Thermographic imaging techniques produce images whose pixel values represent the temperature distribution of an object. This research focuses on features that can be utilized for the classification of syrinx identification (CLMS/COMS). The thermographic image data from 19° C to 40° C was linearly remapped to create images with 256 gray level values. Features were extracted from these images, including histogram, second-order histogram based texture, and Laws texture features. Various pattern classification methods have performed well in thermographic image classification problems such as k-nearest neighbor and nearest neighbor for classification, softmax scaling and standard normal density for data normalization, and comparison metrics such as Euclidean distance and normalized vector inner product. Experiments with these features and methods are conducted to determine the best feature set and pattern classification algorithm for syrinx identification in canines.
|Advisor:||Umbaugh, Scott E.|
|Commitee:||LeAnder, Robert, York, Timothy|
|School:||Southern Illinois University at Edwardsville|
|School Location:||United States -- Illinois|
|Source:||MAI 58/04M(E), Masters Abstracts International|
|Subjects:||Engineering, Electrical engineering|
|Keywords:||Chiari dogs, Syrinx, Thermographic images|
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