EC60002: Computer Vision
Course Syllabus:
[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.
Course Prerequisites
Earlier /concurrent registration in EC60334 (Deep Learning) or in equivalent courses
If you have not taken or not currently taking the above course/s, it is advised that you go through introductory video lectures at least on the following Deep Learning (DL) topics during this course:
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 EC60002 course is divided into 4 unequal parts:
(1) Color -
Color Photography, Color Representation & Color Vision, Color Matching and Reproduction, Color Coordinate Systems, Color Filter Array, Demosaicing & Deinterlacing, Color Balancing and Gamma, Color Constancy and Retinex
(2) Features -
Edge Detection and Linking: LoG, Canny
Local Descriptors: Corner, SIFT, HOG, LBP, Gabor Filter
DL based Perceptual Features
Motion: Optical Flow, Block Matching, Parametric Motion, Flownet
Full-reference Quality: SSIM
(3) Processing -
Generic Filters: LMMSE Filter, Order-statistic Filter, Bilateral Filter, Nonlinear Means, Non-local Means
Video Filters: Spatio-temporal Filtering, Blur Reduction
Deconvolution: Unsharp masking, Deconvolution, CNN based Deconvolution
Super-resolution (SR): Splines, Single Image SR, CNN SR, GAN SR
Low-light Image Enhancement: Retinex based Contrast Enhancement, Illumination Enhancement, DL based Enhancement
Dehazing: Single Image Dehazing, Prior-based Image Dehazing, DL based Image Dehazing
(4) Decision-making -
Saliency Computation: Image Saliency, DL based Saliency
Segmentation: Superpixels, Mean Shift and Mode Seeking Segmentation, CNN based Semantic Segmentation
Object Detection and Recognition: Contrast based Salient Object Detection (SOD), DL based SOD, You Only Look Once (YOLO), Region based CNN (R-CNN) Variants
Vision Language Model.
Books & More:
- Research Papers @ Top Journals and Conferences
- 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 BertalmÃo
- The Essential Guide to Video Processing by Alan C. Bovik
Class Timings & Venue:
Time Slot: Thursdays: 03.00pm - 05.00pm & Fridays: 02.30pm - 04.30pm
Venue: NR121 or F300, Department of E&ECE, IIT Kharagpur
Online Lecture Management:
- Google Classroom [invitation based, exclusive to those who officially register]