Why Now is the Best Time to Learn Computer Vision
Computer vision is an exciting and rapidly growing field with numerous applications in various industries.
If you’re thinking about learning computer vision, there’s no better time than now. Let’s explore what computer vision is, how it works, and why it’s so valuable today.
What is Computer Vision?
Computer vision is a branch of artificial intelligence (AI) and computer science that enables computers to interpret and make decisions based on visual data. It mimics human vision by teaching machines to recognize objects, track movements, and even make decisions based on visual inputs.
How Does Computer Vision Work?
Imagine computer vision as the brain of a camera. It doesn’t just capture images; it analyzes them. Here’s a simple breakdown:
- Image to Pixels: First, an image is converted into pixels.
- Pattern Recognition: The system identifies patterns and features in those pixels.
- Data Extraction: It extracts meaningful information, like identifying objects, faces, or even emotions.
- Analysis and Action: Finally, it interprets the data and makes decisions or provides insights.
Reasons to Learn Computer Vision
Here are some readers to learn Computer Vision:
Growing Market
The computer vision market is booming. By 2024, it’s expected to grow at a compound annual growth rate (CAGR) of 7.8%, reaching USD 17.4 billion. This growth translates to numerous job opportunities and an increasing demand for skilled computer vision engineers.
Versatility
Computer vision has applications in many fields:
- Healthcare: Enhancing diagnostic accuracy through medical imaging.
- Automotive: Enabling features in self-driving cars like obstacle detection and lane keeping.
- Retail: Improving inventory management and customer experience with visual tracking and recognition systems.
With advancements in data science, we’re moving towards a future where many tasks are fully automated, further boosting the need for computer vision experts.
Getting Started with Computer Vision
Learning computer vision is easier than ever with the vast array of resources available online. However, it can be overwhelming to choose the right ones. Here’s a simplified path to get you started.
Essential Skills Needed To Learn Computer Vision
To master computer vision, you’ll need the following skills:
- Mathematical Knowledge: A solid understanding of calculus, probability, and statistics.
- Coding Skills: Proficiency in at least one programming language, ideally Python, which is widely used in computer vision projects.
- Data Structures: Knowledge of data structures to develop efficient algorithms for image processing.
- Hands-On Practice: Access to a computer for applying theoretical knowledge through practical projects.
Practical Projects to Learn Computer Vision
Start with Open-Source Projects
Practical experience is key. Begin with open-source projects that match your skill level. These projects help you apply what you’ve learned and develop your problem-solving skills.
Read more: How is Artificial Intelligence (AI) In Gaming Changing The Market?
OpenCV: A Key Tool for Computer Vision
What is OpenCV?
As the name suggests, OpenCV (Open Source Computer Vision Library) is a free and ‘open-source’ software library for computer vision and machine learning.
OpenCV was created to offer a standard infrastructure for computer vision applications and let various products/solutions incorporate machine perception more effectively. Because OpenCV is a BSD-licensed product, it is simpler for companies to use and alter the code.
The Open Source Computer Vision Library contains more than 2500 real-time computer vision algorithms, detailed documentation, and sample code. JavaScript lets you use it in your browser on Windows, Linux, Mac OS X, Android, and iOS.
With over 23 million downloads, OpenCV is one of the top choices amongst CV enthusiasts. In case you aren’t familiar with the OpenCV platform, here are a few OpenCV projects you can refer to for learning computer vision-
Popular Projects
- Beginner Project: Start with basic projects using Python and OpenCV. Learn to process videos, detect images, and apply smoothing techniques.
- Advanced Project: Once comfortable with the basics, move on to more complex projects. Implement advanced algorithms like camshift, kmeans, and meanshift to deepen your understanding.
Machine Learning and Computer Vision
Machine learning and computer vision are two disciplines that have grown more deeply intertwined. Computer vision has advanced in terms of recognition and tracking thanks to machine learning.
It provides efficient capture, image processing, and object focus methods for computer vision. As a result, computer vision has expanded the capabilities of machine learning.
A digital picture or video, a sensor device, an interpreting device, and the interpretation stage are all part of the process. In the interpreting device and analysis step of computer vision, machine learning is applied.
Now that you have an idea of what is the relationship between Machine Learning and Computer Vision, let us first take a look at a few computer vision applications to understand how machine learning is applied in computer vision-
Applications
- Video Monitoring: Track the movement of objects in sports or surveillance. Object detection techniques help in accurately following the subject.
- Driverless Vehicles: Use computer vision for essential functions in autonomous vehicles, such as lane detection, road curvature estimation, and obstacle identification.
Programming Languages for Computer Vision
Python
When it comes to beginner and professional programmers, Python is a programming language that makes it easier for them to translate their ideas into code.
Many developers prefer Python for CV since it is now the most competent and well-supported programming language in the field of machine learning.
You can choose to learn Python for Computer Vision if you aren’t comfortable with any other programming language.
As we already know, computer vision allows computers to recognize things ‘digitally’ through photos or movies. Using Python to implement CV enables developers to automate processes that need visualization.
- Ease of Coding: Python’s syntax is straightforward, making it easier to learn and implement.
- Rapid Prototyping: Quickly test and iterate on ideas.
- Extensive Libraries: Libraries like OpenCV, TensorFlow, and PyTorch simplify complex tasks.
- Open-Source: Free to use and supported by a large community.
- Community Support: Abundant resources and help available online.
- Web Integration: Easy integration with web frameworks like Django for deploying computer vision applications.
C++
Moving on, we believe some of our readers might not be comfortable with learning Python or any other programming language for computer vision.
Hence, we suggest you learn C++ for computer vision. We recommend that you begin by studying basic C++ syntax and ideas such as pointers, structs, and so on.
Another thing to note is that OpenCV is a C++ library, and to learn computer vision, you must first learn OpenCV. So, this means that if you are planning to build a career in Data Science as a CV Engineer, then learning C++ could be an advantage for you!
Here is why C++ is recommended:
- Performance: C++ provides faster runtime for computational tasks.
- Compatibility: Many foundational libraries in computer vision are written in C++.
- Professional Edge: Knowledge of C++ can be advantageous in performance-critical applications.