Representation and Recognitio2

Representation and Recognitio2 - Shape and texture applies...

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Representation and Recognition Principle Component Analysis (PCA) derives an orthogonal projection basis that leads to directly to dimensionality reduction and feature selection. - Eigenfaces = eigenvectors related to the largest eigenvalues - PCA is optimal criterion for dimensionality reduction, but does not always provide good discrimination. [Kirby90, Turk91] Solution: Integrate PCA with the Bayes classifier (Probabilistic Reasoning Models) [Liu00]
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Unformatted text preview: Shape and texture applies a two-stage procedure- Coding starts by marking important internal and boundary points.- The points are aligned using translation, scaling, and rotation.- Calculate the average of control points defines the mean shape.- Triangulate the marked face and warp each face into the mean shape.- The first stage yields the shape, the second stage yields texture. [Beymer95, Cootes98, Craw92, Edwards98, Lanitis97, Liu01]...
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Representation and Recognitio2 - Shape and texture applies...

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