CRA341 Applied Image Processing Syllabus:

CRA341 Applied Image Processing Syllabus – Anna University Regulation 2021

COURSE OBJECTIVES:

1. To introduce various image processing and preprocessing techniques.
2. To learn about feature detection and matching using Image processing
3. To learn about segmentation using Image processing techniques.
4. To learn about computational photography.
5. To learn about image recognition using Image processing techniques.

UNIT – I IMAGE FORMATION AND PROCESSING

Introduction – Geometric primitives and Transformations – Photometric Image formation – The digital camera. Introduction to image processing – point – spatial – Fourier Transform – Pyramids and wavelets – Geometric transformations – global optimization

UNIT – II FEATURE DETECTION AND MATCHING

Introduction – Points and patches – Feature detectors – Feature Descriptors – SIFT – PCA SIFT – Gradient location orientation histogram

UNIT – III SEGMENTATION

Introduction – Active contours – Snakes – Scissors – Level sets – Split and merge – Watershed – Region splitting – region merging – and graph based segmentation – mean shift and mode finding – Normalized cuts – graph cuts and energy based methods – application

UNIT – IV COMPUTATIONAL PHOTOGRAPHY

Photometric calibration – Radiometric response function – Noise level estimation – Vignetting – Optical blur – High dynamic range imaging – Super resolution and blur removal – Color image demos icing – application

UNIT – V IMAGE RECOGNITION

Object detection – Face recognition – Instance recognition – category recognition – Bag of words – Part based models – context and scene understanding- Application: Image search.

TOTAL: 45 PERIODS

COURSE OUTCOMES

Upon successful completion of the course, students should be able to:
CO1: Understand various image processing and preprocessing techniques.
CO2: Design a feature detection algorithm for given application
CO3: Design a segmentation algorithm for given application.
CO4: Understand and recognize various computational photography techniques.
CO5: Design an image recognition for given application.

TEXT BOOKS:

1. Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2010.
2. Hartley R, Zisserman A, “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2004.

REFERENCES:

1. Forsyth D A, Ponce J, “Computer Vision: A Modern Approach”, 2nd Edition Bostan Pearson, 2015
2. Duda R O, Hart P E, Stork D G, “Pattern Classification”, Wiley, 2001.
3. Richard Sc “Computer Vision: Algorithms and Applications”, Springer, 2010.
4. Simon J.D.Prince “Computer Vision: Models, Learning and Inference”, Cambridge University Press, New York, 2014.