RA3711 deals with the semester VII B.E Robotics and Automation Syllabus at Anna University based on regulation 2021. In this article, we discuss the Robotic Vision and Intelligence Laboratory Syllabus syllabus along with textbooks and references.
We intend to provide a full-planned syllabus for students to gain knowledge of the syllabus. It will give students to be equipped with perfect books and the required knowledge to prepare for the examinations. These are necessary to get a qualified certificate from the university with aggregate marks. Students must perform well to take a step forward toward their careers. They must earn a qualified degree help them to achieve success in their goals. In this article, RA3711 – Robotic Vision and Intelligence Laboratory Syllabus will pioneer the way to do that. Hope the following information is useful. Don’t forget to share it with your classmates.
If you want to know more about the syllabus of B.E Robotics and Automation connected to an affiliated institution’s four-year undergraduate degree program. We provide you with a detailed Year-wise, semester-wise, and Subject-wise syllabus in the following link B.E Robotics and Automation Syllabus Anna University, Regulation 2021.
Aim Of Objectives:
- To understand various lighting techniques, design and image acquisition of machine vision system.
- To practice Feature Extraction, Image pre-processing and pattern recognition.
- To apply machine learning technique to classification and object detection.
List Of Experiments:
- Study on different kinds of vision sensors and lighting techniques for machine vision.
- Study on Design of Machine Vision System.
- Experimentation on image acquisition towards the computation platform.
- Pre-processing techniques in image processing.
- Edge detection and region of interest extraction.
- Experimentation with image processing algorithm for feature extraction.
- Experimentation with pattern recognition.
- Vision-based image classification using Machine Learning Techniques.
- Vision-based Object detection using Machine Learning Techniques.
- Experimentation for Stereo vision.
- Robot-assisted image acquisition.
- Vision-based defect identification.
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