Computer Vision Documentation

This document outlines the key concepts, techniques, and tools used in Computer Vision, structured into thematic chapters.

Chapter 1: Introduction to Computer Vision

• What is Computer Vision? Applications & History: An overview of the field, its historical development, and its widespread applications.
• A Quick Overview of Computer Vision: A concise summary of the core principles and goals of computer vision.
• Applications of Computer Vision: Detailed exploration of how computer vision is used across various industries (e.g., healthcare, automotive, security, manufacturing).
• Fundamentals of Image Formation: Understanding how images are created, including concepts like cameras, lenses, light, and pixel representation.
• Satellite Image Processing: Specific techniques and challenges related to analyzing satellite imagery.

Chapter 2: Image Basics

• Difference Between RGB, CMYK, HSV, and YIQ Color Models: Explaining various color spaces and their properties, advantages, and use cases.
• Hands-on: Load, Display, and Save Images: Practical guide to basic image manipulation.
• Image I/O using OpenCV and PIL: Utilizing popular libraries for reading, writing, and managing image files.
• RGB, Grayscale, Binary Images: Understanding different image representations based on color channels and bit depth.

Chapter 3: Image Processing Fundamentals

• Convolution and Filtering: Core operations for transforming images, including kernels, sliding windows, and their effects.
• Edge Detection (Sobel, Canny): Techniques for identifying sharp changes in image intensity, crucial for feature extraction.
• Hands-on: Build Your Own Image Filters in Python: Practical implementation of custom image filters to understand their mechanics.
• Morphological Operations: Operations like erosion, dilation, opening, and closing for shape manipulation and noise removal.
• Smoothing Techniques (Gaussian, Median): Methods to reduce noise and blur images.
• Thresholding and Histograms: Techniques for segmenting images based on pixel intensity and analyzing image brightness distribution.

Chapter 4: Feature Extraction & Matching

• Create Local Binary Pattern of an Image using OpenCV-Python: A texture descriptor used for image classification and matching.
• Feature Descriptors and Matching: Concepts behind identifying and comparing distinctive points or regions within images.
• Feature Detection and Matching with OpenCV-Python: Practical implementation using OpenCV for finding and matching features.
• Feature Matching using Brute Force in OpenCV: A straightforward method for comparing feature descriptors.
• Feature Matching using ORB Algorithm in Python-OpenCV: Utilizing the Oriented FAST and Rotated BRIEF (ORB) algorithm for efficient feature matching.
• Hands-on: Keypoint Detection and Feature Matching: Practical application of detecting salient points and establishing correspondences between images.
• Harris Corner Detector, FAST, SIFT, ORB: Overview of prominent algorithms for detecting keypoints and corners in images.
• Image Stitching (Panorama): Techniques for combining multiple images to create a wider field of view.
• Mahotas – Speeded-Up Robust Features: Using the Mahotas library and its implementation of SIFT-like features.

Chapter 5: Geometric Vision

• Camera Calibration (Intrinsic/Extrinsic Parameters): Determining the internal properties (focal length, principal point) and external pose (rotation, translation) of a camera.
• Epipolar Geometry, Stereo Vision: Principles governing the relationship between two camera views and methods for depth perception from stereo images.
• Homography, Affine, and Projective Transforms: Geometric transformations used to map points from one plane to another, essential for image warping and alignment.
• Camera Calibration with Python – OpenCV: Practical guide to calibrating cameras using OpenCV.
• Depth Estimation Basics: Understanding how to infer the distance of objects from a scene.
• Hands-on: Perspective Correction and Camera Calibration: Practical exercises in correcting image perspective and calibrating cameras.
• Python OpenCV – Depth Map from Stereo Images: Generating depth maps from pairs of stereo images.
• Python OpenCV – Pose Estimation: Estimating the 3D position and orientation of objects in an image.

Chapter 6: Motion & Tracking

• Background Subtraction, Frame Differencing: Techniques for identifying moving objects in a video by comparing frames.
• Hands-on: Build a Basic Tracking System on Video: Practical implementation of tracking algorithms to follow objects in video sequences.
• Kalman Filter, Optical Flow (Lucas-Kanade, Farneback): Advanced methods for predicting object motion and estimating pixel movement between frames.

Chapter 7: Classical Object Detection

• HOG + SVM (Face, Pedestrian Detection): Using Histogram of Oriented Gradients (HOG) features with Support Vector Machines (SVM) for detecting common objects.
• Sliding Window + Image Pyramid Approach: A traditional method for scanning images at multiple scales to find objects.
• Viola-Jones for Face Detection: An efficient and widely adopted algorithm for real-time face detection.

Chapter 8: Introduction to CNNs

• CNN | Introduction to Padding: Understanding how padding affects the output dimensions of convolutional layers.
• CNN | Introduction to Pooling Layer: Exploring pooling operations (e.g., max pooling, average pooling) for downsampling and feature aggregation.
• Continuous Kernel Convolution: A theoretical concept related to convolution operations.
• Convolutional Neural Network (CNN) Architectures: An overview of the fundamental building blocks and structures of CNNs.
• Dilated Convolution: A technique to increase the receptive field of convolutional layers without increasing the number of parameters.
• Hands-on: Image Classification Using Pre-trained CNN (e.g., ResNet18): Practical application of using pre-trained deep learning models for image classification tasks.
• ML | Introduction to Strided Convolutions: Understanding how strides in convolutions affect output size and feature extraction.
• What are CNNs? Layers, Kernels, Activation: A foundational explanation of the core components of Convolutional Neural Networks.
• What is the Difference Between ‘SAME’ and ‘VALID’ Padding in tf.nn.max_pool of TensorFlow?: Clarifying the behavior of different padding strategies in TensorFlow.

Chapter 9: CNN Architectures & Applications

• Architectures: LeNet, AlexNet, VGG, ResNet: Deep dive into seminal CNN architectures and their contributions.
• Deep Transfer Learning – Introduction: Leveraging knowledge gained from pre-trained models for new tasks.
• Image Recognition with MobileNet: Efficient CNN architectures designed for mobile and embedded devices.
• Introduction to Residual Networks: Understanding the concept of residual connections that enable training of very deep networks.
• ML | Inception Network V1: Exploring the Inception module and its efficiency.
• Residual Networks (ResNet) – Deep Learning: Detailed explanation of ResNet architectures.
• Top 5 PreTrained Models in Natural Language Processing (NLP): While this is NLP focused, it might be included for broader ML context.
• Understanding GoogLeNet Model – CNN Architecture: Analysis of the GoogLeNet (Inception) architecture.
• VGG-16 | CNN Model: A detailed look at the VGG-16 architecture.
• What is Transfer Learning?: A comprehensive explanation of the transfer learning paradigm.

Chapter 10: Representation Learning & Generative Models

• Autoencoders in Machine Learning: Unsupervised learning technique for dimensionality reduction and feature learning.
• Deep Convolutional GAN with Keras: Implementing Generative Adversarial Networks (GANs) using convolutional layers with Keras.
• Difference Between Encoder and Decoder: Explaining the two main components of autoencoders and similar generative models.
• Generative Adversarial Network (GAN): Introduction to GANs for generating synthetic data.
• How Autoencoders Work?: Detailed explanation of the autoencoder mechanism.
• Implementing an Autoencoder in PyTorch: Practical guide to building autoencoders using PyTorch.
• StyleGAN – Style Generative Adversarial Networks: Advanced GANs for generating high-quality, stylized images.

Chapter 11: Object Detection with Deep Learning

• CNN-based Detectors: R-CNN, Fast R-CNN, Faster R-CNN: Evolution of region-based convolutional neural networks for object detection.
• Data Annotation, Bounding Boxes: The crucial process of labeling data for supervised object detection.
• Hands-on: Object Detection with YOLOv5: Practical implementation of the You Only Look Once (YOLO) object detection model.
• YOLO (v5/v8), SSD, RetinaNet: Overview of popular and efficient single-stage object detection architectures.

Chapter 12: Semantic & Instance Segmentation

• FCN, U-Net, DeepLab: Key deep learning architectures for pixel-wise image segmentation.
• Hands-on: Segmentation Using U-Net on Biomedical Images: Applying U-Net for segmenting specific regions in medical images.
• Mask R-CNN: An extension of Faster R-CNN for instance segmentation.
• Use-Cases: Medical Imaging, Autonomous Driving: Applications of semantic and instance segmentation in critical domains.

Chapter 13: OCR Fundamentals

• Hands-on: Extract Text and Tables from Invoices or Forms: Practical guide to optical character recognition (OCR) for document analysis.
• LayoutLM, Donut for Document Understanding: Advanced models for understanding the structure and content of documents.
• Table Detection and Structure Recognition: Techniques for identifying and parsing tables within documents.
• Tesseract OCR, EasyOCR: Popular open-source OCR engines.
• Text Localization: EAST/CRAFT Detectors: Algorithms for finding the precise location of text within images.

Chapter 14: Vision Transformers

• Attention Mechanism Recap: Reviewing the attention mechanism, fundamental to Transformers.
• DETR for Object Detection: A transformer-based approach for object detection without anchors or non-maximum suppression.
• Hands-on: Try ViT and DETR on Custom Datasets: Practical application of Vision Transformers and DETR on user-provided data.
• SAM (Segment Anything Model): Exploring the capabilities of large-scale models for zero-shot segmentation.
• Vision Transformer (ViT), DeiT: Introduction to the Vision Transformer and its variants.

Chapter 15: Model Optimization & Edge Deployment

• Hands-on: Deploy YOLO on a Webcam Using ONNX/TensorRT: Practical guide to deploying object detection models for real-time inference.
• Quantization, Pruning: Techniques for reducing model size and improving inference speed for deployment on resource-constrained devices.
• Real-time Webcam Inference: Achieving smooth and responsive object detection or other vision tasks on live video streams.
• TensorRT, ONNX, OpenVINO: Frameworks and formats for optimizing and deploying deep learning models on various hardware.

Chapter 16: Capstone Projects

• Defect Detection in Manufacturing: Applying computer vision for quality control in industrial settings.
• Document Workflow Automation: Using OCR and computer vision to automate document processing.
• License Plate Recognition: Implementing systems for automatic license plate identification.
• Retail Analytics (People Counting, Shelf Monitoring): Utilizing computer vision for store operations and customer behavior analysis.

Chapter 17: Advanced Topics

• 3D Reconstruction: Creating three-dimensional models from images or sensor data.
• Augmented Reality: Overlaying digital information onto the real world.
• Dataset Creation Tools: CVAT, LabelImg, Roboflow: Software for creating and managing annotated datasets for machine learning.
• Face Recognition (Using FaceNet / Dlib): Implementing biometric systems for identifying individuals based on facial features.
• SLAM (Simultaneous Localization and Mapping): Enabling robots and devices to build a map of their environment while tracking their own position.

Tools & Libraries

• Detectron2, Ultralytics YOLO: Libraries and frameworks for object detection and segmentation tasks.
• ONNX, TensorRT, OpenVINO: Tools for optimizing and deploying machine learning models across different hardware platforms.
• OpenCV, scikit-image, PIL: Essential libraries for fundamental image processing and computer vision operations in Python.
• PyTorch or TensorFlow/Keras: Leading deep learning frameworks for building and training neural networks.
• Streamlit / Flask (for Basic Demos): Web application frameworks for creating interactive demonstrations of computer vision models.
• Tesseract, EasyOCR, LayoutLM, Hugging Face Transformers: Libraries for OCR, document understanding, and leveraging pre-trained language and vision models.

OpenCV

• Computational Photography: Advanced image processing techniques that go beyond basic enhancement.
• Core Operations: Fundamental image manipulation functions within OpenCV.
• Feature Detection and Description: Implementing algorithms for identifying and describing salient image features.
• GUI Features in OPenCV: Tools for creating graphical user interfaces for computer vision applications.
• Image Processing in OpenCV: A broad overview of the image manipulation capabilities of OpenCV.
• Introduction: General introduction to the OpenCV library.
• Object Detection: Utilizing OpenCV for detecting objects in images and videos.
• OPenCV-Python Bindings: How to use OpenCV effectively with the Python programming language.