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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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2008 |
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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 @ dag @ DOL2008 |
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987 |
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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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2009 |
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8th IAPR International Workshop on Graphics Recognition |
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3-11 |
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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 @ dag @ DRV2009 |
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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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2010 |
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Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers |
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6020 |
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260–271 |
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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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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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Admin @ si @ DRV2010 |
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2406 |
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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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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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84–87 |
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Curitiba (Brasil) |
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GREC |
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DAG |
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DAG @ dag @ DPV2007 |
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840 |
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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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5046 |
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288–298 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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DAG @ dag @ DPV2008 |
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988 |
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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 |
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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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Admin @ si @ ERW2023 |
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3842 |
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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 |
Publication |
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 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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Matthias S. Keil; Gabriel Cristobal |
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Separating the chaff from the wheat: possible origins of the oblique effect |
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2000 |
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Journal of the Optical Society of America A – Optics, Image Science, and Vision, 17(4): 697–710 (IF: 1.481) |
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Admin @ si @ KeC2000 |
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630 |
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Author |
Matthias S. Keil; Gabriel Cristobal; Heiko Neumann |
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Gradient representation and perception in the early visual system – A novel account of Mach band formation |
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2006 |
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Vision Research, 46(17): 2659–2674 |
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VR |
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Admin @ si @ KCN2006 |
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649 |
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Matthias S. Keil; Gabriel Cristobal; Thorsten Hansen; Heiko Neumann |
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Recovering real-world images from single-scale boundaries with a novel filling-in architecture |
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2005 |
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Neural Networks 18(10):1319–1331 (IF: 1.665) |
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Admin @ si @ KCH2005 |
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576 |
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Matthias S. Keil; Jordi Vitria |
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Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk |
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2005 |
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European Conference on Visual Perception |
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BCNPCL @ bcnpcl @ KeV2005b |
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607 |
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Matthias S. Keil; Jordi Vitria |
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Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk |
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2005 |
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Perception 34:209–210 Suppl. S (IF: 1.391) |
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BCNPCL @ bcnpcl @ KeV2005a |
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608 |
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Matthias S. Keil; Jordi Vitria |
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Pushing it to the Limit: Adaptation with Dynamically Switching Gain Control |
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2007 |
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EURASIP Journal on Advances in Signal Processing, Vol 2007, Article ID 51684, 10 pages, doi: 10.1155/2007/51684 |
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BCNPCL @ bcnpcl @ KeV2007 |
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794 |
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Maurizio Mencuccini; Jordi Martinez-Vilalta; Josep Piñol; Lasse Loepfe; Mireia Burnat ; Xavier Alvarez; Juan Camacho; Debora Gil |
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A quantitative and statistically robust method for the determination of xylem conduit spatial distribution |
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2010 |
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American Journal of Botany |
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AJB |
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97 |
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8 |
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1247-1259 |
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Geyer; hydraulic conductivity; point pattern analysis; Ripley; Spatstat; vessel clusters; xylem anatomy; xylem network |
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Premise of the study: Because of their limited length, xylem conduits need to connect to each other to maintain water transport from roots to leaves. Conduit spatial distribution in a cross section plays an important role in aiding this connectivity. While indices of conduit spatial distribution already exist, they are not well defined statistically. * Methods: We used point pattern analysis to derive new spatial indices. One hundred and five cross-sectional images from different species were transformed into binary images. The resulting point patterns, based on the locations of the conduit centers-of-area, were analyzed to determine whether they departed from randomness. Conduit distribution was then modeled using a spatially explicit stochastic model. * Key results: The presence of conduit randomness, uniformity, or aggregation depended on the spatial scale of the analysis. The large majority of the images showed patterns significantly different from randomness at least at one spatial scale. A strong phylogenetic signal was detected in the spatial variables. * Conclusions: Conduit spatial arrangement has been largely conserved during evolution, especially at small spatial scales. Species in which conduits were aggregated in clusters had a lower conduit density compared to those with uniform distribution. Statistically sound spatial indices must be employed as an aid in the characterization of distributional patterns across species and in models of xylem water transport. Point pattern analysis is a very useful tool in identifying spatial patterns. |
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IAM; |
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no |
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Call Number |
IAM @ iam @ MMG2010 |
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1623 |
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| Permanent link to this record |
