Transcription of Lecture 9: Introduction to Pattern Analysis
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Lecture 9: Introduction to Pattern Analysis
Lecture 9: Introduction to Pattern Analysis g Features, patterns and classifiers g Components of a PR system g An example g Probability definitions g Bayes Theorem g Gaussian densities Intelligent Sensor Systems 1. Ricardo Gutierrez-Osuna Wright State University Features, patterns and classifiers g Feature n Feature is any distinctive aspect, quality or characteristic g Features may be symbolic ( , color) or numeric ( , height). n The combination of d features is represented as a d-dimensional column vector called a feature vector g The d-dimensional space defined by the feature vector is called feature space g Objects are represented as points in feature space. This representation is called a scatter plot x3. Feature 2. x1 Class 1. x Class 3. x = 2 . x .. x d . x1 x2. Class 2. Feature 1. Feature vector Feature space (3D) Scatter plot (2D). Intelligent Sensor Systems 2. Ricardo Gutierrez-Osuna Wright State University Features, patterns and classifiers g Pattern n Pattern is a composite of traits or features characteristic of an individual n In classification, a Pattern is a pair of variables {x, } where g x is a collection of observations or features (feature vector).
Intelligent Sensor Systems Ricardo Gutierrez-Osuna Wright State University 1 Lecture 9: Introduction to Pattern Analysis g Features, patterns and classifiers g …
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