Automatic parameter optimization of the local model fitting method for single-shot surface profiling.

The local model fitting (LMF) method is a single-shot surface profiling algorithm. Its measurement principle is based on the assumption that the target surface to be profiled is locally flat, which enables us to utilize the information brought by nearby pixels in the single interference image for robust LMF. Given that the shape and size of the local area is appropriately determined, the LMF method was demonstrated to provide very accurate measurement results.

Iteratively-reweighted local model fitting method for adaptive and accurate single-shot surface profiling.

The local model fitting (LMF) method is one of the useful single-shot surface profiling algorithms. The measurement principle of the LMF method relies on the assumption that the target surface is locally flat. Based on this assumption, the height of the surface at each pixel is estimated from pixel values in its vicinity.

Interpolated local model fitting method for accurate and fast single-shot surface profiling.

The local model fitting (LMF) method is a useful single-shot surface profiling algorithm based on spatial carrier frequency fringe patterns. The measurement principle of the LMF method relies on the assumption that the target surface is locally flat. In this paper, we first analyze the measurement error of the LMF method caused by violation of the locally flat assumption.

Single-shot surface profiling by local model fitting.

A new surface profiling algorithm called the local model fitting (LMF) method is proposed. LMF is a single-shot method that employs only a single image, so it is fast and robust against vibration. LMF does not require a conventional assumption of smoothness of the target surface in a band-limit sense, but we instead assume that the target surface is locally constant.

Modulated Hebb-Oja learning rule--a method for principal subspace analysis.

This paper presents analysis of the recently proposed modulated Hebb-Oja (MHO) method that performs linear mapping to a lower-dimensional subspace. Principal component subspace is the method that will be analyzed. Comparing to some other well-known methods for yielding principal component subspace (e.

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions.

A new modulated Hebbian learning rule--biologically plausible method for local computation of a principal subspace.

This paper presents one possible implementation of a transformation that performs linear mapping to a lower-dimensional subspace. Principal component subspace will be the one that will be analyzed. Idea implemented in this paper represents generalization of the recently proposed infinity OH neural method for principal component extraction.

Fast surface profiler by white-light interferometry by use of a new algorithm based on sampling theory.

We propose a fast surface-profiling algorithm based on white-light interferometry by use of sampling theory. We first provide a generalized sampling theorem that reconstructs the squared-envelope function of the white-light interferogram from sampled values of the interferogram and then propose the new algorithm based on the theorem. The algorithm extends the sampling interval to 1.

Optimal design of regularization term and regularization parameter by subspace information criterion.

The problem of designing the regularization term and regularization parameter for linear regression models is discussed. Previously, we derived an approximation to the generalization error called the subspace information criterion (SIC), which is an unbiased estimator of the generalization error with finite samples under certain conditions. In this paper, we apply SIC to regularization learning and use it for: (a) choosing the optimal regularization term and regularization parameter from the given candidates; (b) obtaining the closed form of the optimal regularization parameter for a fixed regularization term.