Full Stack Computer Vision Career Path- Beginner

Gain a foundational understanding of computer vision and its applications in various industries. Learn the basic terminologies and concepts essential for working with images and videos.

• 1.1 What is Computer Vision?:
  • ‍Understand the core concepts and objectives of computer vision, including applications in healthcare (e.g., tumor detection), automotive (e.g., autonomous driving), and retail (e.g., automated checkout).
• 1.2 Basic Terminologies:
  • Larn about pixels, images, channels, and how they form the basis of computer vision tasks, which are fundamental concepts applicable across all computer vision projects.

Explore essential image processing techniques to manipulate and enhance images for analysis.

• 1.1 Image Representation:
  • ‍Understand the differences between grayscale and colour images and their use cases, such as medical imaging where grayscale images are used for X-rays and MRIs.

• 1.2 Image Operations:
  • Master techniques for resizing, cropping, and rotating images, crucial for preprocessing images in facial recognition systems.
• 1.3 Filtering:
  • Apply blurring, sharpening, and edge detection to improve image quality, which is vital for enhancing satellite images used in environmental monitoring.
    

Get hands-on with OpenCV, a powerful library for computer vision tasks.

• 1.1 Installation and Setup:
  • ‍Learn how to set up OpenCV in your development environment, similar to how startups set up environments for rapid prototyping.
• 1.2 Reading and Displaying Images:
  • Acquire skills to read, display, and save images using OpenCV, essential for developing applications that process and analyze image data in real-time.

• 1.3 Basic Image Manipulation:
  • Perform basic image transformations and manipulations with OpenCV, foundational for creating photo editing tools and filters.

Learn techniques to detect and describe features in images for various computer vision applications.

• 1.1 Corner Detection:
  • ‍Understand Harris corner detection and its applications in detecting features for 3D reconstruction in robotics.
• 1.2 Feature Descriptors:
  • Explore SIFT, ORB, and other descriptors for robust feature matching, important for matching keypoints in satellite images for geospatial analysis.
• 1.3 Matching Keypoints:
  • Learn techniques for matching keypoints between images, useful for creating panoramic images from multiple photographs.

Delve into object detection methods to identify and locate objects within images.

• 1.1 Understanding Bounding Boxes:
  • ‍Understanding Python syntax and data types.
• 1.2 Object-Oriented Programming (OOPs):
  • IGrasp the basics of bounding for object detection, which is crucial for implementing security systems that detect and track intruders.
• 1.3 Control Structures and Functions:
  • Explore algorithms like Haar cascades and HOG for detecting objects, commonly used in face detection in cameras and mobile phones.

Utilize deep learning to classify images into categories.

• 1.1 Introduction to Neural Networks:
  • ‍Understand the basics of neural networks and their applications in image classification, such as classifying medical images to detect diseases.
• 1.2 Convolutional Neural Networks (CNNs):
  • Learn how CNNs work and why they are effective for image classification, essential for identifying defects in manufacturing processes through image analysis.
• 1.3 Training a Simple CNN:
  • Hands-on training of a CNN for a basic image classification task, such as building a system to categorize images on social media platforms.

Leverage pre-trained models to enhance image classification tasks with less training data.

• 1.1 Using Pre-trained Models:
  • ‍Explore how to use pre-trained models for various tasks, accelerating the development of custom image classifiers in limited data scenarios.
• 1.2 Fine-tuning Models:
  • Learn techniques for fine-tuning pre-trained models to suit specific needs, such as adapting general object detectors for specific industrial applications.

Learn to classify each pixel in an image into a meaningful category.

• 1.1 Understanding Semantic Segmentation:
  • ‍Explore the concepts and applications of semantic segmentation, such as segmentation medical images to identify different tissue types.

• 1.2 Introduction to U-Net Architecture:
  • Understand the U-Net architecture used for image segmentation tasks, like using U-Net for automated tumor segmentation in radiology.