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Jordi Vitria; M. Bressan; Petia Radeva |
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Bayesian classification of cork stoppers using class-conditional independent component analysis |
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2007 |
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IEEE Transactions on Systems, Man and Cybernetics (Part C), 37(1): 32–38 (ISI 0,482) |
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OR;MILAB;MV |
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BCNPCL @ bcnpcl @ VBR2007 |
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795 |
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Author |
Jordi Vitria; J. Llacer |
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Reconstructing 3D light microscopic images using the EM algorithm |
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1996 |
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Pattern Recognition Letters |
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17 |
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14 |
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1491–1498 |
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OR;MV |
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BCNPCL @ bcnpcl @ ViL1996 |
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74 |
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Author |
J.M. Sanchez; X. Binefa; Jordi Vitria |
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Shot Partitioning Based Recognition of Tv Commercials |
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2002 |
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Multimedia Tools and Applications, 18: 233–247, Kluwer Academic Publishers (IF: 0.421) |
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OR;MV |
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BCNPCL @ bcnpcl @ SBV2002 |
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274 |
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Author |
Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza |
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Title |
Evolving weighting schemes for the Bag of Visual Words |
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Journal Article |
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2017 |
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Neural Computing and Applications |
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Neural Computing and Applications |
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28 |
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5 |
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925–939 |
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Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision |
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The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method. |
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Springer |
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HUPBA;MV; no menciona |
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Admin @ si @ EPE2017 |
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2743 |
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Gloria Fernandez Esparrach; Jorge Bernal; Maria Lopez Ceron; Henry Cordova; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; F. Javier Sanchez |
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Exploring the clinical potential of an automatic colonic polyp detection method based on the creation of energy maps |
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2016 |
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Endoscopy |
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END |
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48 |
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9 |
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837-842 |
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Background and aims: Polyp miss-rate is a drawback of colonoscopy that increases significantly in small polyps. We explored the efficacy of an automatic computer vision method for polyp detection.
Methods: Our method relies on a model that defines polyp boundaries as valleys of image intensity. Valley information is integrated into energy maps which represent the likelihood of polyp presence.
Results: In 24 videos containing polyps from routine colonoscopies, all polyps were detected in at least one frame. Mean values of the maximum of energy map were higher in frames with polyps than without (p<0.001). Performance improved in high quality frames (AUC= 0.79, 95%CI: 0.70-0.87 vs 0.75, 95%CI: 0.66-0.83). Using 3.75 as maximum threshold value, sensitivity and specificity for detection of polyps were 70.4% (95%CI: 60.3-80.8) and 72.4% (95%CI: 61.6-84.6), respectively.
Conclusion: Energy maps showed a good performance for colonic polyp detection. This indicates a potential applicability in clinical practice. |
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MV; |
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Admin @ si @FBL2016 |
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2778 |
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