MSc: Computer Vision - Revision history

R.sirgalina at 08:43, 29 August 2022

2022-08-29T08:43:40Z

M.petrishchev at 07:17, 25 April 2022

2022-04-25T07:17:56Z

← Older revision		Revision as of 07:17, 25 April 2022
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	This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.		This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

−	=== Course ~~objectives~~ ~~based~~ on Bloom’s ~~taxonomy~~ ===	+	== Course Objectives Based on Bloom’s Taxonomy ==

−	=== - What should a student remember at the end of the course? ===	+	=== What should a student remember at the end of the course? ===

	By the end of the course, the students should be able to process the video		By the end of the course, the students should be able to process the video
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	* To develop research interest in the theory and application of computer vision		* To develop research interest in the theory and application of computer vision

−	=== - What should a student be able to understand at the end of the course? ===	+	=== What should a student be able to understand at the end of the course? ===

	By the end of the course, the students should be able to choose the correct computer vision model.		By the end of the course, the students should be able to choose the correct computer vision model.
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	* Ability to choose the right model for the given task		* Ability to choose the right model for the given task

−	=== - What should a student be able to apply at the end of the course? ===	+	=== What should a student be able to apply at the end of the course? ===

	By the end of the course, the students should be able to deploy and developed models.		By the end of the course, the students should be able to deploy and developed models.

R.akhmetzyanov: /* Computer Vision */

2022-04-21T07:59:28Z

Computer Vision

← Older revision		Revision as of 07:59, 21 April 2022
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	* <span>'''Course name:'''</span> Computer Vision		* <span>'''Course name:'''</span> Computer Vision
	* <span>'''Course number:'''</span> R-03		* <span>'''Course number:'''</span> R-03
		+	== Prerequisites ==

	== Course Characteristics ==		== Course Characteristics ==

M.petrishchev: M.petrishchev moved page MSc:Computer Vision to MSc: Computer Vision

2022-04-15T08:07:07Z

M.petrishchev moved page MSc:Computer Vision to MSc: Computer Vision

← Older revision	Revision as of 08:07, 15 April 2022
(No difference)

M.petrishchev: M.petrishchev moved page MSc:ComputerVision.S22 to MSc:Computer Vision

2022-04-10T12:31:21Z

M.petrishchev moved page MSc:ComputerVision.S22 to MSc:Computer Vision

← Older revision	Revision as of 12:31, 10 April 2022
(No difference)

I.konyukhov: I.konyukhov moved page MSc:ComputerVision to MSc:ComputerVision.S22

2021-11-14T17:20:05Z

I.konyukhov moved page MSc:ComputerVision to MSc:ComputerVision.S22

← Older revision	Revision as of 17:20, 14 November 2021
(No difference)

10.90.136.11 at 11:24, 30 July 2021

2021-07-30T11:24:26Z

Show changes

10.90.136.11: Created page with "= Computer Vision = * '''Course name:''' Computer Vision * '''Course number:''' R-03 * '''Area of instruction:''' Computer Science and..."

2021-07-30T11:23:16Z

Created page with "= Computer Vision = * '''Course name:''' Computer Vision * '''Course number:''' R-03 * '''Area of instruction:''' Computer Science and..."

New page

= Computer Vision =

* '''Course name:''' Computer Vision
* '''Course number:''' R-03
* '''Area of instruction:''' Computer Science and Engineering

== Administrative details ==

* '''Faculty:''' Computer Science and Engineering
* '''Year of instruction:''' 1st year of MSc
* '''Semester of instruction:''' 2nd semester
* '''No. of Credits:''' 5 ECTS
* '''Total workload on average:''' 180 hours overall
* '''Frontal lecture hours:''' 2 hours per week.
* '''Frontal tutorial hours:''' 0 hours per week.
* '''Lab hours:''' 2 hours per week.
* '''Individual lab hours:''' 2 hours per week.
* '''Frequency:''' weekly throughout the semester.
* '''Grading mode:''' letters: A, B, C, D.

== Course outline ==

This course provides an intensive treatment of a cross-section of the key elements of computer vision, with an emphasis on implementing them in modern programming environments, and using them to solve real-world problems. The course will begin with the fundamentals of image processing and image filtering, but will quickly build to cover more advanced topics, including image segmentation, object detection and recognition, face detection, content-based image retrieval, artificial neural networks, convolutional neural networks, generative adversarial networks, image creation using GANs, and much more. A key focus of the course is on providing students with not only theory but also hands-on practice of building their computer vision applications.

== Expected learning outcomes ==

* Apply knowledge of image acquisition, image processing, and image analysis to extract useful information from visual images.
* Design, implement, and document appropriate, effective, and efficient software solutions for a variety of real-world computer vision problems.
* Exploit standard computer vision software libraries in the development of these solutions.

== Required background knowledge ==

Solid knowledge of essential topics in the courses that are mentioned below.

== Prerequisite courses ==

Linear Algebra, Calculus, Probability & Statistics, Computer Programming, Basic Machine Learning.

== Detailed topics covered in the course ==

* Introduction to Computer Vision, Image Acquisition, Basic Image Processing
* Kernels, Morphological operations, Smoothing and Blurring, Lightening and Color spaces
* Gradients, Edge Detection, Contours, Histograms, Labelling Connected Components
* Object Detection, Template Matching, Image Pyramids, Object Detection using HOG and SVM
* Image Classification, Common Machine Learning Algorithms for Image Classification
* Clustering, Bag of Visual Words, Image Pyramids, Image Classification Examples
* Face Detection
* Image Descriptors: Color channel statistics, Moments, Texture, HoGs
* Local features, Key point detectors, Local invariant descriptors, Binary Descriptors
* Video Processing: Moving object detection, background models
* Artificial Neural Nets and Convolutional Neural Nets
* Generative Adversarial Networks
* Case Studies: Research paper implementation and presentation

== Textbook ==

There is no specific text book for this course.

== Reference material ==

*
*
*
*

== Required computer resources ==

Students should have laptops. A Mac or Window’s PC capable of running a scientific python development environment.

== Evaluation ==

* Quizzes (40%)
* Mid-term exam (15%)
* Case Study (15%)
* Final exam (30%)