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Author |
Felipe Lumbreras; Joan Serrat |
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Title |
Segmentation of petrographical images of marbles |
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1996 |
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Computers and Geosciences. 22(5):547–558 |
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ADAS @ adas @ LuS1996b |
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82 |
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Author |
J. Pladellorens; Joan Serrat; A. Castell; M.J. Yzuel |
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Title |
Using mathematical morphology to determine left ventricular contours. |
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1993 |
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Physics in Medicine and Biology. |
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37 |
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1877––1894 |
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ADAS @ adas @ PSC1993 |
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146 |
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Author |
A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
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Title |
Anisotropic 2-D and 3-D Averaging of fMRI Signals |
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2001 |
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IEEE Transactions on Medical Imaging, 20(2): 86–93 (IF: 3.142) |
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ADAS @ adas @ SNS2001 |
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165 |
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Author |
Daniel Ponsa; Robert Benavente; Felipe Lumbreras; J. Martinez; Xavier Roca |
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Quality control of safety belts by machine vision inspection for real-time production |
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2003 |
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Optical Engineering, 42:1114–1120 (IF: 0.877) |
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ADAS;ISE;CIC |
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ADAS @ adas @ PRL2003 |
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399 |
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Author |
A.F. Sole; Antonio Lopez; G. Sapiro |
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Title |
Crease Enhancement Diffusion |
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2001 |
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Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298) |
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New York; USA |
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ADAS @ adas @ SLS2001 |
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485 |
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Author |
A. Restrepo; Angel Sappa; M. Devy |
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Title |
Edge registration versus triangular mesh registration, a comparative study |
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2005 |
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Signal Processing: Image Communication 20: 853–868 (IF: 1.264) |
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ADAS @ adas @ RSD2005 |
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567 |
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Author |
Jaume Amores; Petia Radeva |
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Title |
Retrieval of IVUS Images Using Contextual Information and Elastic Matching |
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2005 |
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International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657) |
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ADAS;MILAB |
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ADAS @ adas @ AmR2005a |
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593 |
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Author |
Angel Sappa |
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Title |
Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation |
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2006 |
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IEEE Transactions on Image Processing, 15(2):377–384 |
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ADAS |
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ADAS @ adas @ Sap2006a |
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637 |
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Author |
Jaume Amores |
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Title |
Multiple Instance Classification: review, taxonomy and comparative study |
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Year |
2013 |
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Artificial Intelligence |
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AI |
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201 |
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81-105 |
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Multi-instance learning; Codebook; Bag-of-Words |
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Abstract |
Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods. |
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Elsevier Science Publishers Ltd. Essex, UK |
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0004-3702 |
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ADAS; 601.042; 600.057 |
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Admin @ si @ Amo2013 |
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2273 |
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Author |
Angel Sappa |
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Title |
Splitting up Panoramic Range Images into Compact 2½D Representations |
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2006 |
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International Journal of Imaging Systems and Technology, 16(3): 85–91 |
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ADAS @ adas @ Sap2006b |
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721 |
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