Computer Vision Tutorials Index

Overview of computer vision and its Applications

• Computer Vision – Introduction
• A Quick Overview to Computer Vision
• Applications of Computer Vision
• Image Formation Tools & Technique
  • Digital Photography
  • Satellite Image Processing
  • Lidar(Light Detection and Ranging)
  • Synthetic Image Generation
  • Image Stitching & Composition
  • Fundamentals of Image Formation
  • Image Formats
• Beginner’s Guide to Photoshop Tools

Image Processing & Transformation

• Digital Image
  • Digital Image Processing Basics
  • Digital image color spaces
    • RGB, HSV, 
• Image Transformation:
  • Pixel Transformation
  • Geometric transformations
  • Fourier Transforms for Image Transformation
  • Intensity Transformation
• Image Enhancement Techniques
  • Histogram Equalization
  • Color correction
    • Color Inversion using Pillow
    • Automatic color correction with OpenCV and Python
  • Contrast Enhancement
  • Image Sharpening
    • sharpen() function in Wand
  • Edge Detection
    • Image Edge Detection Operators
    • Edge Detection using Pillow
    • OpenCV – Roberts Edge Detection
    • OpenCV – Canny Edge Detector
    • Edge detection using Prewitt, Scharr and Sobel Operator
  • Noise Reduction & Filtering Technique
    • Smoothing and Blurring the Image
    • Gaussian Smoothing
      • GaussianBlur() method
      • Apply a Gauss filter to an image
    • Spatial Filtering
      • Spatial Filters – Averaging filter and Median filter
      • MedianFilter() and ModeFilter()
      • Image Restoration Using Spatial Filtering
    • Bilateral Filtering
  • Morphological operations
    • Erosion and Dilation of
    • Difference between Opening and Closing in Digital Image Processing
  • Image Denoising Techniques
    • Denoising of colored images using opencv
    • Total Variation Denoising
    • Wavelet Denoising
    • Non-Local Means Denoising

Feature Extraction and Description:

• Feature detection and matching with OpenCV-Python
• Boundary Feature Descriptors
• Region Feature Descriptors
• Interest point detection
• Local feature descriptors
• Harris Corner Detection
• Scale-Invariant Feature Transform (SIFT)
• Speeded-Up Robust Features (SURF)
  • Mahotas – Speeded-Up Robust Features
• Histogram of Oriented Gradients (HOG)
• Principal Component as Feature Detectors
• Local Binary Patterns (LBP)
• Convolutional Neural Networks (CNN)

Deep Learning for Computer Vision

• Convolutional Neural Networks (CNN)
  • Introduction to Convolution Neural Network
  • Types of Convolutions
    • Strided Convolutions
    • Dilated Convolution
      • Flattened Convolutions
      • Spatial and Cross-Channel convolutions
      • Depthwise Separable Convolutions
      • Grouped Convolutions
      • Shuffled Grouped Convolutions
      • Continuous Kernel Convolution
  • What is a Pooling Layers?
  • Introduction to Padding
    • Same and Valid Padding
• Data Augmentation in Computer Vision
• Deep ConvNets Architectures for Computer Vision
  • ImageNet Dataset
  • Transfer Learning for Computer Vision
    • What is Transfer Learning?
    • Residual Network
      • ResNet
    • Inception Network
      • GoogleNet (or InceptionNet)
      • Inception Network V1
      • Inception V2 and V3
    • MobileNet
      • Image Recognition with Mobilenet
    • EfficientNet
    • Visual Geometry Group Network (VGGNet)
      • VGG-16 | CNN model
    • FaceNet Architecture
• AutoEncoders
  • How Autoencoders works
  • Encoder and Decoder network architecture
    • Difference between Encoder and Decoder
  • Latent space representation
  • Implementing an Autoencoder in PyTorch
  • Autoencoders for Computer Vision:
    • Feedforward Autoencoders
    • Deep Convolutional Autoencoders
    • Variational autoencoders (VAEs)
    • Denoising autoencoders
    • Sparse autoencoders
    • Adversarial Autoencoder
  • Applications of Autoencoders
    • Dimensionality reduction and feature extraction using autoencoders 
    • Image compression and reconstruction techniques 
    • Anomaly detection and outlier identification with autoencoders
• Generative Adversarial Network (GAN)
  • Deep Convolutional GAN
  • StyleGAN – Style Generative Adversarial Networks
  • Cycle Generative Adversarial Network (CycleGAN)
  • Super Resolution GAN (SRGAN)
  • Selection of GAN vs Adversarial Autoencoder models
  • Real-Life Application of GAN
    • Image and Video Generation using DCGANs
    • Conditional GANs for image synthesis and style transfer 
    • VAEs for image generation and latent space manipulation
  • Evaluation metrics for generative models

Object Detection and Recognition

• Introduction to Object Detection and Recognition
  • Introduction to Object Detection?
• Traditional Approaches for Object Detection and Recognition
  • Feature-based approaches: SIFT, SURF, HOG
  • Sliding Window Approach
  • Selective Search for Object Detection
  • Haar Cascades for Object Detection
  • Template Matching
• Object Detection Techniques
  • Bounding Box Predictions in Object Detection
  • Intersection over Union
  • Non – Max Suppression
  • Anchor Boxes in Object Detection
  • Region Proposals in Object Detection
  • Feature Pyramid Networks (FPN)
  • Contextual information and attention mechanisms
  • Object tracking and re-identification
• Neural network-based approach for Object Detection and Recognition
  • R Proposals in Object Detection | R – CNN
  • Fast R-CNN
  • Faster R – CNN
  • Single Shot MultiBox Detector (SSD)
  • You Look Only Once(YOLO) Algorithm in Object Detection
    • YOLO v2 – Object Detection
• Object Recognition in Video
• Evaluation Metrics for Object Detection and Recognition
  • Intersection over Union (IoU)
  • Precision, recall, and F1 score
  • Mean Average Precision (mAP)
• Object Detection and Recognition Applications
  • Object Detection and Self-Driving Cars
  • Object Localization
  • Landmark Detection
  • Face detection and recognition
    • What is Face Recognition Task?
    • DeepFace Recognition
    • Eigen Faces for Face Recognition
    • Emojify using Face Recognition with Machine Learning
    • Face detection and landmark localization
    • Facial expression recognition
  • Hand gesture recognition
  • Pedestrian detection
  • Object Detection with Detection Transformer (DETR) by Facebook
  • Vehicle detection and tracking
  • Object detection for autonomous driving
  • Object recognition in medical imaging

Image Segmentation

• Introduction to Image Segmentation
• Point, Line & Edge Detection
• Thresholding Technique for Image Segmentation
• Contour Detection & Extraction
• Graph-based Segmentation
• Region-based Segmentation
  • Region and Edge Based Segmentation
  • Watershed Segmentation Algorithm
  • Semantic Segmentation
• Deep Learning Approaches to Image Segmentation
  • Fully convolutional networks (FCN)
  • U-Net architecture for semantic segmentation
    • Image Segmentation Using UNet
  • Mask R-CNN for instance segmentation
    • Mask R – CNN
  • Encoder-Decoder architectures (e.g., SegNet, DeepLab)
• Evaluation Metrics for Image Segmentation
  • Pixel-level evaluation metrics (e.g., accuracy, precision, recall)
  • Region-level evaluation metrics (e.g., Jaccard Index, Dice coefficient)
  • Mean Intersection over Union (mIoU)
  • Boundary-based evaluation metrics (e.g., average precision, F-measure)

3D Reconstruction

• Structure From Motion for 3D Reconstruction
• Monocular Depth Estimation Techniques
• Fusion Techniques for 3D Reconstruction
  • LiDAR | Light Detection and Ranging
  • Depth Sensor Fusion
• Volumetric Reconstruction
• Point Cloud Reconstruction

Computer Vision Interview Questions

• Computer Vision Interview

Computer Vision Projects

• Top Computer Vision Projects

Computer vision, a fascinating field at the intersection of computer science and artificial intelligence, which enables computers to analyze images or video data, unlocking a multitude of applications across industries, from autonomous vehicles to facial recognition systems.

This Computer Vision tutorial is designed for both beginners and experienced professionals, covering both basic and advanced concepts of computer vision, including Digital Photography, Satellite Image Processing, Pixel Transformation, Color Correction, Padding, Filtering, Object Detection and Recognition, and Image Segmentation.