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Author |
S. Chanda; Oriol Ramos Terrades; Umapada Pal |
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Title |
SVM Based Scheme for Thai and English Script Identification |
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Conference Article |
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Year |
2007 |
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9th International Conference on Document Analysis and Recognition |
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1 |
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551–555 |
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Curitiba (Brazil) |
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ICDAR |
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DAG |
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no |
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DAG @ dag @ CRP2007a |
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885 |
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Author |
David Geronimo; Antonio Lopez; Angel Sappa |
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Title |
Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey |
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Conference Article |
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Year |
2007 |
Publication |
3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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Volume |
1 |
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Pages |
547–554 |
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Keywords |
Pedestrian detection |
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Abstract |
Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study. |
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Girona (Spain) |
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J. Marti et al. |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ GLS2007 |
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804 |
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Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
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Title |
Text/Graphics Separation in Color Maps |
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Conference Article |
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Year |
2007 |
Publication |
International Conference on Computing: Theory and Applications |
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545–551 |
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Kolkata (India) |
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ICCTA |
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DAG |
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DAG @ dag @ RLP2007a |
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806 |
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Author |
P. Ricaurte; C. Chilan; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Angel Sappa |
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Title |
Performance Evaluation of Feature Point Descriptors in the Infrared Domain |
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Conference Article |
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Year |
2014 |
Publication |
9th International Conference on Computer Vision Theory and Applications |
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1 |
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545-550 |
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Keywords |
Infrared Imaging; Feature Point Descriptors |
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Abstract |
This paper presents a comparative evaluation of classical feature point descriptors when they are used in the long-wave infrared spectral band. Robustness to changes in rotation, scaling, blur, and additive noise are evaluated using a state of the art framework. Statistical results using an outdoor image data set are presented together with a discussion about the differences with respect to the results obtained when images from the visible spectrum are considered. |
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Lisboa; Portugal; January 2014 |
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VISAPP |
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ADAS; 600.055; 600.076 |
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no |
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Call Number |
Admin @ si @ RCA2014b |
Serial |
2476 |
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Author |
Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados |
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Title |
Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling |
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Conference Article |
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Year |
2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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10029 |
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543-552 |
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Keywords |
Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection |
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Abstract |
The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results. |
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Merida; Mexico; December 2016 |
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Springer International Publishing |
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LNCS |
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978-3-319-49054-0 |
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S+SSPR |
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Notes |
DAG; 600.097; 602.006 |
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no |
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Call Number |
Admin @ si @ TSF2016 |
Serial |
2877 |
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Author |
Smriti Joshi; Richard Osuala; Carlos Martin-Isla; Victor M.Campello; Carla Sendra-Balcells; Karim Lekadir; Sergio Escalera |
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Title |
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging |
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Conference Article |
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Year |
2022 |
Publication |
International MICCAI Brainlesion Workshop |
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12963 |
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540–551 |
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Keywords |
Domain adaptation; Vestibular schwannoma (VS); Deep learning; nn-UNet; CycleGAN |
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Abstract |
In recent years, deep learning models have considerably advanced the performance of segmentation tasks on Brain Magnetic Resonance Imaging (MRI). However, these models show a considerable performance drop when they are evaluated on unseen data from a different distribution. Since annotation is often a hard and costly task requiring expert supervision, it is necessary to develop ways in which existing models can be adapted to the unseen domains without any additional labelled information. In this work, we explore one such technique which extends the CycleGAN [2] architecture to generate label-preserving data in the target domain. The synthetic target domain data is used to train the nn-UNet [3] framework for the task of multi-label segmentation. The experiments are conducted and evaluated on the dataset [1] provided in the ‘Cross-Modality Domain Adaptation for Medical Image Segmentation’ challenge [23] for segmentation of vestibular schwannoma (VS) tumour and cochlea on contrast enhanced (ceT1) and high resolution (hrT2) MRI scans. In the proposed approach, our model obtains dice scores (DSC) 0.73 and 0.49 for tumour and cochlea respectively on the validation set of the dataset. This indicates the applicability of the proposed technique to real-world problems where data may be obtained by different acquisition protocols as in [1] where hrT2 images are more reliable, safer, and lower-cost alternative to ceT1. |
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MICCAIW |
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Notes |
HUPBA; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ JOM2022 |
Serial |
3800 |
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Author |
Arnau Ramisa; Adriana Tapus; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors |
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Conference Article |
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Year |
2008 |
Publication |
IEEE International Conference on Robotics and Automation, |
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538–543 |
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Pasadena; CA; USA |
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ICRA |
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Notes |
RV;ADAS |
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no |
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Call Number |
Admin @ si @ RTL2008 |
Serial |
1144 |
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Author |
Wenwen Yu; Chengquan Zhang; Haoyu Cao; Wei Hua; Bohan Li; Huang Chen; Mingyu Liu; Mingrui Chen; Jianfeng Kuang; Mengjun Cheng; Yuning Du; Shikun Feng; Xiaoguang Hu; Pengyuan Lyu; Kun Yao; Yuechen Yu; Yuliang Liu; Wanxiang Che; Errui Ding; Cheng-Lin Liu; Jiebo Luo; Shuicheng Yan; Min Zhang; Dimosthenis Karatzas; Xing Sun; Jingdong Wang; Xiang Bai |
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Title |
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images |
Type |
Conference Article |
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Year |
2023 |
Publication |
17th International Conference on Document Analysis and Recognition |
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Volume |
14188 |
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Pages |
536–552 |
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Abstract |
Structured text extraction is one of the most valuable and challenging application directions in the field of Document AI. However, the scenarios of past benchmarks are limited, and the corresponding evaluation protocols usually focus on the submodules of the structured text extraction scheme. In order to eliminate these problems, we organized the ICDAR 2023 competition on Structured text extraction from Visually-Rich Document images (SVRD). We set up two tracks for SVRD including Track 1: HUST-CELL and Track 2: Baidu-FEST, where HUST-CELL aims to evaluate the end-to-end performance of Complex Entity Linking and Labeling, and Baidu-FEST focuses on evaluating the performance and generalization of Zero-shot/Few-shot Structured Text extraction from an end-to-end perspective. Compared to the current document benchmarks, our two tracks of competition benchmark enriches the scenarios greatly and contains more than 50 types of visually-rich document images (mainly from the actual enterprise applications). The competition opened on 30th December, 2022 and closed on 24th March, 2023. There are 35 participants and 91 valid submissions received for Track 1, and 15 participants and 26 valid submissions received for Track 2. In this report we will presents the motivation, competition datasets, task definition, evaluation protocol, and submission summaries. According to the performance of the submissions, we believe there is still a large gap on the expected information extraction performance for complex and zero-shot scenarios. It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Call Number |
Admin @ si @ YZC2023 |
Serial |
3896 |
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Author |
Mohamed Ilyes Lakhal; Albert Clapes; Sergio Escalera; Oswald Lanz; Andrea Cavallaro |
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Title |
Residual Stacked RNNs for Action Recognition |
Type |
Conference Article |
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Year |
2018 |
Publication |
9th International Workshop on Human Behavior Understanding |
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Pages |
534-548 |
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Keywords |
Action recognition; Deep residual learning; Two-stream RNN |
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Abstract |
Action recognition pipelines that use Recurrent Neural Networks (RNN) are currently 5–10% less accurate than Convolutional Neural Networks (CNN). While most works that use RNNs employ a 2D CNN on each frame to extract descriptors for action recognition, we extract spatiotemporal features from a 3D CNN and then learn the temporal relationship of these descriptors through a stacked residual recurrent neural network (Res-RNN). We introduce for the first time residual learning to counter the degradation problem in multi-layer RNNs, which have been successful for temporal aggregation in two-stream action recognition pipelines. Finally, we use a late fusion strategy to combine RGB and optical flow data of the two-stream Res-RNN. Experimental results show that the proposed pipeline achieves competitive results on UCF-101 and state of-the-art results for RNN-like architectures on the challenging HMDB-51 dataset. |
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Munich; September 2018 |
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ECCVW |
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HUPBA; no proj |
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no |
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Call Number |
Admin @ si @ LCE2018b |
Serial |
3206 |
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Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Learning from# Barcelona Instagram data what Locals and Tourists post about its Neighbourhoods |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision Workshops |
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11134 |
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530-544 |
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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. |
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Munich; Alemanya; September 2018 |
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ECCVW |
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DAG; 600.129; 601.338; 600.121 |
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no |
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Call Number |
Admin @ si @ GGG2018b |
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3176 |
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Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
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Title |
Hierarchical graph representation for symbol spotting in graphical document images |
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Conference Article |
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2012 |
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Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
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7626 |
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529-538 |
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Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
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Miyajima-Itsukushima, Hiroshima |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-34165-6 |
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SSPR&SPR |
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DAG |
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no |
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Admin @ si @ BDJ2012 |
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2126 |
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Author |
Jialuo Chen; Pau Riba; Alicia Fornes; Juan Mas; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Word-Hunter: A Gamesourcing Experience to Validate the Transcription of Historical Manuscripts |
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Conference Article |
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2018 |
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16th International Conference on Frontiers in Handwriting Recognition |
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528-533 |
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Crowdsourcing; Gamification; Handwritten documents; Performance evaluation |
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Nowadays, there are still many handwritten historical documents in archives waiting to be transcribed and indexed. Since manual transcription is tedious and time consuming, the automatic transcription seems the path to follow. However, the performance of current handwriting recognition techniques is not perfect, so a manual validation is mandatory. Crowdsourcing is a good strategy for manual validation, however it is a tedious task. In this paper we analyze experiences based in gamification
in order to propose and design a gamesourcing framework that increases the interest of users. Then, we describe and analyze our experience when validating the automatic transcription using the gamesourcing application. Moreover, thanks to the combination of clustering and handwriting recognition techniques, we can speed up the validation while maintaining the performance. |
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Niagara Falls, USA; August 2018 |
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ICFHR |
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DAG; 600.097; 603.057; 600.121 |
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no |
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Admin @ si @ CRF2018 |
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3169 |
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Author |
Felipe Lumbreras; Xavier Roca; Daniel Ponsa; Robert Benavente; Judit Martinez; Silvia Sanchez; Coen Antens; Juan J. Villanueva |
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Title |
Visual Inspection of Safety Belts |
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Conference Article |
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2001 |
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International Conference on Quality Control by Artificial Vision |
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2 |
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526–531 |
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France |
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ADAS;ISE;CIC |
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ADAS @ adas @ LRP2001 |
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122 |
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Author |
Hamdi Dibeklioglu; Theo Gevers; Albert Ali Salah |
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Title |
Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles |
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Conference Article |
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Year |
2012 |
Publication |
12th European Conference on Computer Vision |
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7574 |
Issue |
III |
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525-538 |
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Abstract |
Smiling is an indispensable element of nonverbal social interaction. Besides, automatic distinction between spontaneous and posed expressions is important for visual analysis of social signals. Therefore, in this paper, we propose a method to distinguish between spontaneous and posed enjoyment smiles by using the dynamics of eyelid, cheek, and lip corner movements. The discriminative power of these movements, and the effect of different fusion levels are investigated on multiple databases. Our results improve the state-of-the-art. We also introduce the largest spontaneous/posed enjoyment smile database collected to date, and report new empirical and conceptual findings on smile dynamics. The collected database consists of 1240 samples of 400 subjects. Moreover, it has the unique property of having an age range from 8 to 76 years. Large scale experiments on the new database indicate that eyelid dynamics are highly relevant for smile classification, and there are age-related differences in smile dynamics. |
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Florence, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-33711-6 |
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ECCV |
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ALTRES;ISE |
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no |
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Admin @ si @ DGS2012 |
Serial |
2024 |
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Author |
Sanket Biswas; Pau Riba; Josep Llados; Umapada Pal |
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Title |
Graph-Based Deep Generative Modelling for Document Layout Generation |
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Conference Article |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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12917 |
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525-537 |
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Abstract |
One of the major prerequisites for any deep learning approach is the availability of large-scale training data. When dealing with scanned document images in real world scenarios, the principal information of its content is stored in the layout itself. In this work, we have proposed an automated deep generative model using Graph Neural Networks (GNNs) to generate synthetic data with highly variable and plausible document layouts that can be used to train document interpretation systems, in this case, specially in digital mailroom applications. It is also the first graph-based approach for document layout generation task experimented on administrative document images, in this case, invoices. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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no |
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Admin @ si @ BRL2021 |
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3676 |
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