| |
Records |
Links |
|
Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero |
|
| |
Title |
Use case visual Bag-of-Words techniques for camera based identity document classification |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
721 - 725 |
|
| |
Keywords |
|
|
| |
Abstract |
Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes. |
|
| |
Address  |
Nancy; France; August 2015 |
|
| |
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 |
ICDAR |
|
| |
Notes |
DAG; 600.077; 600.061; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HRL2015a |
Serial |
2726 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
|
| |
Title |
Attributed Graph Grammar for floor plan analysis |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
726 - 730 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. |
|
| |
Address |
Nancy; France; August 2015 |
|
| |
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 |
ICDAR |
|
| |
Notes |
DAG; 600.077; 600.061 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ HRL2015b |
Serial |
2727 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Pau Riba; Alicia Fornes; Josep Llados |
|
| |
Title |
Towards the Alignment of Handwritten Music Scores |
Type |
Conference Article |
| |
Year |
2015 |
Publication |
11th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results. |
|
| |
Address |
Nancy; France; August 2015 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer International Publishing |
Place of Publication |
|
Editor |
Bart Lamiroy; Rafael Dueire Lins |
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
978-3-319-52158-9 |
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
GREC |
|
| |
Notes |
DAG |
Approved |
no |
|
| |
Call Number |
Admin @ si @ |
Serial |
2874 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Gemma Sanchez; Josep Llados; Enric Marti |
|
| |
Title |
A string-based method to recognize symbols and structural textures in architectural plans |
Type |
Conference Article |
| |
Year |
1997 |
Publication |
2nd IAPR Workshop on Graphics Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
This paper deals with the recognition of symbols and struc- tural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clus- tering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the simila- rity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
|
| |
Address |
Nancy, France |
|
| |
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 |
DAG; IAM |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ SLE1997 |
Serial |
1498 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas |
|
| |
Title |
Single Shot Scene Text Retrieval |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
|
|
| |
Volume |
11218 |
Issue |
|
Pages |
728-744 |
|
| |
Keywords |
Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC |
|
| |
Abstract |
Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed. |
|
| |
Address |
Munich; September 2018 |
|
| |
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 |
ECCV |
|
| |
Notes |
DAG; 600.084; 601.338; 600.121; 600.129 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GMR2018 |
Serial |
3143 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
|
| |
Title |
Learning to Learn from Web Data through Deep Semantic Embeddings |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
11134 |
Issue |
|
Pages |
514-529 |
|
| |
Keywords |
|
|
| |
Abstract |
In this paper we propose to learn a multimodal image and text embedding from Web and Social Media data, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the pipeline can learn from images with associated text without supervision and perform a thourough analysis of five different text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text based image retrieval task, and we clearly outperform state of the art in the MIRFlickr dataset when training in the target data. Further we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
|
| |
Address |
Munich; Alemanya; September 2018 |
|
| |
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 |
ECCVW |
|
| |
Notes |
DAG; 600.129; 601.338; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGG2018a |
Serial |
3175 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
|
| |
Title |
Soft-PHOC Descriptor for End-to-End Word Spotting in Egocentric Scene Images |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
International Workshop on Egocentric Perception, Interaction and Computing at ECCV |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
Word spotting in natural scene images has many applications in scene understanding and visual assistance. We propose Soft-PHOC, an intermediate representation of images based on character probability maps. Our representation extends the concept of the Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We show how to use our descriptors for word spotting tasks in egocentric camera streams through an efficient text line proposal algorithm. This is based on the Hough Transform over character attribute maps followed by scoring using Dynamic Time Warping (DTW). We evaluate our results on ICDAR 2015 Challenge 4 dataset of incidental scene text captured by an egocentric camera. |
|
| |
Address |
Munich; Alemanya; September 2018 |
|
| |
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 |
ECCVW |
|
| |
Notes |
DAG; 600.129; 600.121; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BKB2018b |
Serial |
3174 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
|
| |
Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
Type |
Conference Article |
| |
Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
Abbreviated Journal |
|
|
| |
Volume |
11134 |
Issue |
|
Pages |
530-544 |
|
| |
Keywords |
|
|
| |
Abstract |
Massive tourism is becoming a big problem for some cities, such as Barcelona, due to its concentration in some neighborhoods. In this work we gather Instagram data related to Barcelona consisting on images-captions pairs and, using the text as a supervisory signal, we learn relations between images, words and neighborhoods. Our goal is to learn which visual elements appear in photos when people is posting about each neighborhood. We perform a language separate treatment of the data and show that it can be extrapolated to a tourists and locals separate analysis, and that tourism is reflected in Social Media at a neighborhood level. The presented pipeline allows analyzing the differences between the images that tourists and locals associate to the different neighborhoods. The proposed method, which can be extended to other cities or subjects, proves that Instagram data can be used to train multi-modal (image and text) machine learning models that are useful to analyze publications about a city at a neighborhood level. We publish the collected dataset, InstaBarcelona and the code used in the analysis. |
|
| |
Address |
Munich; Alemanya; September 2018 |
|
| |
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 |
ECCVW |
|
| |
Notes |
DAG; 600.129; 601.338; 600.121 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ GGG2018b |
Serial |
3176 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Carlos Boned Riera; Oriol Ramos Terrades |
|
| |
Title |
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
2186-2191 |
|
| |
Keywords |
Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition |
|
| |
Abstract |
Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks. |
|
| |
Address |
Montreal; Quebec; Canada; August 2022 |
|
| |
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 |
ICPR |
|
| |
Notes |
DAG; 600.121; 600.162 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BoR2022 |
Serial |
3741 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados |
|
| |
Title |
TWD: A New Deep E2E Model for Text Watermark Detection in Video Images |
Type |
Conference Article |
| |
Year |
2022 |
Publication |
26th International Conference on Pattern Recognition |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection |
|
| |
Abstract |
Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge |
|
| |
Address |
Montreal; Quebec; Canada; August 2022 |
|
| |
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 |
ICPR |
|
| |
Notes |
DAG; |
Approved |
no |
|
| |
Call Number |
Admin @ si @ BSA2022 |
Serial |
3788 |
|
| Permanent link to this record |
