In this paper we present a multiresolution scheme using local polynomial regression theory in order to design a more accurate prediction operator.
In this paper we use tools of statistical learning in order to design a more accurate prediction operator in this framework based on a training sample, resulting in multiresolution decompositions with enhanced sparsity.
The results confirm that the proposed enhancements to GLTP such as the use of the more accurate Scharr operator, dimensionality reduction via PCA and facial component extraction, when combined, further improve the recognition accuracy of GLTP.
GLTP also makes use of the inaccurate Sobel operator for computing the gradient magnitude image when more accurate gradient operators could have been used.
Improvements to GLTP such as the use of enhanced pre-processing, a more accurate Scharr gradient operator, dimensionality reduction via PCA and facial component extraction were proposed.
These include the use of a more accurate Scharr gradient operator, dimensionality reduction to reduce the size of the feature vector, and facial component extraction.
In this paper, GLTP is investigated in more detail and further improvements such as the use of enhanced pre-processing, a more accurate Scharr gradient operator, dimensionality reduction via principal component analysis (PCA) and facial component extraction are proposed.
Thus, this machine is faster and more accurate than human operators and can replace them.
Assumed to be more accurate and less operator-dependent than visual analysis, quantification is increasingly used in positron emission tomography (PET) studies in routine practice or clinical trials.
When used in combination with computerised endocardial contour tracking algorithms, it indeed permits faster, more accurate and less operator-dependent quantitation of LV volumes and function than 1-DE or 2-DE.
