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Author |
Carles Sanchez; Jorge Bernal; Debora Gil; F. Javier Sanchez |
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Title |
On-line lumen centre detection in gastrointestinal and respiratory endoscopy |
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Conference Article |
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Year |
2013 |
Publication |
Second International Workshop Clinical Image-Based Procedures |
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8361 |
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Pages |
31-38 |
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Keywords |
Lumen centre detection; Bronchoscopy; Colonoscopy |
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Abstract  |
We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %). |
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Address |
Nagoya; Japan; September 2013 |
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Publisher |
Springer International Publishing |
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Editor |
Erdt, Marius and Linguraru, Marius George and Oyarzun Laura, Cristina and Shekhar, Raj and Wesarg, Stefan and González Ballester, Miguel Angel and Drechsler, Klaus |
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LNCS |
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ISBN |
978-3-319-05665-4 |
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800 |
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CLIP |
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Notes |
MV; IAM; 600.047; 600.044; 600.060 |
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no |
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Call Number |
Admin @ si @ SBG2013 |
Serial |
2302 |
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Author |
Ernest Valveny; Enric Marti |
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Title |
Application of deformable template matching to symbol recognition in hand-written architectural draw |
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Conference Article |
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Year |
1999 |
Publication |
Proceedings of the Fifth International Conference on |
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We propose to use deformable template matching as a new approach to recognize characters and lineal symbols in hand-written line drawings, instead of traditional methods based on vectorization and feature extraction. Bayesian formulation of the deformable template matching allows combining fidelity to the ideal shape of the symbol with maximum flexibility to get the best fit to the input image. Lineal nature of symbols can be exploited to define a suitable representation of models and the set of deformations to be applied to them. Matching, however, is done over the original binary image to avoid losing relevant features during vectorization. We have applied this method to hand-written architectural drawings and experimental results demonstrate that symbols with high distortions from ideal shape can be accurately identified. |
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Bangalore (India) |
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DAG;IAM; |
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no |
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Call Number |
IAM @ iam @ VAM1999a |
Serial |
1657 |
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