6,613 research outputs found
GLCM-based chi-square histogram distance for automatic detection of defects on patterned textures
Chi-square histogram distance is one of the distance measures that can be
used to find dissimilarity between two histograms. Motivated by the fact that
texture discrimination by human vision system is based on second-order
statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM)
that is based on second-order statistics and propose a new machine vision
algorithm for automatic defect detection on patterned textures. Input defective
images are split into several periodic blocks and GLCMs are computed after
quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact
and to reduce computation time. Dissimilarity matrix derived from chi-square
distances of the GLCMs is subjected to hierarchical clustering to automatically
identify defective and defect-free blocks. Effectiveness of the proposed method
is demonstrated through experiments on defective real-fabric images of 2 major
wallpaper groups (pmm and p4m groups).Comment: IJCVR, Vol. 2, No. 4, 2011, pp. 302-31
Model selection in High-Dimensions: A Quadratic-risk based approach
In this article we propose a general class of risk measures which can be used
for data based evaluation of parametric models. The loss function is defined as
generalized quadratic distance between the true density and the proposed model.
These distances are characterized by a simple quadratic form structure that is
adaptable through the choice of a nonnegative definite kernel and a bandwidth
parameter. Using asymptotic results for the quadratic distances we build a
quick-to-compute approximation for the risk function. Its derivation is
analogous to the Akaike Information Criterion (AIC), but unlike AIC, the
quadratic risk is a global comparison tool. The method does not require
resampling, a great advantage when point estimators are expensive to compute.
The method is illustrated using the problem of selecting the number of
components in a mixture model, where it is shown that, by using an appropriate
kernel, the method is computationally straightforward in arbitrarily high data
dimensions. In this same context it is shown that the method has some clear
advantages over AIC and BIC.Comment: Updated with reviewer suggestion
Application of the Iterated Weighted Least-Squares Fit to counting experiments
Least-squares fits are an important tool in many data analysis applications.
In this paper, we review theoretical results, which are relevant for their
application to data from counting experiments. Using a simple example, we
illustrate the well known fact that commonly used variants of the least-squares
fit applied to Poisson-distributed data produce biased estimates. The bias can
be overcome with an iterated weighted least-squares method, which produces
results identical to the maximum-likelihood method. For linear models, the
iterated weighted least-squares method converges faster than the equivalent
maximum-likelihood method, and does not require problem-specific starting
values, which may be a practical advantage. The equivalence of both methods
also holds for binomially distributed data. We further show that the unbinned
maximum-likelihood method can be derived as a limiting case of the iterated
least-squares fit when the bin width goes to zero, which demonstrates a deep
connection between the two methods.Comment: Accepted by NIM
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