Indian Institute of Information Technology, Allahabad

Computer Vision and Biometrics Lab (CVBL)

Visual Recognition

Odd Semester 2021 - 2022

Course Information

Objective of the course: The field of visual recognition has become part of our lives with applications in self-driving cars, satellite monitoring, surveillance, video analytics particularly in scene understanding, crowd behaviour analysis, action recognition etc. It has eased human lives by acquiring, processing, analyzing and understanding digital images and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information. The visual recognition encapsulates image classification, localization and detection. The course on visual recognition will help students understand new tools, techniques and methods which are influencing the visual recognition field.

Outcome of the course: At the end of this course, the students will be able apply the concepts to solve some real problems in recognition. The students will be able to use computational visual recognition for problems ranging from extracting features, classifying images, to detecting and outlining objects and activities in an image or video using machine learning and deep learning concepts. The student will be also being able to invent new methods in visual recognition for various applications.

Course Instructors

Dr. Satish Kumar Singh

Dr. Shiv Ram Dubey

Teaching Assistants

Nayaneesh Mishra

Nand Yadav

Albert Mundu

Suranjan Goswami

Pravin Kumar

Class meets

Monday: 04.00 - 06.00 pm, Friday: 10.00 - 12.00 pm and 04.00 - 06.00 pm; Remote

Schedule - Lectures

Schedule - Tutorials and Labs

Grading

• C1 (30%): 10% Written + 20% Practice
• C2 (30%): 10% Written + 20% Practice
• C3 (40%): 20% Written + 20% Practice

Prerequisites

• Computer Programming
• Data Structures and Algorithms
• Machine Learning
• Image and Video Processing
• Ability to deal with abstract mathematical concepts

Books

• Computer Vision: Algorithms and Applications, Richard Szeliski, Springer
• Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, MIT Press

Related Classes / Online Resources

• CS231n Convolutional Neural Networks for Visual Recognition, Stanford
• Deep Learning, Coursera
• Visual Recognition, IIT Kanpur
• Deep Learning, New York University
• Deep Learning, CMU

Disclaimer

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