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Dimensionality reduction of dynamic mesh animations using HO-SVD

Romaszewski M., Gawron P., Opozda S.

Journal of Artificial Intelligence and Soft Computing Research

2013

Vol. 3, No. 4

277--289

This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.

A software framework for tensor representation and decompositions for object recognition

Cyganek B.

Image Processing & Communications

2010

Vol. 15, no 2

5-16

The paper presents a framework for object detection by the tensor decomposition, called Higher Order Singular Value Decomposition (HOSVD), of the space of the training patterns. This allows a direct control over a number of dimensions inherent to the pattern space. The pattern space can be build from the available prototypes, as well as their geometrically deformed versions. Such strategy allows recognition of shifted and rotated patterns. In the paper a software framework for efficient representation and manipulations of tensors is also discussed. Tensors are stored in the matricized form with simultaneous abstraction imposed on tensor indices thanks to the proxy design pattern. This allows minimization of data copying, e.g. in the process of tensor decomposition. Finally, the whole framework was tested in the system of driver drowsiness control in which it is used for eye recognition. The latter is called TensorEye processing.