WEBINAR, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

To understand the basic concepts of pattern recognition
To apply Principal component analysis and decision theory algorithms for image classification
To train and analyze real-time images for feature extraction and image classification

To understand the basic concepts of pattern recognition
To apply Principal component analysis and decision theory algorithms for image classification
To train and analyze real-time images for feature extraction and image classification
Pattern recognition is the major field in image feature extraction and classification. Principal Component Analysis is mainly used as the dimensionality reduction technique in various AI applications. It can also be used for finding hidden patterns if data has high dimensions. PCA is effective for regression and classification problems with high-dimensional input data. It works by estimating a sequence of principal components that have maximal dependence on the response variable. Decision Theory is a fundamental statistical approach to the problem of pattern classification. It is considered as the ideal pattern classifier and often used as the benchmark for other algorithms because its decision rule automatically minimizes its loss function.
The webinar was organized for IV year Biomedical Engineering Students on 01.11.2022 from 03:00 a.m. to 04:00 pm. Total of 53 students and two faculty members attended the webinar and got benefited. The webinar gave an overview of image feature extraction and classification based on PCA and decision theory. The algorithm and its importance in Pattern recognition and classification was summarized. The mathematical deviations involved in the algorithms was explained briefly. Types of methods along with their advantages and disadvantages in decision theory was stated. The resource person demonstrated the application using OpenCV with the training and testing sample dataset. The working of PCA in real time was clearly explained.