ET4007 Computer Vision Syllabus:
ET4007 Computer Vision Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
1. To introduce the fundamentals of Human and Computer Vision.
2. To introduce the major ideas, concepts, methods and techniques in Computer Vision.
3. To impart Computer Vision knowledge by way of learning related algorithms.
4. To make them familiar with both the Theoretical and Practical aspects of Computing with Images.
5. To provide the student with programming experience for implementing Computer Vision and algorithms.
UNIT I INTRODUCTION TO COMPUTER VISION
Digital Image Processing – Various Fields that use Image Processing – Fundamentals Steps in Digital Image Processing – Components of an Image Processing System. Applications of Computer Vision – Recent Research in Computer Vision. Introduction to Computer Vision and Basic Concepts of Image Formation: Introduction and Goals – Image Formation and Radiometry – Geometric Transformation – Geometric Camera Models – Image Reconstruction from a Series of Projections.
UNIT II IMAGE PROCESSING CONCEPTS AND IMAGE FEATURES
Image Processing Concepts: Fundamentals – Image Transforms – Image Filtering – Colour Image Processing – Mathematical Morphology – Image Segmentation. Image Descriptors and Features: Texture Descriptors – Colour Features – Edge Detection – Object Boundary and Shape Representation – Interest or Cornet Point Detectors – Histogram Oriented Gradients – Scale Invariant Feature Transform.
UNIT III IMAGE PROCESSING WITH OPENCV
Introduction to OpenCV and Python: Setting up OpenCV – Image Basics in OpenCV – Handling Files and Images – Constructing Basic Shapes in OpenCV. Image Processing in OpenCV: Image Processing Techniques – Constructing and Building Histograms – Thresholding Techniques.
UNIT IV OBJECT DETECTION
Models and types – Importance of Object Detection. The Working: Inputs and outputs – Basic Structure – Model Architecture Overview – Object Detection on the Edge. Use Cases and Applications: Video Surveillance – Self-driving Cars. Embedded Boards: Connecting Cameras to Embedded Boards – Simple algorithms for processing Images and Videos.
UNIT V APPLICATIONS AND CASE STUDIES
Applications: Machine Learning algorithms and their Applications in Medical Image Segmentation – Motion Estimation and Object Tracking – Face and Facial Expression Recognition – Image Fusion. Case Studies: Face Detection – Object Tracing – Eye Tracking – Handwriting Recognition with HoG.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
At the end of this course, the students will have the ability to
CO1: Understand the major concepts and techniques in computer vision and image processing
CO2: Infer known principles of human visual system
CO3: Demonstrate a thorough knowledge of Open CV
CO4: Develop real-life Computer Visions Applications.
CO5: Build design of a Computer Vision System for a specific problem.
REFERENCES:
1. “Digital Image Processing”, 4th Edition (Global Edition), Rafael C Gonzalez and Richard E Woods, Pearson Education Limited, 2018.
2. “Computer Vision and Image Processing – Fundamentals and Applications”, Manas Kamal Bhuyan, CRC Press, 2020.
3. “Mastering OpenCV 4 with Python”, Alberto FernándezVillán, Packt Publishing, 2019.
4. “Practical Python and Open CV: Case Studies”, 3rd Edition, Adrian Rosebrock, PyImageSearch, 2016.