Breaking Bad Habits: Learning Computer Vision Instead of Just Binge-Watching Netflix

Computer vision is a field of artificial intelligence that has become increasingly popular in recent years. It involves teaching machines to understand and interpret visual information, and it has many applications in areas like facial recognition, self-driving cars, and medical imaging. If you’re interested in learning computer vision from scratch, here’s a step-by-step guide that includes algorithms to get you started.

1. Learn the Basics of Python

Python is a widely used programming language in the field of computer vision. It’s easy to learn, and there are many libraries available that make it easy to perform complex tasks. Start by learning the basics of Python, such as syntax, data types, and control structures. Once you’re comfortable with the basics, move on to more advanced topics like object-oriented programming.

2. Understand Image Processing Techniques

Image processing is a crucial part of computer vision. It involves techniques for enhancing, analyzing, and transforming images. Some common techniques include image filtering, edge detection, and image segmentation. Learn the basics of these techniques and how they can be applied to real-world problems. Here are some popular algorithms in image processing:

• Sobel edge detection algorithm
• Canny edge detection algorithm
• Haar cascade classifier algorithm

1. Familiarize yourself with NumPy and Pandas: NumPy is a Python library for numerical computing, while Pandas is a library for data manipulation and analysis. Both of these libraries are essential for working with images in computer vision.
2. Study computer vision algorithms: There are many computer vision algorithms that you should be familiar with, including:
3. Feature detection: Feature detection is the process of identifying distinctive features in an image. Some popular feature detection algorithms include the Harris Corner Detector and SIFT (Scale-Invariant Feature Transform).
4. Object recognition: Object recognition involves identifying objects in an image. Popular object recognition algorithms include Haar Cascades and YOLO (You Only Look Once).

1. Image segmentation: Image segmentation is the process of dividing an image into multiple segments or regions. Some popular image segmentation algorithms include watershed segmentation and k-means clustering.
2. Deep learning: Deep learning is a type of machine learning that involves training artificial neural networks. It has become a popular technique in computer vision because it can automatically learn features from data. Popular deep learning architectures include Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

1. Practice real-world problems: To truly master computer vision, you need to apply what you’ve learned to real-world problems. Look for open-source datasets and try to solve problems like object recognition, face detection, or image segmentation. Build your own projects and experiment with different techniques to see what works best.
2. Learn and use computer vision libraries: Computer vision libraries like Open CV and sci kit-image can save you a lot of time and effort. They provide ready-made algorithms and functions for image processing, feature detection, and more. Learn how to use these libraries and leverage their power in your projects.
3. Keep learning and practicing: Computer vision is a constantly evolving field, and new techniques and algorithms are being developed all the time. Keep learning and practicing to stay up-to-date with the latest developments.

By following this step-by-step approach, including algorithms, you can learn computer vision from scratch and become a proficient computer vision engineer. Good luck!

follow My previous blogs to learn computer vision from scratch = https://medium.com/@korlakuntasaikamal10

LinkedIn = https://www.linkedin.com/in/sai-kamal-korlakunta-a81326163/