Introduction : Computer Vision from beginner to advanced | Key Concepts – Part 1

In the fascinating field of computer vision, machines can learn to perceive and comprehend images in the same way that people do. We will go through the basic ideas, uses cases, and technologies that comprise the field of computer vision in this introduction blog post. This guide aims to provide you a thorough overview to get you started, whether you’re a seasoned developer looking to expand your skills or a novice interested in this rapidly developing technology.

How does computer vision work?

Computer vision is a subfield of computer science and artificial intelligence (AI) that gives computers the ability to interpret visual data from our environment. Similar to how our eyes and brains collaborate to interpret images and comprehend our environment, computer vision systems employ algorithms and methods to examine digital photos and videos.

Essential Ideas and Methods

1. Fundamentals of Image Processing

Image processing, or the alteration and improvement of digital images, is the fundamental component of computer vision. To prepare images for additional analysis, methods like resizing, cropping, and filtering (such as sharpening, blurring) are crucial.

2. Feature Extraction

Feature extraction in computer vision is the process of locating particular patterns or features in an image. These characteristics could be corners, edges, textures, or more intricate structures that act as the foundation for more in-depth examination.

3. Identifying and Detecting Objects

Computers can recognise and locate objects within an image or video frame thanks to object detection algorithms. Methods include convolutional neural networks (CNNs), which are used in advanced deep learning techniques, as well as more conventional approaches like Haar cascades and Histogram of Oriented Gradients (HOG).

4. Image Segmentation

By dividing an image into meaningful segments or regions, image segmentation makes it easier to analyse and comprehend the image further. For a variety of applications, including autonomous driving and medical image analysis, segmentation is essential.

Getting Started: Working on Image Data with OpenCv

For computer vision tasks, OpenCV (Open Source Computer Vision Library) is a well-liked open-source library. It offers a wide range of tools and algorithms that let programmers create reliable computer vision applications fast and effectively. In this series, we’ll use OpenCV with Python to examine real-world examples that show how to apply different computer vision techniques. This section will cover the fundamentals of using OpenCV to work with image data, outlining its features and how you can start utilising it for your projects.

Topics we will cover throughout the course-

1. Overview of Computer Vision:

1. What is Computer Vision? What applications does it have?
2. Overview of important ideas: object detection, feature extraction, image processing, etc.
3. The development and history of computer vision technology.

2. Getting Started with OpenCV:

1. Installing OpenCV and setting up your development environment.
2. Image manipulation techniques include resizing, cropping, and rotating images. 
3. Basic image operations include reading, displaying, and saving images.
4. Comprehending the various kinds of image filters (such as Gaussian and Sobel) and how they are used
5. Improving the quality of images through contrast, sharpening, and denoising; * Using OpenCV and Python to implement filters.

3. Object Detection and Localization:

1. An overview of object detection techniques, such as Haar cascades, HOG, and deep learning-based detectors.
2. Using OpenCV to construct a basic object detection pipeline.
3. Applications of object detection in practical settings and case studies.
4. Working with Most Popular Object detection algorithm YOLO

4. Image Segmentation Using YOLO :

1. Gain knowledge of image segmentation algorithms such as contour detection, watershed, and thresholding.
2. Recognise applications of image segmentation in autonomous vehicles, medical imaging, and other fields.
3. Applying OpenCV segmentation techniques.
4. Working with Pretrained YOLO object detection and Image segmentation model.

5. Deep Learning for Computer Vision:

1. An overview of computer vision deep learning frameworks, such as TensorFlow and PyTorch.
2. Convolutional neural network (CNN) construction for image classification applications.
3. Applying transfer learning and optimising pretrained models for particular applications.
4. Building our own image classification neural networks from scratch using Pytorch/Tensorflow.

6. Performance Optimisation in Computer Vision:

1. Methods to increase computer vision algorithms’ speed and effectiveness.
2. Options for hardware acceleration (such as GPU computing) to process images more quickly.
3. Profiling and code optimisation for applications that run in real time.

7. Interactive Projects and Tutorials:

1. Offering Jupyter notebooks or downloadable code repositories for practical instruction.
2. Detailed instructions for constructing realistic computer vision projects such as serveillance camera, traffic congestion system, virtual mouse using eye tracking etc.

And Many more topics in the field of AI and computer vision. we will be extending the topics as we go through these above topics. also i will be creating youtube videos for these tutorials to understand better.