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Author  |
Matthias S. Keil; Agata Lapedriza; David Masip; Jordi Vitria |
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Title |
Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine |
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2008 |
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PLoS ONE 3(7):e2590, DOI:10.1371/journal.pone.0002590 |
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OR;MV |
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BCNPCL @ bcnpcl @ KLM2008 |
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978 |
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Author |
Matthias S. Keil |
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Title |
Smooth Gradient Representations as a Unifying Account of Chevreul’s Illusion, Mach Bands, and a Variant of the Ehrenstein Disk |
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2006 |
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Neural Computation |
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NEURALCOMPUT |
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18 |
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4 |
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871–903 |
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Admin @ si @ Kei2006 |
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633 |
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Author |
Matthias Eisenmann; Annika Reinke; Vivienn Weru; Minu D. Tizabi; Fabian Isensee; Tim J. Adler; Sharib Ali; Vincent Andrearczyk; Marc Aubreville; Ujjwal Baid; Spyridon Bakas; Niranjan Balu; Sophia Bano; Jorge Bernal; Sebastian Bodenstedt; Alessandro Casella; Veronika Cheplygina; Marie Daum; Marleen de Bruijne |
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Title |
Why Is the Winner the Best? |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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19955-19966 |
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International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. |
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Vancouver; Canada; June 2023 |
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CVPR |
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ISE |
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no |
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Call Number |
Admin @ si @ ERW2023 |
Serial |
3842 |
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Author |
Mathieu Nicolas Delalandre; Tony Pridmore; Ernest Valveny; Herve Locteau; Eric Trupin |
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Title |
Building Synthetic Graphical Documents for Performance Evaluation |
Type |
Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
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Volume |
5046 |
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Pages |
288–298 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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no |
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DAG @ dag @ DPV2008 |
Serial |
988 |
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Author |
Mathieu Nicolas Delalandre; Tony Pridmore; Ernest Valveny; Eric Trupin; Herve Locteau |
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Title |
Building Synthetic Graphical Documents for Performance Evaluation |
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Conference Article |
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Year |
2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
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84–87 |
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Address |
Curitiba (Brasil) |
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GREC |
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DAG |
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no |
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Call Number |
DAG @ dag @ DPV2007 |
Serial |
840 |
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Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman |
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Title |
A Performance Characterization Algorithm for Symbol Localization |
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Conference Article |
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Year |
2009 |
Publication |
8th IAPR International Workshop on Graphics Recognition |
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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). |
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La Rochelle; July 2009 |
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Springer |
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GREC |
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DAG |
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no |
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Call Number |
DAG @ dag @ DRV2009 |
Serial |
1443 |
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Author |
Mathieu Nicolas Delalandre; Jean-Yves Ramel; Ernest Valveny; Muhammad Muzzamil Luqman |
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Title |
A Performance Characterization Algorithm for Symbol Localization |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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Volume |
6020 |
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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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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-13727-3 |
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GREC |
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DAG |
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no |
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Call Number |
Admin @ si @ DRV2010 |
Serial |
2406 |
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Author |
Mathieu Nicolas Delalandre; Jean-Marc Ogier; Josep Llados |
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Title |
A Fast System for the Retrieval of Ornamental Letter Image |
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Conference Article |
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Year |
2007 |
Publication |
Seventh IAPR International Workshop on Graphics Recognition |
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51–54 |
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Curitiba (Brasil) |
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GREC |
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DAG |
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DAG @ dag @ DOL2007 |
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841 |
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Author |
Mathieu Nicolas Delalandre; Jean-Marc Ogier; Josep Llados |
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Title |
A Fast Cbir System of Old Ornamental Letter |
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Book Chapter |
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Year |
2008 |
Publication |
Graphics Reognition: Recent Advances and New Opportunities |
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5046 |
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135–144 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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DAG @ dag @ DOL2008 |
Serial |
987 |
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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Tony Pridmore; Dimosthenis Karatzas |
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Title |
Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems |
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Journal Article |
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Year |
2010 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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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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Springer-Verlag |
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1433-2833 |
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DAG @ dag @ DVP2010 |
Serial |
1289 |
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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados |
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Title |
Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview |
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Report |
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2008 |
Publication |
CVC Technical Report #117 |
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Bellaterra (Spain) |
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DAG @ dag @ DVL2008a |
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946 |
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Author |
Mathieu Nicolas Delalandre; Ernest Valveny; Josep Llados |
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Title |
Performance Evaluation of Symbol Recognition and Spotting Systems |
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Conference Article |
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Year |
2008 |
Publication |
Proceedings of the 8th International Workshop on Document Analysis Systems, |
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497–505 |
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Nara (Japan) |
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DAS |
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DAG |
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DAG @ dag @ DVL2008b |
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1060 |
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Author |
Mateusz Pyla; Kamil Deja; Bartłomiej Twardowski; Tomasz Trzcinski |
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Title |
Bayesian Flow Networks in Continual Learning |
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Miscellaneous |
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2023 |
Publication |
arxiv |
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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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LAMP |
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Admin @ si @ PDT2023 |
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3972 |
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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 |
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Title |
The First Visual Object Tracking Segmentation VOTS2023 Challenge Results |
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Conference Article |
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2023 |
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Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops |
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1796-1818 |
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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/. |
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Paris; France; October 2023 |
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Masakazu Iwamura; Naoyuki Morimoto; Keishi Tainaka; Dena Bazazian; Lluis Gomez; Dimosthenis Karatzas |
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ICDAR2017 Robust Reading Challenge on Omnidirectional Video |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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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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DAG; 600.084; 600.121 |
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