Computer Vision Tutorial

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Computer Vision Tutorial

Computer Vision is the AI domain that lets machines make sense of images and video. Combining classical image processing with deep models, it detects objects, recognizes patterns, and pulls useful signals from visual input.

Basics

This section covers how systems inspect and interpret imagery—via image processing methods and deep learning architectures.

  • Introduction
  • Image Processing
  • Image Representation and Pixels
  • Image Analysis and Manipulation

Mathematical Prerequisites

Before diving into Computer Vision, a solid footing in selected math topics is helpful:

1. Linear Algebra

  • Linear Algebra
  • Vectors
  • Matrices and Tensors
  • Eigenvalues and Eigenvectors
  • Singular Value Decomposition

2. Signal Processing

  • Signal Processing
  • Image Filtering and Convolution
  • Discrete Fourier Transform (DFT)
  • Fast Fourier Transform (FFT)
  • Principal Component Analysis (PCA)

Key Concepts

These ideas cover how digital images are transformed and examined. Typical image processing work includes:

  • Image Transformation
  • Image Transformation
  • Geometric Transformations
  • Fourier Transform
  • Intensity Transformation
  • Image Enhancement
  • Image Enhancement
  • Histogram Equalization
  • Contrast Enhancement
  • Image Sharpening
  • Color Correction
  • Noise Reduction Techniques
  • Noise Reduction Techniques
  • Median Filtering
  • Bilateral Filtering
  • Wavelet Denoising
  • Morphological Operations
  • Morphological Operations
  • Erosion and Dilation

2. Feature Extraction

Feature extraction isolates distinctive image cues for later analysis. Common techniques include:

  • Edge Detection Techniques
  • Computer Vision Algorithms
  • Edge Detection Techniques
  • Canny Edge Detector
  • Sobel Operator
  • Laplacian of Gaussian (LoG)
  • Corner and Interest Point Detection
  • Harris Corner Detection
  • Feature Descriptors
  • Feature Descriptors
  • SIFT (Scale-Invariant Feature Transform)
  • SURF (Speeded-Up Robust Features)
  • ORB (Oriented FAST and Rotated BRIEF)
  • HOG (Histogram of Oriented Gradients)

Popular Libraries

Practical computer vision work commonly relies on these libraries:

  • OpenCV
  • TensorFlow
  • PyTorch
  • Scikit Learn

Deep Learning

Deep learning has advanced computer vision by giving machines stronger tools for understanding visual data.

1. Convolutional Neural Networks (CNNs)

CNNs learn layered spatial features from images.

  • Deep Learning
  • Convolutional Neural Networks
  • Convolutional Layers
  • Pooling Layers
  • Fully Connected Layers

2. Generative Adversarial Networks (GANs)

A GAN pairs two networks that compete to produce convincing images.

  • Generative Adversarial Networks (GANs)
  • Deep Convolutional GAN (DCGAN)
  • Conditional GAN (cGAN)
  • Cycle-Consistent GAN (CycleGAN)
  • Super-Resolution GAN (SRGAN)
  • StyleGAN

3. Variational Autoencoders (VAEs)

VAEs are probabilistic autoencoders that model a distribution over latent space rather than a single fixed code.

  • Autoencoders
  • Variational Autoencoders (VAEs)
  • Denoising Autoencoders (DAE)
  • Convolutional Autoencoder (CAE)

4. Vision Transformers (ViT)

Inspired by language transformers, ViTs treat an image as a sequence of patches and apply self-attention.

  • Vision Transformers (ViT)
  • Swin Transformer
  • CvT (Convolutional Vision Transformer)

5. Vision Language Models

These models fuse vision and text so systems can reason across images and language together.

  • Vision language models
  • CLIP (Contrastive Language-Image Pre-training)
  • ALIGN (A Large-scale ImaGe and Noisy-text)
  • BLIP (Bootstrapping Language-Image Pre-training)

Applications

1. Image Classification

Classification inspects an image and assigns a label or category that matches its content.

  • Image Classification
  • Using Support Vector Machine (SVM)
  • Using RandomForest
  • Using CNN
  • Using TensorFlow
  • Using PyTorch Lightning

Various types of Image Classification:

  • Multiclass classification
  • Multilabel classification
  • Zero-shot classification

2. Object Detection

Detection finds objects in an image and marks them with bounding boxes.

  • Object Detection
  • YOLO
  • SSD
  • Region-Based Convolutional Neural Networks (R-CNNs)
  • Fast R-CNN
  • Faster R-CNN
  • Mask R-CNN
  • Using TensorFlow
  • Using PyTorch

Various types of Object Detection:

  • Bounding Box Regression
  • Intersection over Union (IoU)
  • Region Proposal Networks (RPN)
  • Non-Maximum Suppression (NMS)

3. Image Segmentation

Segmentation divides an image into regions so objects and boundaries can be labeled at the pixel level.

  • Image Segmentation
  • Using K Means Clustering
  • Using UNet
  • Using TensorFlow
  • Using Mask R-CNN

Various types of image segmentation:

  • Semantic Segmentation
  • Instance Segmentation
  • Panoptic Segmentation

Related Articles:

  • Steps in Computer Vision
  • Computer Vision Libraries
  • Applications of Computer Vision