Computer Vision Applications

Computer visioning

What do you mean by computer vision?

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. 

What is computer vision and how it works?

Computer vision is the field of computer science that focuses on creating digital systems that can process, analyze, and make sense of visual data (images or videos) in the same way that humans do. The concept of computer vision is based on teaching computers to process an image at a pixel level and understand it.

What is the purpose of computer vision?

Computer vision is a subfield of artificial intelligence. The purpose of computer vision is to program a computer to “understand” a scene or features in an image. Typical goals of computer vision include: The detection, segmentation, localisation, and recognition of certain objects in images (e.g., human faces)

History of computer vision

Early experiments in computer vision took place in the 1950s, using some of the first neural networks to detect the edges of an object and to sort simple objects into categories like circles and squares. In the 1970s, the first commercial use of computer vision interpreted typed or handwritten text using optical character recognition. This advancement was used to interpret written text for the blind. 

As the internet matured in the 1990s, making large sets of images available online for analysis, facial recognition programs flourished. These growing data sets helped make it possible for machines to identify specific people in photos and videos.

Today, a number of factors have converged to bring about a renaissance in computer vision:

Mobile technology with built-in cameras has saturated the world with photos and videos.

Computing power has become more affordable and easily accessible.

Hardware designed for computer vision and analysis is more widely available.

New algorithms like convolutional neural networks can take advantage of the hardware and software capabilities.

The effects of these advances on the computer vision field have been astounding. Accuracy rates for object identification and classification have gone from 50 percent to 99 percent in less than a decade — and today’s systems are more accurate than humans at quickly detecting and reacting to visual inputs.

Computer vision in today’s world

From recognizing faces to processing the live action of a football game, computer vision rivals and surpasses human visual abilities in many areas.

Deep learning and computer vision

How does deep learning train a computer to see? Learn how the different types of neural networks work and how they are used for computer vision.

Image Analysis and AI

See and introduction to image analysis and learn analytical techniques you can apply to image data. 

Who’s using computer vision?

Computer vision is used across industries to enhance the consumer experience, reduce costs and increase security.

Learn More About Industries Using This Technology

Computer vision is one of the most remarkable things to come out of the deep learning and artificial intelligence world. The advancements that deep learning has contributed to the computer vision field have really set this field apart.

Wayne Thompson SAS Data Scientist

What are the various applications of Computer Vision?

The concept of computer vision was first introduced in the 1970s. All these new applications of computer vision excited everyone. Having said that, the computer vision technology advanced enough to make these applications available to everyone at ease today.

However, in recent years the world witnessed a significant leap in technology that has put computer vision on the priority list of many industries.

As mentioned earlier, computer vision plays a crucial role in the advancement of technologies, especially AI. The applications of computer vision, in particular, are very versatile and never-ending.

From something as basic as convenient stores to healthcare, computer vision is being implemented everywhere.

  • Retail stores: The newest and most exciting application of computer vision can be seen in the new store launched by Amazon Company called ‘Amazon Go’. In this innovative retail store, there are no cashiers or checkout stations!

That’s right, no more waiting in line to pay the bills. It’s a partially automated store and by utilizing computer vision, deep learning, and sensor fusion customers are able to simply exit the store with products of their choice and get charged for their purchases through their Amazon account. Amazing, right?

  • Automotive: Computer vision is also taking the automotive industry by storm. And we can clearly see why. Companies like Waymo and Tesla have developed self-driving cars that are going to literally rule the streets in the coming years. According to the World Health Organization, more than 1.25 million people die each year as a result of traffic incidents and these cars promise to make our driving safer.

They are equipped with sensors and software that can detect 360 degrees of movements of pedestrians, cyclists, vehicles, road work, and other objects. They are able to follow the traffic flow and regulations and can detect obstacles in its way.

  • Healthcare: While we all know that computers can never replace humans in doing what they do, especially in healthcare. But technology is helping healthcare professionals accurately classify conditions and illnesses by reducing and eliminating inaccurate diagnoses and thereby saving patients’ lives.

One of the most recent and exciting medical procedures involving computer vision is a real-time blood monitor by the Gauss Surgical company, that solves the problem of inaccurate blood loss measurement during injuries and surgeries. It maximizes transfusions and recognizes hemorrhage better than the human eye. 

How does computer vision differ from image processing?

Both computer vision and image processing are based on the input of an image or signal and then processing the signal to give us the altered output.  As their names already imply their goals and methodologies, the boundaries of these two fields may seem clear. However, they draw heavily from the methodologies of one another, which can make the boundaries between them blurry and people confused between the two. 

To put out simply, image processing is a subset of computer vision. A computer vision system uses the image processing algorithms to try and perform its functions.

As the name suggests, in image processing an image is processed. The image is bettered. It may have something to do with smoothing, sharpening, contrasting, stretching etc. It makes the image more enhancive & readable. Also, input and output are both images.

The ultimate goal of computer vision is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. Computer vision is not limited to pixel-wise operations, it can be a lot more complex than image processing. The input can be both images or videos, and the output is not necessarily an image but can also be quantitative or qualitative information, like size, color, shape, classification, etc.

Therefore, if the goal is to enhance the image for later use, then this may be called image processing. And if the goal is to recognize objects and provide useful information on it then it can be called computer vision. 

Simple enough?

Does computer vision technology involve machine learning?

As you already know, there is a lot of overlap between computer vision and image processing. Machine learning, on the other hand, is flexible as it can be used in both computer vision and image processing. Machine learning is an application of artificial intelligence that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. The goal of machine learning is to optimize differentiable parameters so that a certain loss/cost function is minimized.

Machine learning can be used in both image processing and computer vision but it is found to be more useful in computer vision than in image processing.

Computer Vision has the power of complex image processing techniques to extract meaningful features from a given image or video samples whereas machine learning deals with pattern recognition and computational learning using sophisticated data-prediction algorithms, artificial neural networks, etc. 

How to develop skills in Computer Vision?

Before starting a career in your desired field, you need to develop proper skills to increase the chances of getting employed. And the earlier you start, the better.

  1. Get your basics right: For starters, as usual, get the basics right by developing your knowledge in probability, statistics, linear algebra, calculus (both: differential and integral). A brief introduction to matrix calculus should also come in handy.

It would be really helpful to have a background on both R and Python language. You can build projects that use Python programming to develop some hands-on experience. Click here to check out some projects based on Python.

Remember, the only way to truly master a skill is by practicing it.

  1. Digital Image Processing: As you’ve already seen, it is immensely crucial to have knowledge of image processing. It is sometimes considered as a part of computer vision and, therefore, it is crucial to have your concepts clear on it.
  2. Computer Vision: Once done with Digital Image Processing the next step is to understand the mathematical models underlying the formulations of a 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.
  3. 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 these packages built by others into your projects. No need to implement everything from scratch.
  4. Machine Learning and ConvNets: The core idea is to teach 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.

Remember, the most important skill you need to develop is practical skills. Build projects, learn more. Keep yourself updated on the latest trends and innovations regarding computer vision.

And, stay curious!

Let’s look at some projects idea you can try immediately from your home and improve your skills.

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 like image thresholding, optical character recognition, etc.

Sounds exciting, right?

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 algorithms. Using this project, you can carry out all the functionalities of a mouse by just showing corresponding colors in the webcam.

You can make your computer “see” and move your mouse accordingly!

You’ll also gain more knowledge of image processing algorithms, Canny edge detection, object tracking, etc. by doing this project.

3. Facial Expression Recognition Project:

Imagine a project where a machine can detect a person’s facial expression and even display whether he/she is smiling, sad or shocked. Sounds fun, right?

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 these parameters, the system compares them with default expressions for sadness, smile, and others. Based on the statistics, the system concludes the person’s emotional state. Visual Studio and SQL Server are some of the technologies that are used and you can gain knowledge on them.

4. Cursor Movement by Hand Gesture Project:

Imagine if you could control cursor through hand gestures. It sounds really intriguing, doesn’t it?

Well, this project puts forward a system that allows the user to control the mouse movements through the use of hand movements only. No need to use a mouse anymore. The implementation of this project will be really useful in large scale industries and offices

The system uses a 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. 

5. Theft Detection Device Project:

Are you paranoid about your safety? If you are, there is no surprise. Everyday theft is increasing and having a machine that can detect thieves will undoubtedly come in handy.

This device helps to secure your homes and offices from theft. It uses image processing on live video to detect theft using motion. It also highlights the area where 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 the 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 the internet to be viewed by the user online.

You can gain so much knowledge on image processing algorithms its and Raspberry Pi by doing this project.

6. Camera Motion Sensing Project:

This motion sensor project detects motion in a particular environment and sets off an alarm accordingly. It is like having your own CCTV camera. The only difference is you would be the one who built it.

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.

You first need 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 as 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.

7.Look Based Media Player:

Remember those moments when you’re completely immersed in a movie you’re playing on the laptop and then you get an urgent call? And you miss important parts while answering it and you have to rewind again to the part where you exactly left it off. Sounds irritating, right? Well, it is. However, here is a solution to this problem.

A look based media player that pauses itself when the user is not looking at it and resumes as soon as the user looks at it again.

This is done using the camera or webcam on top of the computer. As long as the camera detects the user’s face looking at it, the media will be played. The player pauses as soon as the user’s face is not completely seen. This way you won’t miss any emotional drama from your favorite movie.

8. Read Me My Book App: 

No one likes carrying huge books. It is difficult and hectic. And in the world of digitalization, this project will make your books digitalized as well. Read me my book application helps in converting hardcopy of books into pdf form. 

In this project, you can build an application where you can click a picture using your 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 a pdf file by doing this project.

The other 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.

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 technical knowledge, don’t worry!

We at Skyfi Labs have developed an innovative learning methodology through which you can learn the latest technologies by building projects hands-on right from your home. With the online course content available 24×7 and 1-1 technical assistance provided, developing great expertise on the latest technologies like computer vision will never be tough for you.

Computer vision: the advantages

Let’s now move on to understanding how computer vision systems benefit business users. Having the ability to see and interpret, computer vision systems automate several tasks without needing human intervention. As a result, business users can enjoy benefits like: 

Faster and simpler process – Computer vision systems can carry out monotonous, repetitive tasks at a faster rate, making the entire process simpler.Accurate outcome – It’s no secret that machines never make any mistake. Likewise, computer vision systems with image-processing capabilities will commit zero mistakes, unlike humans. Ultimately, products or services provided will not only be quick but also of high quality.Cost-reduction – With machines taking up responsibilities of performing cumbersome tasks, errors will be minimized, leaving no room for faulty products or services. As a result, companies can save a lot of money that would be otherwise spent on fixing flawed processes and products.

Computer vision: the limitations

No technology is free from flaws. And the same applies to computer vision systems. Let’s now look at a few limitations that the technology inherently has:Lack of specialists – Computer vision technology involves the use of AI and ML. To train a computer vision system powered by AI and ML, companies need to have a team of professionals with technical expertise. Without them, building a system that can analyze and process the possible surrounding details is not possible.Need for regular monitoring – What if a computer vision system breaks down or has a technical glitch? To ensure that doesn’t happen, companies have to get a dedicated team onboard for regular monitoring and evaluation.Despite their current limitations, computer vision systems can bring companies immense opportunities to increase revenue streams, meet productivity goals, and streamline work processes. However, we have barely just scratched the surface of computer vision capabilities. The future is yet to be seen.

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