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Author |
Antonio Lopez; D. Lloret; Joan Serrat |
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Title |
Creaseness measures for CT and MR image registration. |
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1998 |
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CVPR’98 , IEEE Computer Society, pgs.694–699 |
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Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy |
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Santa Barbara, USA. |
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ADAS |
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ADAS @ adas @ LLS1998a |
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11 |
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Author |
Antonio Lopez; Felipe Lumbreras; Joan Serrat |
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Title |
Creaseness form level set extrinsec curvature. |
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1998 |
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5th European Conference on Computer Vision (ECCV’98), Lecture Notes in Computer Science,vol 1407, pgs. 156–169 |
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Freiburg, Germany. |
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ADAS @ adas @ LLS1998b |
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12 |
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Author |
D. Lloret; Antonio Lopez; Joan Serrat |
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3-D image Processing and Modeling, workshop on non-linear model-based image analysis. |
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1998 |
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NMBIA |
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Glasgow, U.K. |
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ADAS @ adas @ LLS1998c |
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15 |
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Xavier Roca; X. Binefa; Jordi Vitria |
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A New Autofocus Algorithm for Cytological Tissue in a Microscopy Environment. |
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1998 |
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Optical Engineering. |
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OR;ISE;MV |
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BCNPCL @ bcnpcl @ RBV1998 |
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16 |
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Author |
Petia Radeva; J. Guerrero; C. Molina |
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Title |
A Physics-Based Kohonen Ring. |
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1998 |
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SPIE – Medical Imaging. |
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San Diego; CA; USA |
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MILAB |
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BCNPCL @ bcnpcl @ RGM1998 |
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19 |
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Author |
C. Molina; G.P. Prause; Petia Radeva; M. Sonka |
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Title |
Catheter Path Reconstruction from Biplane Angiography using 3D Snakes. |
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1998 |
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SPIE – Medical imaging. |
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San Diego; CA; USA |
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MILAB |
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BCNPCL @ bcnpcl @ MPR1998 |
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22 |
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Petia Radeva; A.F. Sole; Antonio Lopez; Joan Serrat |
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Title |
Detecting Nets of Linear Structures in Satellite Images. |
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1998 |
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Londres |
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ADAS;MILAB |
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ADAS @ adas @ RSL1998 |
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25 |
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X. Binefa; Petia Radeva; J.A. Cortijo; J. Garcia |
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Title |
Contour detection and color influence in defocused environtments. |
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1998 |
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MILAB |
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BCNPCL @ bcnpcl @ BRC1998 |
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28 |
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Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
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Title |
3D Vessel Reconstruction from Biplane Angiograms using Snakes. |
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1998 |
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Cleveland, OH |
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MILAB;ADAS |
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BCNPCL @ bcnpcl @ RTV1998a |
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198 |
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Petia Radeva; Ricardo Toledo; Craig Von Land; Juan J. Villanueva |
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Title |
3D Dynamic Model of the Coronary Tree. |
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1998 |
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Cleveland, OH |
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MILAB;ADAS |
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BCNPCL @ bcnpcl @ RTV1998b |
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200 |
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D. Lloret; Antonio Lopez; Joan Serrat |
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Title |
Precise registration of CT and MR volumes based on a new creaseness measure |
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1998 |
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Workshop on Non–linear model–based image analysis (NMBIA’98). S. Marshall, N. Harvey and D. Shah Eds. Springer_Verlag London, pgs 15–20 |
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Glasgow (Scottland) |
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ADAS |
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ADAS @ adas @ LLS1998d |
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487 |
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Antonio Lopez; Joan Serrat |
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Title |
Ridges and Valleys in Image Analysis |
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1998 |
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CVC Technical Report #22 |
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CVC (UAB) |
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ADAS @ adas @ LoS1998 |
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533 |
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Author |
A. Martinez; Robert Benavente |
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The AR face database |
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1998 |
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CVC Technical Report #24 |
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CAT @ cat @ MaB1998 |
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534 |
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A. Martinez; Jordi Vitria |
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Visual annotation for mobile robot navigation |
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1998 |
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CVC Technical Report #25 |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1998b |
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535 |
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Author |
Josep Llados; Gemma Sanchez; Enric Marti |
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A string based method to recognize symbols and structural textures in architectural plans |
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1998 |
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Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers |
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LNCS |
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1389 |
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1998 |
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91-103 |
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This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion. |
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IAM @ iam @ SLE1998 |
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1573 |
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