Development of an automated method for classifying digital images using a combination of image quality modification and Bayesian classification is the subject of this thesis. The specific example is classification of images obtained by monitoring molten plastic in an extruder. These images were to be classified into two groups: the “with particle” (WP) group which showed contaminant particles and the “without particle” (WO) group which did not. Previous work effected the classification using only an adaptive Bayesian model. This work combines adaptive image quality modification with the adaptive Bayesian model. The first objective was to develop an off-line automated method for determining how to modify each individual raw image to obtain the quality required for improved classification results. This was done in a very novel way by defining image quality in terms of probability using a Bayesian classification model. The Nelder Mead Simplex method was then used to optimize the quality. The result was a “Reference Image Database” which was used as a basis for accomplishing the second objective. The second objective was to develop an in-line method for modifying the quality of new images to improve classification over that which could be obtained previously. Case Based Reasoning used the Reference Image Database to locate reference images similar to each new image. The database supplied instructions on how to modify the new image to obtain a better quality image. Experimental verification of the method used a variety of images from the extruder monitor including images purposefully produced to be of wide diversity. Image quality modification was made adaptive by adding new images to the Reference Image Database. When combined with adaptive classification previously employed, error rates decreased from about 10% to less than 1% for most images. For one unusually difficult set of images that exhibited very low local contrast of particles in the image against their background it was necessary to split the Reference Image Database into two parts on the basis of a critical value for local contrast. The end result of this work is a very powerful, flexible and general method for improving classification of digital images that utilizes both image quality modification and classification modeling.

Development of an automated method for classifying digital images using a combination of image quality modification and Bayesian classification is the subject of this thesis. The specific example is classification of images obtained by monitoring molten plastic in an extruder. These images were to be classified into two groups: the “with particle” (WP) group which showed contaminant particles and the “without particle” (WO) group which did not. Previous work effected the classification using only an adaptive Bayesian model. This work combines adaptive image quality modification with the adaptive Bayesian model. The first objective was to develop an off-line automated method for determining how to modify each individual raw image to obtain the quality required for improved classification results. This was done in a very novel way by defining image quality in terms of probability using a Bayesian classification model. The Nelder Mead Simplex method was then used to optimize the quality. The result was a “Reference Image Database” which was used as a basis for accomplishing the second objective. The second objective was to develop an in-line method for modifying the quality of new images to improve classification over that which could be obtained previously. Case Based Reasoning used the Reference Image Database to locate reference images similar to each new image. The database supplied instructions on how to modify the new image to obtain a better quality image. Experimental verification of the method used a variety of images from the extruder monitor including images purposefully produced to be of wide diversity. Image quality modification was made adaptive by adding new images to the Reference Image Database. When combined with adaptive classification previously employed, error rates decreased from about 10% to less than 1% for most images. For one unusually difficult set of images that exhibited very low local contrast of particles in the image against their background it was necessary to split the Reference Image Database into two parts on the basis of a critical value for local contrast. The end result of this work is a very powerful, flexible and general method for improving classification of digital images that utilizes both image quality modification and classification modeling.

abstract: In recent years, solar photovoltaic (PV) industry has seen lots of improvements in technology and of growth in market with crystalline silicon PV modules being the most widely used technology. Plant inspections are gaining much importance to identify and quantitatively determine the impacts of various visual defects on performance. There are about 86 different types of defects found in the PV modules installed in various climates and most of them can be visually observed. However, a quantitative determination of impact or risk of each of identified defect on performance is challenging. Thus, it is utmost important to quantify the risk for each of the visual defects without any human subjectivity. The best way to quantify the risk of each defect is to perform current-voltage measurements of the defective modules installed in the plant but it requires disruption of plant operation, expensive measuring equipment and intensive human resources. One of the most riskiest and dominant visual defects is encapsulant browning which affects the PV module performance in the form of current degradation. The present study deals with developing an automated image processing tool which can address the issues of human subjectivity on browning level impacting performance. The image processing tool developed in this work can be directly used to quantify the impact of browning on performance without intrusively disconnecting the modules from the plant. In this work, the quantified browning level impact on performance has also been experimentally validated through a correlation study using short-circuit current and reflectance/transmittance measurements of browned PV modules retrieved from aged plants/systems installed in diverse climatic conditions. The primary goal of the image processing tool developed in this work is to determine the performance impact of encapsulant browning without interrupting the plant operation for I-V measurements. The use of image processing tool provides a single numerical value, called browning index (BI), which can accurately quantify browning levels on modules and also correlate with the performance and reflectance/transmittance parameters of the modules. / Dissertation/Thesis / Masters Thesis Mechanical Engineering 2016