PATTERN RECOGNITION BASED ON PRINCIPAL COMPONENT ANALYSIS AND DECISION THEORY, KPR Institute Engineering and Technology, Autonomous Engineering Institution, Coimbatore, India

1. The Students have understood the basic concepts of pattern recognition
2. The students learned how to apply Principal component analysis and decision theory algorithms for image classification
3. The Students have been trained to analyze real-time images for feature extraction and image

Department of Biomedical Engineering, KPRIET in association with Industry Institute Partnership Cell (IIPC) is conducting a webinar with the Industry person Mr. R. Raghul as the Resource person for III & IV year Biomedical Engineering students on 01.09.2022 between 3.00 pm to 4.00 pm through Online mode using Google meet platform. The webinar delivered the topic of Pattern recognition and its necessities. Mr. R. Krishna Kumar, AP/BME delivered Introduction to the Resource Person and explained his achievements and contributions in Academics and also as a professional. The Resource person delivered his speech on Principal Component Analysis (PCA) and Decision theory and how it is used to analyze Patterns. The Resource person has introduced Pattern recognition and explained PCA and Decision theory for analyzing patterns on Images and extracting the necessary information using suitable techniques. The Students have understood the basic concepts of pattern recognition. The students learned how to apply Principal component analysis and decision theory algorithms for image classification. The Students have been trained to analyze real-time images for feature extraction and image. Mr. R. Raghul delivered the content to students and the questionnaire session was conducted. The concept of Principal Component Analysis was understood by the Students. The concept of Computer Vision topic was explained. The PCA reconstruction was explained in PCA as an important mechanism in the PCA analysis. He explained the image processing techniques and image restoration, segmentation, and finding regions of interest. The Students have asked about their doubts regarding Pattern Recognition and Decision theory. Finally, the session ended with a vote of thanks by a Final year student.