| Records |
| Author |
Alicia Fornes; Veronica Romero; Arnau Baro; Juan Ignacio Toledo; Joan Andreu Sanchez; Enrique Vidal; Josep Llados |
| Title |
ICDAR2017 Competition on Information Extraction in Historical Handwritten Records |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages  |
1389-1394 |
| Keywords |
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| Abstract |
The extraction of relevant information from historical handwritten document collections is one of the key steps in order to make these manuscripts available for access and searches. In this competition, the goal is to detect the named entities and assign each of them a semantic category, and therefore, to simulate the filling in of a knowledge database. This paper describes the dataset, the tasks, the evaluation metrics, the participants methods and the results. |
| Address |
Kyoto; Japan; November 2017 |
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Conference |
ICDAR |
| Notes |
DAG; 600.097; 601.225; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ FRB2017 |
Serial |
3052 |
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| Author |
Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Klaus McDonald Maier |
| Title |
Effects of Non-Driving Related Tasks during Self-Driving mode |
Type |
Journal Article |
| Year |
2022 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
| Volume |
23 |
Issue |
2 |
Pages |
1391-1399 |
| Keywords |
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| Abstract |
Perception reaction time and mental workload have proven to be crucial in manual driving. Moreover, in highly automated cars, where most of the research is focusing on Level 4 Autonomous driving, take-over performance is also a key factor when taking road safety into account. This study aims to investigate how the immersion in non-driving related tasks affects the take-over performance of drivers in given scenarios. The paper also highlights the use of virtual simulators to gather efficient data that can be crucial in easing the transition between manual and autonomous driving scenarios. The use of Computer Aided Simulations is of absolute importance in this day and age since the automotive industry is rapidly moving towards Autonomous technology. An experiment comprising of 40 subjects was performed to examine the reaction times of driver and the influence of other variables in the success of take-over performance in highly automated driving under different circumstances within a highway virtual environment. The results reflect the relationship between reaction times under different scenarios that the drivers might face under the circumstances stated above as well as the importance of variables such as velocity in the success on regaining car control after automated driving. The implications of the results acquired are important for understanding the criteria needed for designing Human Machine Interfaces specifically aimed towards automated driving conditions. Understanding the need to keep drivers in the loop during automation, whilst allowing drivers to safely engage in other non-driving related tasks is an important research area which can be aided by the proposed study. |
| Address |
Feb. 2022 |
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Conference |
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| Notes |
IAM; 600.139; 600.145 |
Approved |
no |
| Call Number |
Admin @ si @ MHE2022 |
Serial |
3468 |
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| Author |
Ali Furkan Biten; Andres Mafla; Lluis Gomez; Dimosthenis Karatzas |
| Title |
Is An Image Worth Five Sentences? A New Look into Semantics for Image-Text Matching |
Type |
Conference Article |
| Year |
2022 |
Publication |
Winter Conference on Applications of Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1391-1400 |
| Keywords |
Measurement; Training; Integrated circuits; Annotations; Semantics; Training data; Semisupervised learning |
| Abstract |
The task of image-text matching aims to map representations from different modalities into a common joint visual-textual embedding. However, the most widely used datasets for this task, MSCOCO and Flickr30K, are actually image captioning datasets that offer a very limited set of relationships between images and sentences in their ground-truth annotations. This limited ground truth information forces us to use evaluation metrics based on binary relevance: given a sentence query we consider only one image as relevant. However, many other relevant images or captions may be present in the dataset. In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance. Additionally, we incorporate a novel strategy that uses an image captioning metric, CIDEr, to define a Semantic Adaptive Margin (SAM) to be optimized in a standard triplet loss. By incorporating our formulation to existing models, a large improvement is obtained in scenarios where available training data is limited. We also demonstrate that the performance on the annotated image-caption pairs is maintained while improving on other non-annotated relevant items when employing the full training set. The code for our new metric can be found at github. com/furkanbiten/ncsmetric and the model implementation at github. com/andrespmd/semanticadaptive_margin. |
| Address |
Virtual; Waikoloa; Hawai; USA; January 2022 |
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Expedition |
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Conference |
WACV |
| Notes |
DAG; 600.155; 302.105; |
Approved |
no |
| Call Number |
Admin @ si @ BMG2022 |
Serial |
3663 |
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| Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
| Title |
A deep co-attentive hand-based video question answering framework using multi-view skeleton |
Type |
Journal Article |
| Year |
2023 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
| Volume |
82 |
Issue |
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Pages |
1401–1429 |
| Keywords |
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| Abstract |
In this paper, we present a novel hand –based Video Question Answering framework, entitled Multi-View Video Question Answering (MV-VQA), employing the Single Shot Detector (SSD), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and Co-Attention mechanism with RGB videos as the inputs. Our model includes three main blocks: vision, language, and attention. In the vision block, we employ a novel representation to obtain some efficient multiview features from the hand object using the combination of five 3DCNNs and one LSTM network. To obtain the question embedding, we use the BERT model in language block. Finally, we employ a co-attention mechanism on vision and language features to recognize the final answer. For the first time, we propose such a hand-based Video-QA framework including the multi-view hand skeleton features combined with the question embedding and co-attention mechanism. Our framework is capable of processing the arbitrary numbers of questions in the dataset annotations. There are different application domains for this framework. Here, as an application domain, we applied our framework to dynamic hand gesture recognition for the first time. Since the main object in dynamic hand gesture recognition is the human hand, we performed a step-by-step analysis of the hand detection and multi-view hand skeleton impact on the model performance. Evaluation results on five datasets, including two datasets in VideoQA, two datasets in dynamic hand gesture, and one dataset in hand action recognition show that MV-VQA outperforms state-of-the-art alternatives. |
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Conference |
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| Notes |
HUPBA |
Approved |
no |
| Call Number |
Admin @ si @ RKE2023b |
Serial |
3881 |
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| Author |
Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez |
| Title |
Camera Egomotion Estimation in the ADAS Context |
Type |
Conference Article |
| Year |
2010 |
Publication |
13th International IEEE Annual Conference on Intelligent Transportation Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1415–1420 |
| Keywords |
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| Abstract |
Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain. |
| Address |
Madeira Island (Portugal) |
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Edition |
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| ISSN |
2153-0009 |
ISBN |
978-1-4244-7657-2 |
Medium |
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| Area |
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Conference |
ITSC |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ CPL2010 |
Serial |
1425 |
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| Author |
Andres Traumann; Gholamreza Anbarjafari; Sergio Escalera |
| Title |
Accurate 3D Measurement Using Optical Depth Information |
Type |
Journal Article |
| Year |
2015 |
Publication |
Electronic Letters |
Abbreviated Journal |
EL |
| Volume |
51 |
Issue |
18 |
Pages |
1420-1422 |
| Keywords |
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| Abstract |
A novel three-dimensional measurement technique is proposed. The methodology consists in mapping from the screen coordinates reported by the optical camera to the real world, and integrating distance gradients from the beginning to the end point, while also minimising the error through fitting pixel locations to a smooth curve. The results demonstrate accuracy of less than half a centimetre using Microsoft Kinect II. |
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Conference |
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| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ TAE2015 |
Serial |
2647 |
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| Author |
Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
| Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
Type |
Journal Article |
| Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
| Volume |
30 |
Issue |
15 |
Pages |
1424–1433 |
| Keywords |
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| Abstract |
Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. |
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| Notes |
HuPBA; DAG; MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ EFP2009a |
Serial |
1180 |
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| Author |
Mohammad Rouhani; Angel Sappa |
| Title |
A Fast accurate Implicit Polynomial Fitting Approach |
Type |
Conference Article |
| Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1429–1432 |
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| Abstract |
This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons. |
| Address |
Hong-Kong |
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| ISSN |
1522-4880 |
ISBN |
978-1-4244-7992-4 |
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Conference |
ICIP |
| Notes |
ADAS |
Approved |
no |
| Call Number |
ADAS @ adas @ RoS2010b |
Serial |
1359 |
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| Author |
Jordi Gonzalez; Dani Rowe; J. Varona; Xavier Roca |
| Title |
Understanding Dynamic Scenes based on Human Sequence Evaluation |
Type |
Journal Article |
| Year |
2009 |
Publication |
Image and Vision Computing |
Abbreviated Journal |
IMAVIS |
| Volume |
27 |
Issue |
10 |
Pages |
1433–1444 |
| Keywords |
Image Sequence Evaluation; High-level processing of monitored scenes; Segmentation and tracking in complex scenes; Event recognition in dynamic scenes; Human motion understanding; Human behaviour interpretation; Natural-language text generation; Realistic demonstrators |
| Abstract |
In this paper, a Cognitive Vision System (CVS) is presented, which explains the human behaviour of monitored scenes using natural-language texts. This cognitive analysis of human movements recorded in image sequences is here referred to as Human Sequence Evaluation (HSE) which defines a set of transformation modules involved in the automatic generation of semantic descriptions from pixel values. In essence, the trajectories of human agents are obtained to generate textual interpretations of their motion, and also to infer the conceptual relationships of each agent w.r.t. its environment. For this purpose, a human behaviour model based on Situation Graph Trees (SGTs) is considered, which permits both bottom-up (hypothesis generation) and top-down (hypothesis refinement) analysis of dynamic scenes. The resulting system prototype interprets different kinds of behaviour and reports textual descriptions in multiple languages. |
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ISE |
Approved |
no |
| Call Number |
ISE @ ise @ GRV2009 |
Serial |
1211 |
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| Author |
Josep Llados; Horst Bunke; Enric Marti |
| Title |
Finding rotational symmetries by cyclic string matching |
Type |
Journal Article |
| Year |
1997 |
Publication |
Pattern recognition letters |
Abbreviated Journal |
PRL |
| Volume |
18 |
Issue |
14 |
Pages |
1435-1442 |
| Keywords |
Rotational symmetry; Reflectional symmetry; String matching |
| Abstract |
Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm |
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Elsevier |
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| Notes |
DAG;IAM; |
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no |
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IAM @ iam @ LBM1997a |
Serial |
1562 |
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| Author |
M. Visani; V.C.Kieu; Alicia Fornes; N.Journet |
| Title |
The ICDAR 2013 Music Scores Competition: Staff Removal |
Type |
Conference Article |
| Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1439-1443 |
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| Abstract |
The first competition on music scores that was organized at ICDAR in 2011 awoke the interest of researchers, who participated both at staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario: old music scores. For this purpose, we have generated a new set of images using two kinds of degradations: local noise and 3D distortions. This paper describes the dataset, distortion methods, evaluation metrics, the participant's methods and the obtained results. |
| Address |
Washington; USA; August 2013 |
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1520-5363 |
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Conference |
ICDAR |
| Notes |
DAG; 600.045; 600.061 |
Approved |
no |
| Call Number |
Admin @ si @ VKF2013 |
Serial |
2338 |
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| Author |
Debora Gil; Petia Radeva |
| Title |
A Regularized Curvature Flow Designed for a Selective Shape Restoration |
Type |
Journal Article |
| Year |
2004 |
Publication |
IEEE Transactions on Image Processing |
Abbreviated Journal |
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| Volume |
13 |
Issue |
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Pages |
1444–1458 |
| Keywords |
Geometric flows, nonlinear filtering, shape recovery. |
| Abstract |
Among all filtering techniques, those based exclu- sively on image level sets (geometric flows) have proven to be the less sensitive to the nature of noise and the most contrast preserving. A common feature to existent curvature flows is that they penalize high curvature, regardless of the curve regularity. This constitutes a major drawback since curvature extreme values are standard descriptors of the contour geometry. We argue that an operator designed with shape recovery purposes should include a term penalizing irregularity in the curvature rather than its magnitude. To this purpose, we present a novel geometric flow that includes a function that measures the degree of local irregularity present in the curve. A main advantage is that it achieves non-trivial steady states representing a smooth model of level curves in a noisy image. Performance of our approach is compared to classical filtering techniques in terms of quality in the restored image/shape and asymptotic behavior. We empirically prove that our approach is the technique that achieves the best compromise between image quality and evolution stabilization. |
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| Notes |
IAM;MILAB |
Approved |
no |
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BCNPCL @ bcnpcl @ GiR2004b |
Serial |
491 |
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| Author |
ChunYang; Xu Cheng Yin; Hong Yu; Dimosthenis Karatzas; Yu Cao |
| Title |
ICDAR2017 Robust Reading Challenge on Text Extraction from Biomedical Literature Figures (DeTEXT) |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1444-1447 |
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| Abstract |
Hundreds of millions of figures are available in the biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information and understanding biomedical documents. Unlike images in the open domain, biomedical figures present a variety of unique challenges. For example, biomedical figures typically have complex layouts, small font sizes, short text, specific text, complex symbols and irregular text arrangements. This paper presents the final results of the ICDAR 2017 Competition on Text Extraction from Biomedical Literature Figures (ICDAR2017 DeTEXT Competition), which aims at extracting (detecting and recognizing) text from biomedical literature figures. Similar to text extraction from scene images and web pictures, ICDAR2017 DeTEXT Competition includes three major tasks, i.e., text detection, cropped word recognition and end-to-end text recognition. Here, we describe in detail the data set, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods. |
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ISBN |
978-1-5386-3586-5 |
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Conference |
ICDAR |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ YCY2017 |
Serial |
3098 |
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| Author |
N.Nayef; F.Yin; I.Bizid; H.Choi; Y.Feng; Dimosthenis Karatzas; Z.Luo; Umapada Pal; Christophe Rigaud; J. Chazalon; W.Khlif; Muhammad Muzzamil Luqman; Jean-Christophe Burie; C.L.Liu; Jean-Marc Ogier |
| Title |
ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT |
Type |
Conference Article |
| Year |
2017 |
Publication |
14th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Issue |
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Pages |
1454-1459 |
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| Abstract |
Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge. |
| Address |
Kyoto; Japan; November 2017 |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
978-1-5386-3586-5 |
Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 600.121 |
Approved |
no |
| Call Number |
Admin @ si @ NYB2017 |
Serial |
3097 |
| Permanent link to this record |
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| |
| Author |
Dimosthenis Karatzas; Faisal Shafait; Seiichi Uchida; Masakazu Iwamura; Lluis Gomez; Sergi Robles; Joan Mas; David Fernandez; Jon Almazan; Lluis Pere de las Heras |
| Title |
ICDAR 2013 Robust Reading Competition |
Type |
Conference Article |
| Year |
2013 |
Publication |
12th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1484-1493 |
| Keywords |
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| Abstract |
This report presents the final results of the ICDAR 2013 Robust Reading Competition. The competition is structured in three Challenges addressing text extraction in different application domains, namely born-digital images, real scene images and real-scene videos. The Challenges are organised around specific tasks covering text localisation, text segmentation and word recognition. The competition took place in the first quarter of 2013, and received a total of 42 submissions over the different tasks offered. This report describes the datasets and ground truth specification, details the performance evaluation protocols used and presents the final results along with a brief summary of the participating methods. |
| Address |
Washington; USA; August 2013 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1520-5363 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG; 600.056 |
Approved |
no |
| Call Number |
Admin @ si @ KSU2013 |
Serial |
2318 |
| Permanent link to this record |
