Computer Vision [2021]

EC60002: Computer Vision

Course Syllabus:
* mentioned exhaustively, actual coverage is expected to be less than that given below. [Medium of communication: English]

This is an advanced PG and PhD level course with state-of-the-art syllabus, which assumes that a registrant is well-versed with basic concepts of linear algebra, probability and random process, optimization, and signal processing.

Preferred Course Prerequisites

  • EC61409: Neural Networks and Applications [offered in the autumn semesters]

If you have not taken the above course, it is advised that you go through introductory video lectures at least on the following Deep Learning (DL) topics before the part (2) of this course starts:
Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN).
For example, you may consider the relevant lectures of the CS231n course offered by Stanford University (do a web search!) or those from the Deep Learning course by Prof. P. K. Biswas of IIT Kharagpur's E&ECE dept provided by NPTEL in Youtube. The relevant video lectures from EC61409 will be also be shared with those who register for this course.

The EC60002 course is divided into 4 unequal parts:

(1) Color -
Color Photography, Color Representation, Color Matching and Reproduction, Color Coordinate Systems, Color Differences, Color Vision, Color Filter Array, Demosaicing & Deinterlacing, Color Balancing and Gamma, Color Constancy and Retinex

(2) Features -
Local Descriptors: Corner, SIFT, LBP, Steerable Filters
Edge Detection and Linking: LoG, Canny
Image Features: Shape & Texture, Pyramids & Wavelet, Random Fields, DL based Perceptual Features
Motion: Optical Flow, Block Matching, Parametric Motion, Global Motion, Flownet
Depth: Depth from Focus & Defocus, Depth from Structure, DL based Depth, Structure & Depth from Egomotion
Full-reference Quality: SSIM, FSIM, MOVIE index

(3) Processing -
Generic Filters: LMMSE Filter, Order-statistic Filter, Bilateral Filter, Nonlinear Means, Non-local Means
Video Filters: Spatio-temporal Filtering, Blur Reduction, Blotch Detection and Removal, Flicker Correction
Super-resolution (SR): Splines, Single Image SR, CNN SR, GAN SR, Temporal SR
Deconvolution: Unsharp masking, LMMSE & Bayes based Deconvolution, CNN based Deconvolution
Low-light Image Enhancement: Retinex based Contrast Enhancement, Illumination Enhancement, DL based Enhancement
Dehazing: Single Image Dehazing, Prior-based Image Dehazing, DL based Image and Video Dehazing
Deraining: Rain Removal from Images and Videos, DL based Rain Removal

(4) Decision-making -
Saliency Computation: Image and Video Saliency, DL based Image and Video Saliency
Retargeting and Inpainting: Seam Carving for Image and Video Retargeting, Image Inpainting, DL based Inpainting
Segmentation: Segmentation using Graph Cuts, Mean Shift and Mode Seeking Segmentation, CNN based Semantic Image and Video Segmentation
Object Detection and Recognition: Contrast based Salient Object Detection (SOD), DL based SOD, Video SOD, You Only Look Once (YOLO), Region based CNN (R-CNN) Variants, Face Detection and Recognition
Categorization and Captioning: Bag of words, EfficientNet on ImageNet, Image Captioning
Change and Shot Detection: Thresholding and Background Subtraction for Change Detection, DL based Change Detection, Shot Boundary Detection
Video Summarization: Dynamic Summary Generation, DL based Summary Generation


Books & More:

- Research Papers @ IEEE TIP, IEEE TPAMI, CVPR, etc.
- Computer Vision: Algorithms and Applications by Richard Szeliski
- Fundamentals of Digital Image Processing by Anil K. Jain
- Digital Video Processing by A. Murat Tekalp
- Digital Image Processing by Rafael C. GonzaLez and Richard E. Woods
- Image Processing for Cinema by Marcelo BertalmĂ­o
- The Essential Guide to Video Processing by Alan C. Bovik


Online Lecture Management:

- Google Classroom [invitation based, exclusive to those who officially register]