Unsupervised contour representation and estimation using B-splines and a minimum description length criterion
This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the cont...
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| Veröffentlicht in: | IEEE transactions on image processing 2000-06, Vol.9 (6), p.1075-1087 |
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| container_title | IEEE transactions on image processing |
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| creator | Figueiredo, M.A.T. Leitao, J.M.N. Jain, A.K. |
| description | This paper describes a new approach to adaptive estimation of parametric deformable contours based on B-spline representations. The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach. |
| doi_str_mv | 10.1109/83.846249 |
| format | Article |
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The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. 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The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach.</description><subject>Adaptive estimation</subject><subject>Biomedical imaging</subject><subject>Counting circuits</subject><subject>Criteria</subject><subject>Deformable models</subject><subject>Deformation</subject><subject>Formability</subject><subject>Image analysis</subject><subject>Image segmentation</subject><subject>Iterative algorithms</subject><subject>Mathematical models</subject><subject>Morphology</subject><subject>Parameter estimation</subject><subject>Parametrization</subject><subject>Representations</subject><subject>Shape</subject><subject>Spline</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqF0btP3zAQB3ALteI9sHZAEQOoQ-DOj9geAUFbCakLzFEeF2qUOKmdIPW_ryE_UakDTH7cxyfrvowdIZwjgr0w4tzIgku7xXbRSswBJP-U9qB0rlHaHbYX4xMASoXFNttBw5WSWu-y4cHHZaLw7CK1WTP6eVxCFmgKFMnP1exGn1W-zSjObliPS3T-MbvK49Q7T_G1XGWD825Yhqyl2AQ3vcqe_OP8K0vnmUK6OGCfu6qPdLhZ99nD7c399ff87ue3H9eXd3mjuJhzKZCgBqFaYXhlSHLOW11Bp7sGoKsbTRYV1A0ZwkJ1oASaohaFtbKjSoh9drb2ncL4e0lfLwcXG-r7ytO4xNKiLJQxCj6UWkg0VsBLz9N3JTdSowD7MdSFFFrzBE_-g09p-D4NpjRGmkIrpRL6uqImjDEG6soppCDCnxKhfEm_NKJc00_2eNNwqQdq_8lN3Al8WYEjorfy5vVfnlmyWw</recordid><startdate>20000601</startdate><enddate>20000601</enddate><creator>Figueiredo, M.A.T.</creator><creator>Leitao, J.M.N.</creator><creator>Jain, A.K.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The problem is formulated in a statistical framework with the likelihood function being derived from a region-based image model. The parameters of the image model, the contour parameters, and the B-spline parameterization order (i.e., the number of control points) are all considered unknown. The parameterization order is estimated via a minimum description length (MDL) type criterion. A deterministic iterative algorithm is developed to implement the derived contour estimation criterion, the result is an unsupervised parametric deformable contour: it adapts its degree of smoothness/complexity (number of control points) and it also estimates the observation (image) model parameters. The experiments reported in the paper, performed on synthetic and real (medical) images, confirm the adequate and good performance of the approach.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>18255477</pmid><doi>10.1109/83.846249</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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| issn | 1057-7149 1941-0042 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive estimation Biomedical imaging Counting circuits Criteria Deformable models Deformation Formability Image analysis Image segmentation Iterative algorithms Mathematical models Morphology Parameter estimation Parametrization Representations Shape Spline |
| title | Unsupervised contour representation and estimation using B-splines and a minimum description length criterion |
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