model these distributions. In this work, we propose a new statistical approach to minimizing the domain shift based on stochastically projecting and evaluating the cumulative density function in both domains. As with non-parametric approaches, there are no additional trainable parameters. As with parametric approaches, the internal structure of both domains’ probability distributions is considered, thus leveraging a higher amount of information when reducing the domain shift. Evaluation on standard datasets used for Domain Adaptation shows better performance of the proposed model compared to non-parametric approaches while being competitive with parametric ones. (Code available at: https://github.com/moliusimon/mca). | ||||
| Address |
Indonesia; October 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICIAP | ||
| Notes | HuPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ OAS2022 | Serial | 3777 | ||
| Permanent link to this record | |||||
| Author | Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva | ||||
| Title | Multiresolution texture classification of ceramic tiles. | Type | Book Chapter | ||
| Year | 1999 | Publication | Recent Research developments in optical engineering, Research Signpost, 2: 213–228 | Abbreviated Journal | |
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| Address |
India | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | ADAS;CIC | Approved | no | ||
| Call Number | ADAS @ adas @ LBV1999b | Serial | 45 | ||
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| Author | Md.Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig | ||||
| Title | Food Places Classification in Egocentric Images Using Siamese Neural Networks | Type | Conference Article | ||
| Year | 2019 | Publication | 22nd International Conference of the Catalan Association of Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 145-151 | ||
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| Abstract | Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2. | ||||
| Address |
Illes Balears; October 2019 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CCIA | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ SBR2019 | Serial | 3368 | ||
| Permanent link to this record | |||||
| Author | Utkarsh Porwal; Alicia Fornes; Faisal Shafait (eds) | ||||
| Title | Frontiers in Handwriting Recognition. International Conference on Frontiers in Handwriting Recognition. 18th International Conference, ICFHR 2022 | Type | Book Whole | ||
| Year | 2022 | Publication | Frontiers in Handwriting Recognition. | Abbreviated Journal | |
| Volume | 13639 | Issue | Pages | ||
| Keywords | |||||
| Abstract | |||||
| Address |
ICFHR 2022, Hyderabad, India, December 4–7, 2022 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer | Place of Publication | Editor | Utkarsh Porwal; Alicia Fornes; Faisal Shafait | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-031-21648-0 | Medium | ||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ PFS2022 | Serial | 3809 | ||
| Permanent link to this record | |||||
| Author | Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio | ||||
| Title | The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Semantic Segmentation | ||||
| Abstract | State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.
Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. |
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| Address |
Honolulu; USA; July 2017 | ||||
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| Language | Summary Language | Original Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | MILAB; ADAS; 600.076; 600.085; 601.281 | Approved | no | ||
| Call Number | ADAS @ adas @ JDV2016 | Serial | 2866 | ||
| Permanent link to this record | |||||
| 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 | |
| Volume | Issue | Pages | |||
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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 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPRW | ||
| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017b | Serial | 2920 | ||
| Permanent link to this record | |||||
| 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 | |
| Volume | Issue | Pages | |||
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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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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | CVPR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GPR2017 | Serial | 2889 | ||
| Permanent link to this record | |||||