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Multivariate Image Analysis (multivariate + image_analysis)
Selected AbstractsStandardization of line-scan NIR imaging systemsJOURNAL OF CHEMOMETRICS, Issue 3-4 2007Zheng Liu Abstract A simple and easy to use method is proposed for standardizing NIR imaging systems for differences among detectors in the charge-coupled device (CCD) array and illumination unevenness. The standardization equations are then used to pre-treat NIR image data to reduce the systematic errors introduced by a line-scan NIR imaging system. The method requires only easily available homogeneous standards with relatively uniform spectral response. The effectiveness of the standardization in reducing the pixel-to-pixel biases and other systematic effects is illustrated with examples, and the improved sensitivity in results obtained from a multivariate image analysis (MIA) based on multi-way principal component analysis (MPCA) is demonstrated. Copyright © 2007 John Wiley & Sons, Ltd. [source] Integration of colour and textural information in multivariate image analysis: defect detection and classification issuesJOURNAL OF CHEMOMETRICS, Issue 1-2 2007J. M. Prats-Montalbán Abstract In industrial processes, the detection and visualisation of defects and the development of efficient automated classification tools are strategic issues, especially when dealing with random colour textures (RCTs). This paper discusses the benefits of integrating colour and spatial (i.e. textural) information of digital RGB colour images in multivariate image analysis (MIA) to deal with these topics. Regarding the first one, a simple and computational cost-effective monitoring procedure based on colour-textural MIA merged with multivariate statistical process control (MSPC) ideas is outlined. Two novel computed images: T2 and RSS Images are proposed. The procedure is applied on digital RGB colour images from artificial stone plates. With respect to the second issue, when colour-textural MIA is used for image classification a lot of factors (e.g. pre-processing, modelling,,,) likely affecting the success rate in the classification (SRC) show up. This paper presents a methodology based on the combination of experimental design and logistic regression for choosing the best combination of factors to maximise the SRC of different types of images. Digital RGB colour images from ceramic tiles and orange fruits are used to illustrate the potential of the proposed methodology. Copyright © 2007 John Wiley & Sons, Ltd. [source] Fast algorithm for the solution of large-scale non-negativity-constrained least squares problemsJOURNAL OF CHEMOMETRICS, Issue 10 2004Mark H. Van Benthem Abstract Algorithms for multivariate image analysis and other large-scale applications of multivariate curve resolution (MCR) typically employ constrained alternating least squares (ALS) procedures in their solution. The solution to a least squares problem under general linear equality and inequality constraints can be reduced to the solution of a non-negativity-constrained least squares (NNLS) problem. Thus the efficiency of the solution to any constrained least square problem rests heavily on the underlying NNLS algorithm. We present a new NNLS solution algorithm that is appropriate to large-scale MCR and other ALS applications. Our new algorithm rearranges the calculations in the standard active set NNLS method on the basis of combinatorial reasoning. This rearrangement serves to reduce substantially the computational burden required for NNLS problems having large numbers of observation vectors. Copyright © 2005 John Wiley & Sons, Ltd. [source] Analysis of video images from a gas,liquid transfer experiment: a comparison of PCA and PARAFAC for multivariate image analysisJOURNAL OF CHEMOMETRICS, Issue 7 2003Stephen P. Gurden Abstract The use of chemical imaging is a developing area which has potential benefits for chemical systems where spatial distribution is important. Examples include processes in which homogeneity is critical, such as polymerizations, pharmaceutical powder blending and surface catalysis, and dynamic processes such as the study of diffusion rates or the transport of environmental pollutants. Whilst single images can be used to determine chemical distribution patterns at a given point in time, dynamic processes can be studied using a sequence of images measured at regular time intervals, i.e. a movie. Multivariate modeling of image data can help to provide insight into the important chemical factors present. However, many issues of how best to apply these models remain unclear, especially when the data arrays involved have four or five different dimensions (height, width, wavelength, time, experiment number, etc.). In this paper we describe the analysis of video images recorded during an experiment to investigate the uptake of CO2 across a free air,water interface. The use of PCA and PARAFAC for the analysis of both single images and movies is described and some differences and similarities are highlighted. Some other image transformation techniques, such as chemical mapping and histograms, are found to be useful both for pretreatment of the raw data and for dimensionality reduction of the data arrays prior to further modeling. Copyright © 2003 John Wiley & Sons, Ltd. [source] Monitoring roughness and edge shape on semiconductors through multiresolution and multivariate image analysisAICHE JOURNAL, Issue 5 2009Pierantonio Facco Abstract Photolithography is one of the most important processes in the production of integrated circuits. Usually, attentive inspections are required after this process, but are limited to the measurement of some physical parameters such as the critical dimension and the line edge roughness. In this paper, a novel multiresolution multivariate technique is presented to identify the abnormalities on the surface of a photolithographed device and the location of defects in a sensitive fashion by comparing it to a reference optimum, and generating fast, meaningful and reliable information. After analyzing the semiconductor surface image in different levels of resolutions via wavelet decomposition, the application of multivariate statistical monitoring tools allows the in-depth examination of the imprinted features of the product. A two level nested PCA model is used for surface roughness monitoring, while a new strategy based on "spatial moving window" PCA is proposed to analyze the shape of the patterned surface. The effectiveness of the proposed approach is tested in the case of semiconductor surface SEM images after the photolithography process. The approach is general and can be applied also to inspect a product through different types of images, different phases of the same production systems, or different processes. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] | ||||||||||