What is Computer Vision?
At its core, computer vision is the technology that enables machines (computers) to replicate the human’s visual system. As with many other data science technologies, computer vision is the subfield of artificial intelligence, which is primarily targeted to collect information from digital images/videos, analyze them, and unveil its characteristics. While the technology is closely related to the image processing technique (where it is most widely used), technically two terms can’t be used interchangeably.
Nonetheless, the process of computer vision includes acquisition, analysis, processing, identification, and extraction of information from digital images or videos. The technology enables machines (computers) to understand and extract complex information from the visual content and take necessary actions. The computer vision projects are often associated with decoding the visual content from images/videos into required information, extracted by gathering multidimensional data.
Computer Vision Examples
Self-driving cars are a primary example of the application and potential of computer vision technology. Here the technology is replacing the need for human intervention of driving; using robust AI systems; including computer vision. In this case, the primary role of computer vision technology is to imitate the logic of human vision and assist the machine (Car in this case) to take necessary data-backed actions. In this example, computer vision technology will be responsible to scan, identify, and categorize live objects, based on which the machine (Care) would run or make a stop. For self-driving cars to be a success story, computer vision technology would have to perform all these steps within a second.
Working of Computer Vision
Pattern identification is the key to the successful working and operations of computer vision algorithms. The algorithms will use pattern recognition to train itself in the identification and understanding of the data. Thus, the availability of complex and rich datasets remains crucial to train computer vision algorithms. This in turn is crucial to ensure accurate and fast processing of computer vision algorithms.
Initially, scientists used machine learning algorithms to perform various computer vision applications. However, today scientists are more interested in using deep learning methods for this domain. That’s because training machine learning algorithms require extensive datasets and even human intervention. However, the deep learning models based on neural networks are able to self-learn using examples and labeled data. Thus, making the process of training computer vision algorithms more accurate and efficient.
Why is Computer Vision Important?
The applications of computer vision technology are massive and widespread. Interestingly, the majority of people don’t realize the use of computer vision technology in their daily lives. For instance, selfies and landscape images have become part and parcel of our lives. According to one research, nearly 2 billion images are clicked every day across the world (that’s only the number of images uploaded on various online platforms). Similarly, over 4,000,000 videos are streamed online each day, whereas, over 10,000,000 spam emails are filtered daily across the world. Now, the majority of users don’t realize that all of these routine tasks use computer vision technology. And this is just the beginning, while the technology is extensively used across the entertainment, internet, and communication industry as well.
Remember back in the days when you would have to manually tag the photos on Facebook? How come the platform is able to automatically tag the photos today? Well, it’s the computer vision algorithms that enable Facebook to analyze and process all the images uploaded to the platform and automatically give suggestions for tagging. And we are not even starting on the scientific use of technology in various fields including healthcare, image processing, and others.
So, in essence, technology has quietly become part and parcel of our daily lives without us even noticing it.
How to learn Computer Vision?
You may learn computer vision with respect to its various applications including;
Before you start, you will need strong skills in the following areas;
- Linear algebra
- Basic statistics
- Programming (Python, MATLAB, etc.)
Digital Image processing
For learning the application of computer vision you would require to learn the following techniques;
- Image and videos compressions (JPEG, MPEG)
- Working knowledge of basic image processing tools (median filtering, histogram stretching, band statistics, etc.)
- Supervised classification
- Unsupervised classification
- Neural networks, and
- Ai Image Processing
Machine Learning Basics:
For the development of computer vision skills with respect to machine learning, you would need skills;
- convoluted neural network
- Support vector machine (SVM)
- Fully connected neural networks
- Recurrent neural networks
- Generative adversarial network, and
Basic Computer Vision:
Once you are done with learning the relevant tools and techniques for various applications of computer vision. Next, you would need to develop skills to decode mathematical models for image and video formulations. Start with pattern recognition, and signal processing works, from there you may move into advanced learning.
Best Languages for computer vision
Practically, computer vision algorithms can be developed in various programming languages including;
- Python, and
However, if you ask professionals they would recommend you to go with Python for its easy use and flexibility. Python has quickly become the language of choice for the development of various advanced applications for its versatility and flexibility to use in various cases.
Some of the benefits of using Python for developing computer vision applications include;
- Ease of use
- Widely used language
- Debugging and visualization
- Web backend development
- Detailed documentation
- Powerful matrix library
Challenges for Computer Vision
While computer vision may have emerged as one of the most widely used subfields of artificial intelligence, there are various challenges faced by technology. Replicating human vision is inherently a complex and intricate task. What’s more, developing a system as effective and human vision makes it even more intriguing. Nonetheless, technology has come a long way and will continue to overcome many challenges in the future. Below are some of the existing challenges for the development of computer vision technology;
- Reasoning issues
- Fake content
- Privacy and ethics
- Adversarial attacks
Contributed byMohammed Imran of Dynamics.folio3.com