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Author  |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman | ||||
| Title | A Performance Characterization Algorithm for Symbol Localization | Type | Conference Article | ||
| Year | 2009 | Publication | 8th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3-11 | ||
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| Abstract | In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols). | ||||
| Address | La Rochelle; July 2009 | ||||
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| Publisher | Springer | Place of Publication | Editor | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DRV2009 | Serial | 1443 | ||
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| Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman | ||||
| Title | A Performance Characterization Algorithm for Symbol Localization | Type | Book Chapter | ||
| Year | 2010 | Publication | Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers | Abbreviated Journal | |
| Volume | 6020 | Issue | Pages | 260–271 | |
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| Abstract | In this paper we present an algorithm for performance characterization of symbol localization systems. This algorithm is aimed to be a more “reliable” and “open” solution to characterize the performance. To achieve that, it exploits only single points as the result of localization and offers the possibility to reconsider the localization results provided by a system. We use the information about context in groundtruth, and overall localization results, to detect the ambiguous localization results. A probability score is computed for each matching between a localization point and a groundtruth region, depending on the spatial distribution of the other regions in the groundtruth. Final characterization is given with detection rate/probability score plots, describing the sets of possible interpretations of the localization results, according to a given confidence rate. We present experimentation details along with the results for the symbol localization system of [1], exploiting a synthetic dataset of architectural floorplans and electrical diagrams (composed of 200 images and 3861 symbols). | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-13727-3 | Medium | |
| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DRV2010 | Serial | 2406 | ||
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| Author |
Mathieu Nicolas Delalandre; Jean-Marc Ogier; Josep Llados | ||||
| Title | A Fast System for the Retrieval of Ornamental Letter Image | Type | Conference Article | ||
| Year | 2007 | Publication | Seventh IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 51–54 | ||
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| Address | Curitiba (Brasil) | ||||
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| Area | Expedition | Conference | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DOL2007 | Serial | 841 | ||
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| Author |
Mathieu Nicolas Delalandre; Jean-Marc Ogier; Josep Llados | ||||
| Title | A Fast Cbir System of Old Ornamental Letter | Type | Book Chapter | ||
| Year | 2008 | Publication | Graphics Reognition: Recent Advances and New Opportunities | Abbreviated Journal | |
| Volume | 5046 | Issue | Pages | 135–144 | |
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| Publisher | Place of Publication | Editor | W. Liu, J. Llados, J.M. Ogier | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DOL2008 | Serial | 987 | ||
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| Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Tony Pridmore; Dimosthenis Karatzas | ||||
| Title | Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems | Type | Journal Article | ||
| Year | 2010 | Publication | International Journal on Document Analysis and Recognition | Abbreviated Journal | IJDAR |
| Volume | 13 | Issue | 3 | Pages | 187-207 |
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| Abstract | This paper deals with the topic of performance evaluation of symbol recognition & spotting systems. We propose here a new approach to the generation of synthetic graphics documents containing non-isolated symbols in a real context. This approach is based on the definition of a set of constraints that permit us to place the symbols on a pre-defined background according to the properties of a particular domain (architecture, electronics, engineering, etc.). In this way, we can obtain a large amount of images resembling real documents by simply defining the set of constraints and providing a few pre-defined backgrounds. As documents are synthetically generated, the groundtruth (the location and the label of every symbol) becomes automatically available. We have applied this approach to the generation of a large database of architectural drawings and electronic diagrams, which shows the flexibility of the system. Performance evaluation experiments of a symbol localization system show that our approach permits to generate documents with different features that are reflected in variation of localization results. | ||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1433-2833 | ISBN | Medium | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DVP2010 | Serial | 1289 | ||
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| Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados | ||||
| Title | Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview | Type | Report | ||
| Year | 2008 | Publication | CVC Technical Report #117 | Abbreviated Journal | |
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| Address | Bellaterra (Spain) | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DVL2008a | Serial | 946 | ||
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| Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados | ||||
| Title | Performance Evaluation of Symbol Recognition and Spotting Systems | Type | Conference Article | ||
| Year | 2008 | Publication | Proceedings of the 8th International Workshop on Document Analysis Systems, | Abbreviated Journal | |
| Volume | Issue | Pages | 497–505 | ||
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| Address | Nara (Japan) | ||||
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| Area | Expedition | Conference | DAS | ||
| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ DVL2008b | Serial | 1060 | ||
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| Author |
Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski | ||||
| Title | Bayesian Flow Networks in Continual Learning | Type | Miscellaneous | ||
| Year | 2023 | Publication | arxiv | Abbreviated Journal | |
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| Abstract | Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type. Their power comes from the expressiveness of neural networks and Bayesian inference which make them suitable in the context of continual learning. We delve into the mechanics behind BFNs and conduct the experiments to empirically verify the generative capabilities on non-stationary data. | ||||
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| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ PDT2023 | Serial | 3972 | ||
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| Author |
Matej Kristan; Jiri Matas; Martin Danelljan; Michael Felsberg; Hyung Jin Chang; Luka Cehovin Zajc; Alan Lukezic; Ondrej Drbohlav; Zhongqun Zhang; Khanh-Tung Tran; Xuan-Son Vu; Johanna Bjorklund; Christoph Mayer; Yushan Zhang; Lei Ke; Jie Zhao; Gustavo Fernandez; Noor Al-Shakarji; Dong An; Michael Arens; Stefan Becker; Goutam Bhat; Sebastian Bullinger; Antoni B. Chan; Shijie Chang; Hanyuan Chen; Xin Chen; Yan Chen; Zhenyu Chen; Yangming Cheng; Yutao Cui; Chunyuan Deng; Jiahua Dong; Matteo Dunnhofer; Wei Feng; Jianlong Fu; Jie Gao; Ruize Han; Zeqi Hao; Jun-Yan He; Keji He; Zhenyu He; Xiantao Hu; Kaer Huang; Yuqing Huang; Yi Jiang; Ben Kang; Jin-Peng Lan; Hyungjun Lee; Chenyang Li; Jiahao Li; Ning Li; Wangkai Li; Xiaodi Li; Xin Li; Pengyu Liu; Yue Liu; Huchuan Lu; Bin Luo; Ping Luo; Yinchao Ma; Deshui Miao; Christian Micheloni; Kannappan Palaniappan; Hancheol Park; Matthieu Paul; HouWen Peng; Zekun Qian; Gani Rahmon; Norbert Scherer-Negenborn; Pengcheng Shao; Wooksu Shin; Elham Soltani Kazemi; Tianhui Song; Rainer Stiefelhagen; Rui Sun; Chuanming Tang; Zhangyong Tang; Imad Eddine Toubal; Jack Valmadre; Joost van de Weijer; Luc Van Gool; Jash Vira; Stephane Vujasinovic; Cheng Wan; Jia Wan; Dong Wang; Fei Wang; Feifan Wang; He Wang; Limin Wang; Song Wang; Yaowei Wang; Zhepeng Wang; Gangshan Wu; Jiannan Wu; Qiangqiang Wu; Xiaojun Wu; Anqi Xiao; Jinxia Xie; Chenlong Xu; Min Xu; Tianyang Xu; Yuanyou Xu; Bin Yan; Dawei Yang; Ming-Hsuan Yang; Tianyu Yang; Yi Yang; Zongxin Yang; Xuanwu Yin; Fisher Yu; Hongyuan Yu; Qianjin Yu; Weichen Yu; YongSheng Yuan; Zehuan Yuan; Jianlin Zhang; Lu Zhang; Tianzhu Zhang; Guodongfang Zhao; Shaochuan Zhao; Yaozong Zheng; Bineng Zhong; Jiawen Zhu; Xuefeng Zhu; Yueting Zhuang; ChengAo Zong; Kunlong Zuo | ||||
| Title | The First Visual Object Tracking Segmentation VOTS2023 Challenge Results | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 1796-1818 | ||
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| Abstract | The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website\footnote https://www.votchallenge.net/vots2023/. | ||||
| Address | Paris; France; October 2023 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | LAMP | Approved | no | ||
| Call Number | Admin @ si @ KMD2023 | Serial | 3939 | ||
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| Author |
Masakazu Iwamura; Naoyuki Morimoto; Keishi Tainaka; Dena Bazazian; Lluis Gomez; Dimosthenis Karatzas | ||||
| Title | ICDAR2017 Robust Reading Challenge on Omnidirectional Video | Type | Conference Article | ||
| Year | 2017 | Publication | 14th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
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| Abstract | Results of ICDAR 2017 Robust Reading Challenge on Omnidirectional Video are presented. This competition uses Downtown Osaka Scene Text (DOST) Dataset that was captured in Osaka, Japan with an omnidirectional camera. Hence, it consists of sequential images (videos) of different view angles. Regarding the sequential images as videos (video mode), two tasks of localisation and end-to-end recognition are prepared. Regarding them as a set of still images (still image mode), three tasks of localisation, cropped word recognition and end-to-end recognition are prepared. As the dataset has been captured in Japan, the dataset contains Japanese text but also include text consisting of alphanumeric characters (Latin text). Hence, a submitted result for each task is evaluated in three ways: using Japanese only ground truth (GT), using Latin only GT and using combined GTs of both. Finally, by the submission deadline, we have received two submissions in the text localisation task of the still image mode. We intend to continue the competition in the open mode. Expecting further submissions, in this report we provide baseline results in all the tasks in addition to the submissions from the community. | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ IMT2017 | Serial | 3077 | ||
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| Author |
Maryam Asadi-Aghbolaghi; Hugo Bertiche; Vicent Roig; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-temporal Handcrafted Features and Deep Strategies | Type | Conference Article | ||
| Year | 2017 | Publication | Chalearn Workshop on Action, Gesture, and Emotion Recognition: Large Scale Multimodal Gesture Recognition and Real versus Fake expressed emotions at ICCV | Abbreviated Journal | |
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| Address | Venice; Italy; October 2017 | ||||
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| Area | Expedition | Conference | ICCVW | ||
| Notes | HUPBA; no menciona | Approved | no | ||
| Call Number | Admin @ si @ ABR2017 | Serial | 3068 | ||
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| Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera | ||||
| Title | Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey | Type | Book Chapter | ||
| Year | 2017 | Publication | Gesture Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 539-578 | ||
| Keywords | Action recognition; Gesture recognition; Deep learning architectures; Fusion strategies | ||||
| Abstract | Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ ACB2017a | Serial | 2981 | ||
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| Author |
Maryam Asadi-Aghbolaghi; Albert Clapes; Marco Bellantonio; Hugo Jair Escalante; Victor Ponce; Xavier Baro; Isabelle Guyon; Shohreh Kasaei; Sergio Escalera | ||||
| Title | A survey on deep learning based approaches for action and gesture recognition in image sequences | Type | Conference Article | ||
| Year | 2017 | Publication | 12th IEEE International Conference on Automatic Face and Gesture Recognition | Abbreviated Journal | |
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| Abstract | The interest in action and gesture recognition has grown considerably in the last years. In this paper, we present a survey on current deep learning methodologies for action and gesture recognition in image sequences. We introduce a taxonomy that summarizes important aspects of deep learning
for approaching both tasks. We review the details of the proposed architectures, fusion strategies, main datasets, and competitions. We summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, discussing their main features and identify opportunities and challenges for future research. |
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| Address | Washington; USA; May 2017 | ||||
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| Area | Expedition | Conference | FG | ||
| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ ACB2017b | Serial | 2982 | ||
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| Author |
Marwa Dhiaf; Mohamed Ali Souibgui; Kai Wang; Yuyang Liu; Yousri Kessentini; Alicia Fornes; Ahmed Cheikh Rouhou | ||||
| Title | CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
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| Abstract | Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, where data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance on English, Italian and Russian scripts, whilst adding only a few parameters per task. The code and trained models will be publicly available. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ DSW2023 | Serial | 3851 | ||
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| Author |
Martin Menchon; Estefania Talavera; Jose M. Massa; Petia Radeva | ||||
| Title | Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams | Type | Conference Article | ||
| Year | 2020 | Publication | ECCV Workshops | Abbreviated Journal | |
| Volume | 12538 | Issue | Pages | 469-484 | |
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| Abstract | The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person’s patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle. | ||||
| Address | Virtual; August 2020 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ECCVW | ||
| Notes | MILAB; no proj | Approved | no | ||
| Call Number | Admin @ si @ MTM2020 | Serial | 3528 | ||
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