Short introduction to computer vision

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Из курса от партнера National Research University Higher School of Economics

Deep Learning in Computer Vision

124 оценки

National Research University Higher School of Economics

Deep Learning in Computer Vision

124 оценки

Курс 5 из 7 — Specialization Advanced Machine Learning

Deep learning added a huge boost to the already rapidly developing field of computer vision. With deep learning, a lot of new applications of computer vision techniques have been introduced and are now becoming parts of our everyday lives. These include face recognition and indexing, photo stylization or machine vision in self-driving cars. The goal of this course is to introduce students to computer vision, starting from basics and then turning to more modern deep learning models. We will cover both image and video recognition, including image classification and annotation, object recognition and image search, various object detection techniques, motion estimation, object tracking in video, human action recognition, and finally image stylization, editing and new image generation. In course project, students will learn how to build face recognition and manipulation system to understand the internal mechanics of this technology, probably the most renown and oftenly demonstrated in movies and TV-shows example of computer vision and AI.

Из урока

Introduction to image processing and computer vision

Welcome to the "Deep Learning for Computer Vision“ course! In the first introductory week, you'll learn about the purpose of computer vision, digital images, and operations that can be applied to them, like brightness and contrast correction, convolution and linear filtering. These simple image processing methods solve as building blocks for all the deep learning employed in the field of computer vision. Let’s get started!

Short introduction to computer vision4:40

Digital images3:35

Structure of human eye and vision6:15

Color models15:57

Познакомьтесь с преподавателями

Anton Konushin
Senior Lecturer
HSE Faculty of Computer Science
Alexey Artemov
Senior Lecturer
HSE Faculty of Computer Science

[SOUND] Hello, first I want to make a brief

introduction to the field of computer vision.

What is computer vision?

To answer this question, we need to understand first what human vision is.

There is a saying, to see is to know what is where by looking.

The goal of computer vision is to solve the same problem on a computer.

Thus, computer vision is regarded as a part of artificial intelligence.

In computer vision we have our own version of Turing test.

To pass it, the computer vision system should answer any question regarding

an image, which can be answered by a human.

What are the main questions about an image?

The first question comes from the informal definition of vision.

This, what and where?

What objects are seen in the image and where are there are located?

This is called object detection.

Some objects are more important for us than other.

Because we're humans, the most important object for us are other humans and

their faces.

Thus, it is understandable why face detection and person detection

are one of the most rigorously researched topics in computer vision.

And still they are not solved completely, with new algorithms appearing every week.

In this particular image we can see other important objects like text, or

less important, buildings and flags.

After objects are detected, we move on to know their properties.

Is it the picture of a city, or it is something else?

What color is the sky, how strong is the wind?

What can we say about this person?

Currently these problems are usually solved by image classification.

Possible answers are enumerated and an algorithm predict the correct answer.

For example, we can divide all images into images of cities and all the rest, and

then it classify as city or not city.

To identify the particular object and person, we usually store the images and

compare the current image with the stored, which is called image retrieval.

We can also obtain metric information from images,

like distances between selected points in scene.

We can also estimate the shape of the object,

position of camera relative to the object, distance from the camera to the object.

Currently available algorithm of metric vision allows automated creation of fully

textured 3D models from set of photographs downloaded automatically from

the Internet.

The quality of these models are not of production quality yet, and you can't

directly put them into computer game, but we will get there soon, I believe.

In our human brains we combine our observations with prior knowledge

regarding the volt.

It is estimated that a quarter of human brain is devoted to the vision.

A lot of computer vision algorithm have derived ideas

from our knowledge of human vision system.

Probably the most influential work computer vision is the model of human

vision system, which was proposed by David Marr in 70s.

It consists of three steps.

First, low-level image features, like directed edge segments,

are extracted from image to form so-called primal sketch.

From this primal sketch a two 2.5D sketch is obtained using binocular stereo.

Finally, high-level techniques using prior knowledge

is used to get 3D model representation of objects in the scene.

This seminal work has received so much appraised that the most prestigious award

in computer vision is named after the Marr, the Marr Prize.

This is awarded each couple of years during ICCV,

International Conference on Computer Vision.

The progress in computer vision is entangled with progress

in machine learning.

As an example, I want to mention the seminal work,

the Viola-Jones face detector, which was proposed in 2001.

It was the first detector which can elaborately detect human faces in real

time on a personal computer.

This was a demonstration of power of machine learning and data collection.

Until recently, if you saw a face detector in a consumer product,

it was definitely a Viola-Jones detector or one of its derivatives.

Neural networks has a long history in computer vision.

But since 2012, when AlexNet was introduced,

we are witnessing a record improvement of computer vision algorithms.

Deep learning has been successfully applied to most of the computer

vision problems.

In this course we will focus on deep learning methods in computer vision.

But we will also introduce a lot of general computer vision concepts,

explain both basic and seminal non-deep learning methods,

talk about contemporary data sets.

All to give you perspective and

understanding of key ideas, problems,

and achievements of computer vision.

[SOUND]

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