Q-Q Plot

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Chenghsiang Yang - One of the best experts on this subject based on the ideXlab platform.

  • a quantile quantile Plot based pattern matching for defect detection
    Pattern Recognition Letters, 2005
    Co-Authors: Duming Tsai, Chenghsiang Yang
    Abstract:

    Pattern matching has been used extensively for many machine vision applications such as optical character recognition, face detection, object detection, and defect detection. The normalized cross correlation (NCC) is the most commonly used technique in pattern matching. However, it is computationally intensive, sensitive to environmental changes such as lighting and shifting, and suffers from false alarms for a complicated image that contains partial uniform regions. In this paper, a pattern-matching scheme based on the quantile-quantile Plot (Q-Q Plot) is proposed for defect detection applications. In a Q-Q Plot, the quantiles of an inspection image are Plotted against the corresponding quantiles of the template image. The p-value of Chi-square test from the resulting Q-Q Plot is then used as the quantitative measure of similarity between two compared images. The quantile representation transforms the 2D gray-level information into the 1D quantile one. It can therefore efficiently reduce the dimensionality of the data, and accelerate the computation. Experimental results have shown that the proposed pattern-matching scheme is computationally fast and is tolerable to minor displacement and process variation. The proposed similarity measure of p-value has excellent discrimination capability to detect subtle defects, compared with the traditional measure of NCC. With a proper normalization of the Q-Q Plot, the p-value measure can be tolerable to moderate light changes. Experimental results from assembled PCB (printed circuit board) samples, IC wafers, and liquid crystal display (LCD) panels have shown the efficacy of the proposed pattern-matching scheme for defect detection.

K A Lindsay - One of the best experts on this subject based on the ideXlab platform.

  • dealing with the phenomenon of quasi complete separation and a goodness of fit test in logistic regression models in the case of long data sets
    Statistics in Biosciences, 2019
    Co-Authors: V G Vassiliadis, Ioannis Spyroglou, A G Rigas, J R Rosenberg, K A Lindsay
    Abstract:

    The phenomenon of quasi-complete separation that appears in the identification of the neuromuscular system called muscle spindle by a logistic regression model is considered. The system responds when it is affected by a number of stimuli. Both the response and the stimuli are very long binary sequences of events. In the logistic model, three functions are of special interest: the threshold, the recovery and the summation functions. The maximum likelihood estimates are obtained efficiently and very fast by using the penalized likelihood function. A validity test for the fitted model based on the randomized quantile residuals is proposed. The validity test is transformed to a goodness of fit test and the use of Q–Q Plot is also discussed.

Duming Tsai - One of the best experts on this subject based on the ideXlab platform.

  • a quantile quantile Plot based pattern matching for defect detection
    Pattern Recognition Letters, 2005
    Co-Authors: Duming Tsai, Chenghsiang Yang
    Abstract:

    Pattern matching has been used extensively for many machine vision applications such as optical character recognition, face detection, object detection, and defect detection. The normalized cross correlation (NCC) is the most commonly used technique in pattern matching. However, it is computationally intensive, sensitive to environmental changes such as lighting and shifting, and suffers from false alarms for a complicated image that contains partial uniform regions. In this paper, a pattern-matching scheme based on the quantile-quantile Plot (Q-Q Plot) is proposed for defect detection applications. In a Q-Q Plot, the quantiles of an inspection image are Plotted against the corresponding quantiles of the template image. The p-value of Chi-square test from the resulting Q-Q Plot is then used as the quantitative measure of similarity between two compared images. The quantile representation transforms the 2D gray-level information into the 1D quantile one. It can therefore efficiently reduce the dimensionality of the data, and accelerate the computation. Experimental results have shown that the proposed pattern-matching scheme is computationally fast and is tolerable to minor displacement and process variation. The proposed similarity measure of p-value has excellent discrimination capability to detect subtle defects, compared with the traditional measure of NCC. With a proper normalization of the Q-Q Plot, the p-value measure can be tolerable to moderate light changes. Experimental results from assembled PCB (printed circuit board) samples, IC wafers, and liquid crystal display (LCD) panels have shown the efficacy of the proposed pattern-matching scheme for defect detection.

V G Vassiliadis - One of the best experts on this subject based on the ideXlab platform.

  • dealing with the phenomenon of quasi complete separation and a goodness of fit test in logistic regression models in the case of long data sets
    Statistics in Biosciences, 2019
    Co-Authors: V G Vassiliadis, Ioannis Spyroglou, A G Rigas, J R Rosenberg, K A Lindsay
    Abstract:

    The phenomenon of quasi-complete separation that appears in the identification of the neuromuscular system called muscle spindle by a logistic regression model is considered. The system responds when it is affected by a number of stimuli. Both the response and the stimuli are very long binary sequences of events. In the logistic model, three functions are of special interest: the threshold, the recovery and the summation functions. The maximum likelihood estimates are obtained efficiently and very fast by using the penalized likelihood function. A validity test for the fitted model based on the randomized quantile residuals is proposed. The validity test is transformed to a goodness of fit test and the use of Q–Q Plot is also discussed.

Christian Ghiaus - One of the best experts on this subject based on the ideXlab platform.

  • experimental estimation of building energy performance by robust regression
    Energy and Buildings, 2006
    Co-Authors: Christian Ghiaus
    Abstract:

    Abstract Estimation of energy performance indexes, like the heating curve or the energy signature, requires robust regression of the heating losses on the outdoor temperature. The solution proposed in this paper is to use the range between the 1st and the 3rd quartile of the quantilequantile (q–q) Plot to check if the heating losses and the outdoor temperature have the same distribution and, if yes, to perform the regression in this range of the q–q Plot. The result is a model that conserves its prediction performance for data sets of the outdoor temperature different of those used for parameter identification. The robust model gives the overall heat transfer coefficient and the base temperature, and it may be used to estimate the energy consumption for data sets of the outdoor temperature coming from different time—space locations.