Enhanced identification of defective pre-coated paints on metal through simple temperature-perturbed infrared measurement in conjunction with two-trace two-dimensional correlation analysis

Author(s):  
Youngtaek Ma ◽  
Daun Seol ◽  
Kyungmin Nam ◽  
Woosuk Sohng ◽  
Hoeil Chung
Author(s):  
Kumud Arora ◽  
Poonam Garg

Face pose recognition is one of the challenging areas in computer vision. Cross-pose change causes the change in the information of face appearance. The maximization of intrasubject correlation helps to widen the intersubject differences which helps further in achieving pose invariance. In this paper, for cross pose recognition, the authors propose to maximize the cross pose correlation by using the logically concatenated cross binary pattern (LC-CBP) descriptor and two dimensional canonical correlation analysis (2DCCA). The LC-CBP descriptor extracts the local texture details of face images with low computation complexity and the 2DCCA explicitly searches for the maximization of the correlated features to retain most informative content. Joint feature consideration via 2DCCA helps in setting up a better correspondence between a discrete set of nonfrontal pose and the frontal pose of the same subject. Experimental results demonstrate the two dimensional canonical correlation LC-CBP descriptor along with intensity values improve the correlation.


ACS Nano ◽  
2019 ◽  
Vol 13 (12) ◽  
pp. 14274-14282 ◽  
Author(s):  
Per Niklas Hedde ◽  
Elina Staaf ◽  
Sunitha Bagawath Singh ◽  
Sofia Johansson ◽  
Enrico Gratton

2017 ◽  
Vol 72 (2) ◽  
pp. 288-296 ◽  
Author(s):  
Michał Kwaśniewicz ◽  
Mirosław A. Czarnecki

Effect of the chain length on mid-infrared (MIR) and near-infrared (NIR) spectra of aliphatic 1-alcohols from methanol to 1-decanol was examined in detail. Of particular interest were the spectra-structure correlations in the NIR region and the correlation between MIR and NIR spectra of 1-alcohols. An application of two-dimensional correlation analysis (2D-COS) and chemometric methods provided comprehensive information on spectral changes in the data set. Principal component analysis (PCA) and cluster analysis evidenced that the spectra of methanol, ethanol, and 1-propanol are noticeably different from the spectra of higher 1-alcohols. The similarity between the spectra increases with an increase in the chain length. Hence, the most similar are the spectra of 1-nonanol and 1-decanol. Two-dimensional hetero-correlation analysis is very helpful for identification of the origin of bands and may guide selection of the best spectral ranges for the chemometric analysis. As shown, normalization of the spectra pronounces the intensity changes in various spectral regions and provides information not accessible from the raw data. The spectra of alcohols cannot be represented as a sum of the CH3, CH2, and OH group spectra since the OH group is involved in the hydrogen bonding. As a result, the spectral changes of this group are nonlinear and its spectral profile cannot be properly resolved. Finally, this work provides a lot of evidence that the degree of self-association of 1-alcohols decreases with the increase in chain length because of the growing meaning of the hydrophobic interactions. For butyl alcohol and higher 1-alcohols the hydrophobic interactions are more important than the OH OH interactions. Therefore, methanol, ethanol, and 1-propanol have unlimited miscibility with water, whereas 1-butanol and higher 1-alcohols have limited miscibility with water.


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