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P. Rodríguez, R. Dosil, X. M. Pardo, V. Leborán Grupo de Visión Artificial Departamento de Electrónica e Computación Universidade de Santiago de Compostela Automatic Generation of Initial Surfaces for Implicit Snakes
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Introduction Global Shape Model CSG Model Superquadric primitives Methodology Prior Model Construction Image Feature Extraction Matching Results and Conclusions Outline
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Introduction 3 D surface reconstruction: Segmentation with deformable models Good local approximation Need of good initial estimation
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Introduction Previous Solutions Manual initialization: is not practical in 3D Landmark registration: landmarks are not always identifiable Part decomposition techniques need of joint detection or part recovery lack of robustness when data is incomplete or noisy
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Introduction Objectives Automatic initialization of 3D medical images (CT, MRI, …) No use of landmarks Application to multi-part objects Robustness to noise and presence of other objects
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Introduction Proposal: matching with multi-part prior models Initialization by matching with prior models Robustness No need of part or joint detection Use of composite global shape models Multi-part models: CSG Primitives: Superquadrics Image features are image surface points No use of landmarks
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Average Surface Prior Model I. Modeling I. I.Prior model construction from sample images Volume Data Surface Patches II. Preprocessing II. II.Object surface points extraction III. Matching Initial Model III. III.Matching between surface model and object surface pointsIntroduction
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Global Shape Model Constructive Solid Geometry (CSG) Binary tree Leaf nodes: solid primitives Internal nodes: Boolean operations Arcs: rigid transformations Primitives: Superquadrics with global deformations
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Global Shape Model Superquadrics with global deformations Few parameters bring structural information Global Deformations: asymmetry Implicit equation
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Methodology I.Prior model construction from sample images Manual part decomposition Individual modeling of object parts Shape parameters Relative spatial distribution parameters Average Surface Prior Model I. Modeling
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Methodology I.Prior model construction from sample images Optimization with Genetic Algorithms Minimization of error function: whereand Average Surface Prior Model I. Modeling
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Methodology II.Image feature extraction 1.Smoothing by anisotropic diffusion 2.Non gradient maxima suppression 3.Hysteresis thresholding Volume Data Surface Patches II. Preprocessing
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Methodology III.Matching between model and object features Find global rigid transformation T such that the transformed model fits the object surface GA to minimize error function Surface Patches III. Matching Initial Model Prior Model
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Methodology III.Matching between model and object features Radial distance to a deformed implicit surface is difficult to calculate The following approximation is used
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Results
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Results
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Conclusions Contributions Automatization of initialization Easy handling of multipart shapes using a compound model No part or joint detection Easy optimization of the model Future work Introduction of fine tuning of individual part parameters Incorporation of other Boolean operations to the CSG model to handle concavities
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