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The performance of a classifier is largely dependent on the size and representativeness of data used for its training. In circumstances where accumulation and/or labeling of training samples is difficult or expensive, such as medical applications, data augmentation can potentially be used to alleviate the limitations of small datasets. We have previously developed an image blending tool that allows...
Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good...
We have investigated a technique for recognising faces invariant of facial expressions. We apply multi-linear tensor algebra, which subsumes linear algebra, to analyse and recognise 3D face surfaces. This potent framework possesses a remarkable ability to deal with the shortcomings of principle component analysis in less constrained situations. A set of vector spaces can be used to represent the variation...
In this paper, we propose a tensor-based active appearance model (AAM) which improves the fitting performance of conventional AAM. Tensor-based AAM generates the specific AAM basis vectors by indexing the model tensor in terms of the estimated input image variations. Experimental results show that the proposed tensor-based AAM reduces the average fitting error than the conventional AAM significantly.
The research presented in this paper aims at developing and validating a predictive tool of individual exposure to solar Ultra-Violet (UV). UV exposure depends on ambient irradiation level and individual factors related to activity (position to the sun, clothing, duration of exposure, and other forms of sun protection). We predict exposure levels of body parts on basis of ambient irradiation levels...
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