Preface:
The article is targeted towards beginnners. So, experts might not like the article that much. The article goes through the common topics of Computer Vision and Discusses about it’s link with Deep Learning.
Introduction:
Definition:
According To Academics:
It is field which deals with deals with interdisplinary way ‘How Computers Can Derive understanding from images and videos?’
According To Enginners:
It is field which deals ‘How Computer can seek understanding and automate task that the human visual system can do?’
You guys probably figured out that both definitions are clearly different. Cause Academics and Engineers have to deal with problems differently.
I am writing the article as an Academic. So, that’s my bias. I am sorry for that.
Computer Vision includes methods for acquring, processing, analyzing and understanding data from images and extraction of high dimentional data from real world in order to produce numerical and sysmbolical data.Undestanding the context is that; it harvets information from materials like images and videos as input then strips there information and uses it as a process of decision making or elicit.
The scientific discipline of Computer vision is similar to the theory of AI. Where AI extracts information from images.
The data can take many forms such as video processing, 3D image, video sequences and more.
There are some sub-domains such as scene recondtruction, video tracking and object recognizion and more.
History:
I really hate history by the way. I always use get bad grades in history. But still knowing a bit of history can help us in CS.
The only history we need in this article is that;
Computer Vision was never considered a serious subject, It started in late 1960s as a sub pojects where AI was seeing light. Meaning in good Universities! It was created to mimic human visual system!
In 1966, it was believed that it can be acheived as a summer project by tapping a camera to a computer!But when it as able to extract 3D data from images it was started to view as an important subject. So, summer project became field. in 1970, the field saw many good research and many algorithms were written for different parts of Computer Vision. They are still the foundations.
Later history is not useful for us cause it deals with modern blah! Blah! Statements.
Related Fields of Computer Vision:
There are many Fields Which are related To Computer Vision and they all serve purposes for Robotics or similar:
1.Artificial Intelligence
2.Information Engineering
3.Solid State Physics
4.Neurobiology
5.Signal Processing
6.Statistics
7.Optimization
8.Geometry
9.Fashion Commerce
10.Inventory Management
11.Patent Search
12.Furniture
13.The Beauty Industry
Computer Vision In 21st Century:
Computer Vision came a long way of history. It was first thought as a summer project. Then it became an average subject. Then it became a subject which is well known now as a respected field with many potentials.
Computer Vision nowadays is very widespread. It is from our Computer to our Smartphone. As image processing. Google Lens and Augumented Reality.
I always get the lesson that we should never underestimate a subject or field or person. Anyone can do something which can be extraordinary.
Computer Vision nowadays in my opinion became a philosophy. Cause the sub field are more lucrative. Then AI and Deep Learning does most of the work of Computer Vision and It’s Sub-parts. So, the curtain gets involved in Computer Vision and It’s parts. We just see it as Deep Learning or AI.
Anyways the subject produced many algorithms and a lover of math and algorithms I really I love those algorithms.
That’s the reason I am writing,
Applications:
There are many real life impications of Computer Vision. Most of them are really well-known to all of us and useful and we use them all the time.
These are as below:
- Identification
- Controlling
- Detecting
- Visual Survillence
- Interaction
- Autonomus Vehicle
- Indexting Databases
- Medicine
- Machine Vision
- Military
- Tactile Feedback
Typical Tasks:
- Recognition
- Motion Analysis
- Scene Reconstruction
- Image Restoration
System Methods:
- Image acqutation
- Pre-Processing
- Feature Extraction
- Detection/Segmentation
- High-level Processing
- Decision Making
- Image Understanding System
Hardware:
- Structured light 3D scanners
- Thermographic cameras
- hyperspectural imagers
- radar imaging
- lidar scanners
- magnetic resonance imaging
- side scan sonar
- syntheic
- aperture sonar
If I get good views and upvotes in these article I will make a expert version of my Computer Vision Article.