Dr. Peter Bajorski
at Rochester Institute of Technology
SPIE Involvement:
Senior status | Author | Instructor
Publications (17)

PROCEEDINGS ARTICLE | March 27, 2018
Proc. SPIE. 10599, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII
KEYWORDS: Statistical analysis, Data modeling, Sensors, Calibration, Error analysis, Nondestructive evaluation, Measurement devices, Statistical modeling, Precision calibration, Data analysis

PROCEEDINGS ARTICLE | May 17, 2016
Proc. SPIE. 9866, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping
KEYWORDS: Near infrared, Visible radiation, Potassium, Magnesium, Nitrogen, Reflectivity, Zinc, Spectral resolution, Boron, Phosphorus


PROCEEDINGS ARTICLE | May 20, 2011
Proc. SPIE. 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
KEYWORDS: Target detection, Hyperspectral imaging, Image fusion, Sensors, Image segmentation, Composites, Solids, Differential equations, Taxonomy, Lawrencium

SPIE Journal Paper | January 1, 2010
JEI Vol. 19 Issue 01
KEYWORDS: Image quality, Printing, RGB color model, CMYK color model, Modulation transfer functions, Quality measurement, Statistical analysis, Visualization, Image processing, Profiling

PROCEEDINGS ARTICLE | April 27, 2009
Proc. SPIE. 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
KEYWORDS: Target detection, Hyperspectral imaging, Principal component analysis, Matrices, Image processing, Remote sensing, Reflectivity, Clouds, Electrical engineering, Current controlled current source

Showing 5 of 17 publications
Course Instructor
SC1072: Statistics for Imaging and Sensor Data
The purpose of this course is to survey fundamental statistical methods in the context of imaging and sensing applications. You will learn the tools and how to apply them correctly in a given context. The instructor will clarify many misconceptions associated with using statistical methods. The course is full of practical and useful examples of analyses of imaging data. Intuitive and geometric understanding of the introduced concepts will be emphasized. The topics covered include hypothesis testing, confidence intervals, regression methods, and statistical signal processing (and its relationship to linear models). We will also discuss outlier detection, the method of Monte Carlo simulations, and bootstrap.
SC806: Advanced Multivariate Statistics for Imaging
In this course, you will learn some of the more advanced tools for the analysis of multivariate data. The topics covered include canonical correlation analysis, discrimination and classification (supervised learning), Fisher discrimination, independent component analysis (ICA), and a new method of nonnegative PCA. These tools are being used more frequently in a wide range of imaging applications, so it is important for a user to know how and in what context they should be used. The instructor will emphasize intuitive and geometric understanding of the introduced concepts and will also explain the relationships among these methods, clarifying misconceptions about them. All methods will be discussed in the context of practical and useful examples of imaging data.
SC837: Multivariate Analysis of Imaging and Sensor Data
In this course, you will learn useful tools for the analysis of data on many variables (such as data on many spectral bands or on several responses observed in an experiment). You will identify the benefits of incorporating information from several variables as opposed to analyzing each variable separately. Through understanding the principles behind the analytical tools, you will be able to decide when these tools should or should not be used in practice. Many practical and useful examples of analyses of imaging data are included. The instructor will emphasize intuitive and geometric understanding of the introduced concepts. The topics covered include multivariate descriptive statistics, multivariate normal (Gaussian) distribution, multivariate confidence intervals, confidence regions, principal component analysis (PCA), canonical correlation analysis, discrimination and classification (supervised learning), Fisher discrimination, and independent component analysis (ICA).
SC804: Introduction to Statistics for Imaging
The purpose of this course is a survey of the fundamental statistical methods in the context of imaging applications. You will learn the tools and how to apply them correctly in a given context. The instructor will clarify many misconceptions associated with using statistical methods. The course is full of practical and useful examples of analyses of imaging data, and the instructor will emphasize intuitive and geometric understanding of the introduced concepts. The topics covered include hypothesis testing, confidence intervals, and regression methods.
SC805: Principles of Multivariate Statistics for Imaging
In this course, you will learn the basic tools for the analysis of data on many variables (such as data on many spectral bands or on several responses observed in an experiment). You will identify the benefits of incorporating information from several variables as opposed to analyzing each variable separately. Through understanding the principles behind the statistical tools, you will be able to decide when these tools should or should not be used in practice. Many practical and useful examples of analyses of imaging data are included. The instructor will emphasize intuitive and geometric understanding of the introduced concepts. The topics covered include multivariate descriptive statistics, statistical (Mahalanobis) distance, multivariate normal (Gaussian) distribution, multivariate confidence intervals, confidence regions, and principal component analysis (PCA).
SC913: Multivariate Analysis of Optical and Imaging Data
The abundance of data in optical and imaging applications requires sophisticated, often multivariate, methods of analysis. In this course, you will learn useful tools for the analysis of data on many variables (such as data on many spectral bands, or on several responses observed on the same experimental unit). You will identify the benefits of incorporating information from several variables as opposed to analyzing each variable separately. Through understanding the principles behind the analytical tools, you will be able to decide when these tools should or should not be used in practice. Many practical and useful examples of analyses of optical and imaging data are included from the areas of remote sensing, multispectral and hyperspectral imaging, signal processing, and color science. The instructor will emphasize intuitive and geometric understanding of the introduced concepts. The topics covered include multivariate descriptive statistics, multivariate normal (Gaussian) distribution, multivariate confidence intervals, confidence regions, principal component analysis (PCA), canonical correlation analysis, discrimination and classification (supervised learning), Fisher discrimination, independent component analysis (ICA), and a new method of nonnegative PCA.
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