Indian Institute of Information Technology, Allahabad
Computer Vision and Biometrics Lab (CVBL)
Jan-May 2022 Semester
Previous OfferingsVisual Recognition 2021
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.
- Class meets
- Monday: 09.00 - 11.00 am, Thursday: 05.00 - 07.00 pm, Friday: 07.00 - 09.00 pm
- Course Ethics
- Students are strictly advised to avoid the unethical practices in the course including review tests and practice components.
- The project component will be done in team. The team will be formed by the course instructors. The project allotment will be also done by the course instructors.
- You are not allowed to simply claim the existing solutions available in public domain as your own work in this course.
- If it happens that you have already done the similar projects in any other course or with any other faculty which is allotted to you, you should immediately inform us for the same as it is not allowed to have similar projects in this course which you might have already done previously.
- It is best to try to solve problems on your own, since problem solving is an important component of the course.
- You are allowed to discuss class material, problems, and general solution strategies with your classmates. But, when it comes to formulating or writing solutions you must work/implement by yourself.
- You may use free and publicly available sources, such as books, journal and conference publications, and web pages, as research material for your answers. (You will not lose marks for using external sources.) It is does not mean that you claim these existing resources as your work.
- You may not use any paid service and you must clearly and explicitly cite all outside sources and materials that you made use of.
- We consider the use of uncited external sources as portraying someone else's work as your own, and as such it is a violation of the Institute's policies on academic dishonesty.
- Instances will be dealt with harshly and typically result in a failing course grade.
- C1 (30%): 10% Written + 20% Practice
- C2 (30%): 10% Written + 20% Practice
- C3 (40%): 20% Written + 20% Practice
- Computer Programming
- Data Structures and Algorithms
- Machine Learning
- Image and Video Processing
- Ability to deal with abstract mathematical concepts
- Computer Vision: Algorithms and Applications, Richard Szeliski, Springer
- Deep Learning, Ian Goodfellow, Aaron Courville, and Yoshua Bengio, MIT Press
Related Classes / Online Resources
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