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Summary: Any AI system that processes visual information relies on computer vision. And when an AI identifies specific objects and categorizes images based on their content, it is performing image recognition which is a crucial part of Computer Vision. And developing projects on them is a great way to understand the concepts from the core. Building projects can be very challenging on computer vision, but it is not very difficult due to the recent technological advancements and resources available.
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3. 3 Computer Vision Projects (Combo Course)
4. Computer Vision - Text Scanner
5. Computer Vision Based Mouse
I am sure you are familiar with Artificial Intelligence and the wonders it is creating. AI is the present and the future of technology and computer science.
While some might say that AI is at least a few years away from causing any significant impacts on our lives, the fact remains that it is already affecting our decision making and lifestyle on a daily basis.
From multi-billion dollar companies to our own households, it is everywhere.
Whether you’re using your smartphones doing some harmless social media surfing, or buying online products, listening to music or using navigation, AI is impacting our lives in enormous ways without us even realising it.
AI and Computer Vision go hand in hand.
Have you ever seen a bar code reader scan a bunch of stripes in a mall? Or have you ever wondered how does your phone recognise your face and unlocks itself?
It is all because of Computer Vision and its applications.
Being an engineering student you might be wondering what makes AI and more specifically Computer Vision so powerful and popular in the current market. Or you might be confused about what kind of opportunities that this technology offers to engineering students.
Well, you are not alone.
A lot of students have this query and this article will explain the importance of Computer Vision and how an engineering student can improve skills in order to develop an exciting career.
The main topic of this article is projects based on Computer Vision, so let’s just get into it.
But first,
Computer vision is in simple words, a field of artificial intelligence that works on enabling computers to see, identify and process images in the same way that human vision does, and then provide the appropriate output. It includes methods for acquiring, analysing, processing and understanding images. This concept is currently used in many industries by which many of their processes are improved significantly. Computer vision is also described as the enterprise of automating and integrating a wide range of processes and by the representations for vision perception.
Computer Vision is the same as imparting human intelligence and instincts into a computer.
Sounds simple, right?
Want to develop practical skills on Computer Vision? Checkout our latest projects and start learning for free
Well, it isn’t. It is a difficult task to enable computers to recognize images of different objects.
Whenever a machine processes raw visual input – such as a JPEG file or a camera feed – it’s using computer vision to understand what it’s seeing. Think of computer vision as the part of the human brain that processes the information received by the eyes – not the eyes themselves.
Computer vision is closely linked with artificial intelligence, as the computer must interpret what it sees, and then perform appropriate analysis or act accordingly.
Any AI system that processes visual information relies on computer vision. And when an AI identifies specific objects and categorizes images based on their content, it is performing image recognition which is a crucial part of Computer Vision.
As a scientific discipline, computer vision is associated with the theory behind AI systems that extract information from images. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.
Sub-domains of computer vision include scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, and image restoration.
There are some areas of AI that deal with autonomous planning for robotic systems to navigate through the environment. A detailed understanding of the environments is necessary to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot.
Artificial intelligence and computer vision share topics such as pattern recognition and learning techniques.
Consequently, computer vision is sometimes seen as a part of the artificial intelligence field or the computer science field in general.
Real time on the context of Computer Vision can mean very different things, depending on what kind of sensor is being used and also on what you are trying to do. As you already know, Computer Vision is about extracting information from images and/or video, and the meaning of real time depends on the kind of information you are trying to extract.
For example, a typical video camera acquires images at 30 frames per second (fps). So the real-time can be being able to process images at the rate at which you acquire them.
On the other hand, if one has to detect and track objects in videos, then the real time is a function of how fast those objects move.
For example, 10fps is often sufficient for tracking people walking on the street, but probably not for tracking cars going 70 miles per hour on a highway.
The definition of Real-Time is usually dependent on the requirements of your system. Some systems might deem Real-Time as 10Hz, while it might be different for others. However, the rate at which your system needs to operate is often the upper limit of how fast Computer Vision needs to run. If your system needs to operate at 15Hz, you should expect your Computer Vision algorithms to be faster than 15Hz.
Computer vision research demonstrates the use of machine learning and computer vision for smart infrastructure management. This research’s contribution includes two case studies –
Each case study, includes controlled experiments with a verification data set. Both the studies yielded results that validated the approach of using machine learning and computer vision. Both case studies present a scenario where in machine learning is applied to a civil engineering challenge to create a more objective basis for decision-making. This work also includes a summary of the current state-of-the -practice of machine learning in Civil Engineering and the suggested steps to advance its application in civil engineering based on this research in order to use the technology more effectively.
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1. Background Check: For starters, as usual get the basics right with an undergraduate course in probability, statistics, linear algebra, calculus (both: differential and integral). A brief introduction to matrix calculus should come in handy. Also, my experience says that if one has some idea of digital signal processing then it should be helpful to grasp concepts easily.
On the implementation side, I prefer one to have a background in both R and Python. You can build projects that uses Python programming to develop some hands-on experience. Remember, the only way to truly master a skill is by practicing it. Check some of the projects that uses Python here
Do keep in mind that Computer Vision is all about computational programming.
2. Digital Image Processing: In computer science, digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analogue image processing. So it is highly recommended for people interested in AI to understand this concept as much as possible.
3. Computer Vision: Once done with Digital Image Processing the next step is to understand the mathematical models underlying the formulations of variety of applications of image and video content. You can learn about various computer vision algorithms to understand this. For starters, Image Thresholding and Canny Edge Detection algorithms are good ones to begin with.
4. Advanced Computer Vision: Following the first three steps will now make you get going for the advanced material. It seems to look interesting and will definitely give you a feel of how complex yet simple models are built for machine vision systems. Doing projects on advanced computer vision algorithms should also be a stepping stone to get going with academic papers.
5. Bring in Python and Open Source: There are many packages such as OpenCV, PIL, vlfeat and the likes. Now is the right time to use packages built by others into your projects. No need to implement everything from scratch.
You can find many good blogs and videos to get started with Programming Computer Vision with Python. I would recommend you to get a hold of these packages and implement on small projects to understand how to work with them for. Go and have fun! See how MATLAB and Python get you to implement computer vision algorithms.
6. Machine Learning and ConvNets: The core idea of teaching a computer to learn concepts using data—without being explicitly programmed. Practice some basic machine learning algorithms on your own to understand their functionality in computer vision projects.
1. Computer Vision Based Text Scanner:
If you are interested in giving eyes to machines/robots which can be clubbed with processors and actuators to make the machines perform actions based on their vision, this project is your gateway into it.
In this project you will make your computer "read" and identify texts! You will learn to develop a computer vision based text scanner that can scan any text from an image using the optical character recognition algorithm and display the text on your screen. You will also learn image processing algorithms, image thresholding, optical character recognition and many more amazing new skills.
Sounds exciting, right?
Build Computer Vision Based Text Scanner
2. Computer Vision Based Mouse:
If you want to build a career based on Computer Vision, then this project is apt for you. In this project you will build a Computer Vision based mouse to control the cursor using the object tracking algorithm. Using this project, you can carry out all the functionalities of a mouse by just showing corresponding colours in the webcam.
You can make your computer “see” and move your mouse accordingly!
You’ll also gain more knowledge on image processing algorithms, canny edge detection, object tracking and many more useful skills.
Build Computer Vision Based Mouse
3. Read Me My Book App:
These days no one likes carrying books everywhere. It is difficult and hectic. Here we come up with this solution, this read me my book application helps in converting hardcopy of books to pdf form.
You can build an application where the user can click picture using their mobile phone camera of the hardcopy pages and it will be converted into pdf form. You will learn optical character recognition and how it is used to convert hardcopy pictures into pdf file. The advantages of this app are, one can update books by just clicking hardcopies pictures and it also allows us to delete the pages which are not being used.
4. Facial Expression Recognition Project:
This project can detect a person’s facial expression and even display whether he/she is smiling, sad or shocked.
This software system is designed to first detect and read a person’s face. The system then computes a number of facial parameters of the person.
After detecting and registering these parameters, the system compares them with default expressions for sadness, smile and others. Based on the statistics then achieved the system concludes the person’s emotional state. Visual Studio and SQL Server are some of the technologies used.
Build Computer Vision Based Smart Selfie Application
5. Look Based Media Player:
Remember that irritating moment when someone calls while you’re watching a video on your PC? And you miss important parts while answering it. Well here is a solution to this problem.
A look based media player that pauses itself when user is not looking at it and resumes as soon as the user looks at it again.
Sounds amazing, right?
This is done using the camera or webcam on top of the computer. As long as the camera detects the users face looking at it, the media is played. The player pauses as soon as users face is not completely seen. This way you won’t miss any emotional drama in a movie J
6. Camera Motion Sensing Project:
This motion sensor project detects motion and sets off an alarm accordingly. This software system is designed in C that constantly monitors an environment using a camera. It even records images of the motion taking place as soon as it is detected.
The user first needs to set a security code. As soon as the user sets the code and activates the system, the monitoring starts.
The motion detector algorithm now constantly monitors the environment to check for any movement.
As soon any movement takes place in front of the camera the alarm is activated. The user can deactivate the alarm by entering the security code again.
Cursor Movement by Hand Gesture Project:
Have you ever wondered if you could control cursor through hand gestures? Well, this project puts forward a system that allows user to control the mouse movements through the use of hand movements.
The system uses webcam in order to detect hand gesture movements. It continuously scans the camera input for five finger hand like patterns. Once a hand is detected, the system then locks it as an object. After the object has been flagged and detected, the system then constantly records its movements in terms of X-Y direction movement based coordinates. These coordinates are then mapped real time onto the mouse cursor to move it according to hand movements.
Build Computer Vision Based Cursor Control Project
8. Theft Detection Device Project:
This device helps secure tour homes and offices from theft using Raspberry Pi. It uses image processing on live video to detect theft using motion. It also highlights the area where the theft occurs.
It allows the user to view the theft details and saving the video of the theft in a USB drive. In this system a camera along with a circuit, LCD display, IR for night vision and a USB drive for storage is used. As soon as camera detects motion, the system uses image processing to identify the exact area of motion occurrence and then highlights it accordingly. The system now transmits the images of the occurrence over internet to be viewed by the user online.
Hope you got some good computer vision project ideas from this article.
Suppose, if you want to build great computer vision projects but don’t have the necessary technology knowledge, don’t worry!
We at Skyfi Labs have developed an innovative learning methodology through which you can learn latest technologies by building projects hands-on right from your home. With the online course content available 24x7 and 1-1 technical assistance provided, developing great expertise on latest technologies like computer vision will never be tough for you.
Check computer vision projects that you can develop at homeYou can contact us at 1800-200-3855 if you need to talk to our expert team.
Comment your queries below, we will assist you at the earliest.
Good luck!
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