Syllabus

UNIT I
Introduction : Image Processing, Computer Vision and Computer Graphics , What is Computer Vision - Low-level, Mid-level, High-level , Overview of Diverse Computer Vision Applications: Document Image, Analysis, Bio-metrics, Object Recognition, Tracking, Medical Image Analysis, Content-Based Image Retrieval, Video Data Processing, Multimedia, Virtual Reality and Augmented Reality.
UNIT II
Image Formation Models : Monocular imaging system , Orthographic & Perspective Projection, Camera model and Camera calibration, Binocular imaging systems, Multiple views geometry, Structure determination, shape from shading , Photometric Stereo, Depth from Defocus , Construction of 3D model from images.
UNIT III
Image Processing, Feature Extraction, and Motion Estimation : Image pre-processing, Image representations (continuous and discrete) , Edge detection, Regularization theory , Optical computation ,Stereo Vision , Motion estimation , Structure from motion.
UNIT IV
Shape Representation and Segmentation: Contour based representation, Region based representation, De-formable curves and surfaces , Snakes and active contours, Level set representations , Fourier, and wavelet descriptors , Medial representations , Multi-resolution analysis, Object recognition.
UNIT V
Image Understanding and Computer Vision Applications: Pattern recognition methods, Face detection, Face recognition, 3D shape models of faces Application: Surveillance – foreground-background separation –human gait analysis Application: In-vehicle vision system: locating roadway – road markings – identifying road signs – locating pedestrians.

Describe the foundation of image formation and image analysis. Understand the basics of 2D and 3D Computer Vision.

Become familiar with the major technical approaches involved in computer vision. Describe various methods used for registration, alignment, and matching in images.

Perform shape analysis and extract features form Images and do analysis of Images

Get an exposure to advanced concepts, including state of the art deep learning architectures, in all aspects of computer vision.

Upon completion of this course, students will be able to:
1.Know the theoretical and practical aspects of computing with images and the foundation of image formation, measurement, and analysis
2. Implement common methods for robust image matching and alignment
3. Understand the geometric relationships between 2D images and the 3D world
4. Gain exposure to object and scene recognition and categorization from images
5. Develop the practical skills necessary to build computer vision applications.

D. Forsyth and J. Ponce, Computer Vision - A modern approach, Prentice Hall

Richard Szeliski, Computer Vision: Algorithms and Applications (CVAA), Springer, 2010

E. R. Davies, , Computer & Machine Vision, Academic Press, 2012

Dana H. Ballard, Christopher M. Brown, Computer Vision, Prentice Hall 1st Edition (May 1, 1982) , ISBN-978-0131653160

http://conferences.visionbib.com/Iris-Conferences.html

https://sites.usc.edu/iris-cvlab/