Computer Vision Tutorial

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.

What is Computer Vision?

Computer vision is a field of study within artificial intelligence (AI) that focuses on enabling computers to Intercept and extract information from images and videos, in a manner similar to human vision. It involves developing algorithms and techniques to extract meaningful information from visual inputs and make sense of the visual world.

Prerequisite: Before Starting Computer Vision It’s Recommended that you should have a foundational knowledge of Machine Learning, Deep learning and an OpenCV. you can refer to our tutorial page on prerequisites technologies.

Computer Vision Examples:

Here are some examples of computer vision:

• Facial recognition: Identifying individuals through visual analysis.
• Self-driving cars: Using computer vision to navigate and avoid obstacles.
• Robotic automation: Enabling robots to perform tasks and make decisions based on visual input.
• Medical anomaly detection: Detecting abnormalities in medical images for improved diagnosis.
• Sports performance analysis: Tracking athlete movements to analyze and enhance performance.
• Manufacturing fault detection: Identifying defects in products during the manufacturing process.
• Agricultural monitoring: Monitoring crop growth, livestock health, and weather conditions through visual data.

These are just a few examples of the many ways that computer vision is used today. As the technology continues to develop, we can expect to see even more applications for computer vision in the future.

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

How does Computer Vision Work?

Computer Vision Works similarly to our brain and eye work, To get any Information first our eye capture that image and then sends that signal to our brain. Then After, our brain processes that signal data and converted it into meaningful full information about the object then It recognizes/categorises that object based on its properties. 

In a similar fashion to Computer Vision Work, In CV we have a camera to capture the Objects and Then it processes that Visual data by some pattern recognition algorithms and based on that property that object is identified. But, Before giving unknown data to the machine/Algorithm, we trained that machine on a vast amount of Visual labelled data. This labelled data enables the machine to analyze different patterns in all the data points and can relate to those labels.

Example: Suppose we provide audio data of thousands of bird songs. In that case, the computer learns from this data, analyzes each sound, pitch, duration of each note, rhythm, etc., and hence identifies patterns similar to bird songs and generates a model. As a result, this audio recognition model can now accurately detect whether the sound contains a bird song or not for each input sound.

Evolution of Computer Vision

Time Period	Evolution of Computer Vision
2010-2015	Development of deep learning algorithms for. recognition image. Introduction of convolutional neural networks (CNNs) for image classification.  Use of computer vision in autonomous vehicles for object detection and navigation.
2015-2020	Advancements in real-time object detection with systems like YOLO (You Only Look Once).  in facial recognition technology, used in various applications like unlocking smartphones and surveillance. Integration of computer vision in augmented reality (AR) and virtual reality (VR) systems.  Use of computer vision in medical imaging for disease diagnosis.
2020-2025 (Predicted)	Further advancements in real-time object detection and image recognition. More sophisticated use of computer vision in autonomous vehicles. Increased use of computer vision in healthcare for early disease detection and treatment. Integration of computer vision in more consumer products, like smart home devices.

Applications of Computer Vision

1. Healthcare: Computer vision is used in medical imaging to detect diseases and abnormalities. It helps in analyzing X-rays, MRIs, and other scans to provide accurate diagnoses.
2. Automotive Industry: In self-driving cars, computer vision is used for object detection, lane keeping, and traffic sign recognition. It helps in making autonomous driving safe and efficient.
3. Retail: Computer vision is used in retail for inventory management, theft prevention, and customer behaviour analysis. It can track products on shelves and monitor customer movements.
4. Agriculture: In agriculture, computer vision is used for crop monitoring and disease detection. It helps in identifying unhealthy plants and areas that need more attention.
5. Manufacturing: Computer vision is used in quality control in defect detect can It. manufacturing products that are hard to spot with the human eye.
6. Security and Surveillance: Computer vision is used in security cameras to detect suspicious activities, recognize faces, and track objects. It can alert security personnel when it detects a threat.
7. Augmented and Virtual Reality: In AR and VR, computer vision is used to track the user’s movements and interact with the virtual environment. It helps in creating a more immersive experience.
8. Social Media: Computer vision is used in social media for image recognition. It can identify objects, places, and people in images and provide relevant tags.
9. Drones: In drones, computer vision is used for navigation and object tracking. It helps in avoiding obstacles and tracking targets.
10. Sports: In sports, computer vision is used for player tracking, game analysis, and highlight generation. It can track the movements of players and the ball to provide insightful statistics.

FAQs on Computer Vision

Q1. What is OpenCV in computer vision?

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

Q2. Is cv2 and OpenCV same?

No, Actually cv2 was a old Interface of old OpenCV versions named as cv. it is the name that openCV developers choose when they created the binding generators.

Q3. Is OpenCV a C++ or Python?

OpenCV is written by C++ and has more than 2,500 optimized algorithms.

Q4. Which algorithm OpenCV uses?

OpenCV uses various algorithms, including but not limited to, Haar cascades, SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), and ORB (Oriented FAST and Rotated BRIEF).