Image Processing and Computer Vision

28/09/2021 20mins
Venkat Ramakrishnan


Apparently, seeing seems quite easy to all, or you can say it's the easiest task. However, in the background, it's much harder than what it looks like. Having said that, human vision is a nature's complex technology. Not only it involves eyes, and visual cortex, but also includes mental representation of objects, our conceptual thinking and a journey of interactions which we experienced in our lives.

Undeniably, today many digital devices are extremely good at capturing images in such fine detail and resolutions that exceeds the organic human vision system. Digital equipment is able to interpret and measure even the slightest variation in colors precisely. For instance, when we see a picture of a family, we can automatically figure out what content a picture is portraying without giving a second thought. Show the same picture to computer, it will fail to identify if it's not the reference it has been trained too. To a machine, a picture is nothing but a group of pixels. The ability to make a sense out of a picture is still missing and this is where computers have been toiling for a long time.

What is Computer Vision?

Related to computer science, computer vision works on the same principle as humans do. It allows computers to figure out and process faces and objects. In computer vision, it doesn't matter if it's a photo or a video. Until now, computer vision only showed its limited extent. However, with all advancements in technology, such as AI, neural networks and deep learning, computer vision has observed a surge in few tasks of detecting and categorizing data.

Applications of Computer Vision

Self-driving Cars

What makes computer vision important? Well, it helps in solving various problems. This technology is also creating a connection between digital and real world successfully. Computer vision is a part of so many things today. For instance, you have a self-driving car and want to track pedestrians. Computer Vision technology can help you do just that. Cameras make videos from several angles around the car and provide it to computer vision software. Once it sends feed to software, it starts processing the images in order to detect pedestrians, other cars, and extremities of roads. 

self driving cars



The vehicle use deep learning technology and can also calculate the probability of actions to be taken by other drivers in chance of natural occurrences. It leads to the future where your car can steer, stop and accelerate and no longer requires your attention.

Facial recognition

Computer vision has some remarkable applications in facial recognition. This technology allows computers to read and recognize the people's faces. It grants limitless possibilities to all. This is because computer vision algorithms uncover facial features in images and identify the similarities with saved databases of profiles. Facial recognition is also a part of everyday life now.


facial recognition


There are many smartphone devices that use facial recognition technology to verify the identity of their owners. Plus, many social media apps employ this emerging technology to recognize and tag users. Even law enforcement authorities find this technology beneficial and reliable in detecting criminals in video feeds.

Augmented and Mixed Reality

This is the next application of computer vision. It allows computing devices, like smartphones, smart glasses and tablets to superimpose and embed virtual objects on real world imagery. It enables user to interact with a new virtual world. Businesses that invest in AR can provide a new memorable content for their customers. Augmented reality allows users to add digital elements into their actual environment.


AR and MR


Google photos

Google photos use combination of both computer vision and geotags to index your photos. The app collects images of the same objects or people into organized groups. This, in turn, can save you a lot of time while searching for the right image. Sometimes it becomes tough to recall special occasions or event. But now you don't need to look through entire content manually. You can simply put “my photos of bikes” in search bar to get accurate results.

Advances in health-tech

Computer vision has also been playing a crucial part in healthcare advancement. It enables automation of certain tasks like spotting cancerous moles in skin, or detecting symptoms of any disease in MRI scans and x-rays. Computer vision also offers variety of applications. For instance, you can install a smart security camera to constantly connect you with cloud and let you review the video remotely. After installation, you can set up the cloud application to update you regarding any anomaly. Let's say, if any attacker sneaks around your house or fire sets inside the house, it can remotely notify you in such situations. This assures you that there is a vigilant eye taking care of your home. Currently, U.S military is employing computer vision to inspect and warn video content taken by drones or cameras. Since more of the content taken by security camera doesn't need much attention, taking a selective approach seems better. Additionally, you can train the security app to only compile that footage that software has waved as abnormal. Such practice will help you save plenty of storage space in cloud.

Moreover, computers vision can be deployed at security camera's perimeter and train it to send its video footage only if content needs further review and investigation. It will bring convenience and help you redeem network bandwidth by just forwarding what's essential to the cloud.

The Evolution of Computer Vision

Before the emergence of deep learning technology, computer vision was quite insufficient in its offerings, and developers had to do lot of manual coding to run it. In case, if you wished to conduct facial recognition. You would have to go through following steps.

  • Launch a database – First, a database consisting of individual photos of all subjects is collected in a specific format. This step was designed to ease the process of tracking.
  • Interpret Images – Next step is to insert measurements of each individual. You would have to enter various pivotal data points, like width of Nose Bridge, distance between the eyes, and myriad of other specifications that are eccentric to each person.
  • Capture new photos – Either in the form of photographs or videos footage, you would have to click new more images. Once again, you had to undergo the measurement step. This had to be done to label the image with key points. Also, you had to consider angle of the image.
  • Finally, the facial recognition app would compare the images from current and previous database. And, it will inform you if it found a match with any of the profiles it was tracking. As a matter of fact, this process had a very little automation, and involved more manual work. Owing to this, chances of error were found greater.

Machine Learning Helps in Solving Issues

Is computer vision only possible when it is backed by machine learning technology? Well, machine learning helped in resolving many issues related to computer vision. Being a developer, when you use machine learning technology, you don't need to manually insert every single rule into application since machine learning already supports intelligent algorithms. Developers can use algorithm like logistic regression, linear regression, decision trees or support vector machines (SVM) to distinguish patterns, image rating, and object detection in them. To an extent, you can say that machine learning has become one of the main fields in improving traditional software development approach, which was quite challenging earlier.

Deep learning and Computer Vision

Deep learning gave an entirely unique outlook to pursue machine learning. Since deep learning depends on neural networks, it is assumed that it can solve any issue via examples. This is because when you throw flagged examples of specific type of data to neural network, only then it will be able to modify these into a mathematical illustration. This, in turn, will help in indexing data in future. Take an example of facial recognition. When you launch a facial recognition app with deep learning, you must train it first with different faces of people for future detection. You must provide myriad of examples to it. This way neural network will incur face detection with no more specifications on face measurements and features.

Know that deep learning is a much effective way to execute computer vision. Most of computer vision applications, as described above, like detection of cancerous cells, self-driving cars, facial recognition employ the technology of deep learning. Due to advancement in cloud computing resources and hardware, deep learning can be used in practical applications rather than conceptual realm. However, they do have some limitations. For instance, they lack in delivering intelligence and transparency.

While training a deep learning algorithm, you must compile a huge amount of labeled data and take into account other parameters like training epochs, variety and amount of neural network layers. Deep learning tends to be easier, simpler and can be deployed quickly.

Limits of computer vision

With many benefits, there are certain limitations to computer vision, which are:

  • One of the major characteristic of computer vision is its deep learning method. With deep learning, you can think to surpass human performance in classifying images. While deep learning is evocative of human intelligence, neural networks don’t work like human mind. When human mind is known to identify stuff based on experience, knowledge and 3D model, deep neural networks have no such understanding and tend to develop their concept of each type of data individually. Neural networks are good devices for computation but they compare aggregates of data in complex way. This is why technology requires multiple steps of training to identify every object. If it's not trained properly, it can be vulnerable.
  • Computer vision is coping with comprehending the context of images and connection between entities they see. While humans could instantly describe the core of any image, computer vision algorithm doesn't function such way. Instead, pictures are only an assortment of pixels for computer vision algorithms which they statistically draft to specific descriptions.
  • In addition, training of neural networks is also required precisely to recognize the relation between different objects of a photo. This is because human have an understanding of people, relations and how they behave indoors and outdoors, including their expressions. But, computer vision can label any photo with lots of grass and tablecloths as ‘family picnic’ without recognizing the real context behind it. Similarly, it would mark another photo of poor family with mourning look eating in the open-air as a ‘happy family picnic’.

What is Image Processing?

Computer vision vs. image processing

In case of image processing, input and output are both images. An image processing algorithm can manipulate images in many ways, such as smoothing, sharpening, changing the brightness, contrast, highlighting the edges and so on. On the other hand, computer vision focuses on making sense of what a machine sees. Computer vision goes beyond by implementing machine learning and pattern recognition to analysis and image interpretation. 

  Computer vision  

Though both disciplines share similar tools and techniques, they vary at their core.


Image Processing has applications in both field of research, industry and our routine lives. Since it focuses on processing images, this technology can be employed for radar images, seismic data, and natural images and so on. Therefore, traditional image processing has a plenty of scope in numerous fields. Computer vision and image processing collaborate together in many situations. Their combination can be utilized in making of robot (machine) vision system. Many products are also available which are equipped with camera and software to process visual data. However, one thing you must know that the difference between the two becomes blurry when you do pixel to pixel transformations.


Uncover the magic of computer vision by breaking the code of AI (that demonstrates abstract and common sense abilities of human mind). It all depends on ‘when’ or ‘if’ that happens. Until then, you should aim for more data at your computer vision algorithms. Hopefully, this would help in recognizing every possible type of object.


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