Image Processing and Jump Regression Analysis

Qiu, Peihua

doi:10.1002/0471733156

Cited by 135 publications

(116 citation statements)

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“…The use of image data for part quality inspection is becoming more widespread in industry (Qiu, 2005;Yan et al, 2015;Megahed et al, 2011). Random porous and cellular materials represent a category of complicated structures where novel SPC methods are needed to cope with the challenging nature of quality inspection data (Kim et al, 2014;Zhuravleva et al, 2013;Campoli et al, 2013;Banhart, 2001).…”

Section: A Motivating Real Case Studymentioning

confidence: 99%

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

et al. 2018

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The advance of sensor and information technologies is leading to data-rich industrial environments, where big amounts of data are potentially available. In this scenario, image data play a relevant role, as they can easily describe many phenomena of interest. This study focuses on images where several and similar features of interest are randomly distributed and characterized by no spatially correlated structure. Examples are pores in parts obtained via casting or additive manufacturing, voids in metal foams and light-weight components, grains in metallographic analysis, etc. The proposed approach consists of summarizing the random occurrences of the observed features via its (empirical) probability density function (PDF). In particular, a novel approach for PDF monitoring is proposed. It is based on simplicial functional principal component analysis (SFPCA), which is performed by applying an isometric isomorphism between the space of density functions, i.e., the Bayes space B 2 , and the space of square integrable functions L 2 . A simulation study shows the enhanced monitoring performances provided by the SFPCA-based profile monitoring against other competitors proposed in the literature. Eventually, a real case study dealing with the quality control of foamed materials production is discussed, to highlight a practical use of the proposed methodology.

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Section: A Motivating Real Case Studymentioning

confidence: 99%

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

et al. 2018

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“…Qiu [24] only considered the left and right estimators and combined them using similar diagnostic quantities. See also Qiu [25].…”

Section: A Very Simple Idea Here Is To Use the Weighted Residual Sumsmentioning

confidence: 99%

Smoothing and preservation of irregularities using local linear fitting

Gijbels

2008

Appl Math

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“…See Qiu (2005) for detail. When we assume there is only one discontinuity point, the location of the discontinuity point is estimated byx 0 and the jump magnitude at the point is estimated by |T |.…”

Section: Testing Proceduresmentioning

confidence: 99%

“…The asymptotic distribution of T under the null hypothesis is derived in Qiu (2005). Under some regularity conditions, T is asymptotically normal with the mean…”

Section: Testing Proceduresmentioning

confidence: 99%

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Test for Discontinuities in Nonparametric Regression

Park¹

2008

Communications for Statistical Applications and Methods

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The difference of two one-sided kernel estimators is usually used to detect the location of the discontinuity points of regression function. The large absolute value of the statistic imply discontinuity of regression function, so we may use the difference of two one-sided kernel estimators as the test statistic for testing null hypothesis of a smooth regression function. The problem is, however, we only know the asymptotic distribution of the test statistic under H 0 and we hardly expect the good performance of test if we rely solely on the asymptotic distribution for determining the critical points. In this paper, we show that if we adjust the bias of test statistic properly, the asymptotic rules hold for even small sample size situation.

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Image Processing and Jump Regression Analysis

Cited by 135 publications

References 0 publications

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

Profile Monitoring of Probability Density Functions via Simplicial Functional PCA With Application to Image Data

Smoothing and preservation of irregularities using local linear fitting

Test for Discontinuities in Nonparametric Regression

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