Computer Vision is a field of artificial intelligence that enables machines to interpret and understand visual information from images and videos. It uses image processing techniques and deep learning models to detect objects, recognize patterns and extract meaningful insights from visual data.
Basics
This section introduces how machines analyze and understand images and videos using techniques like image processing and deep learning models.
Mathematical Prerequisites
Before moving into Computer Vision, having a foundational understanding of certain mathematical concepts will help us which includes:
1. Linear Algebra
2. Signal Processing
- Signal Processing
- Image Filtering and Convolution
- Discrete Fourier Transform (DFT)
- Fast Fourier Transform (FFT)
- Principal Component Analysis (PCA)
Key Concepts
It refers to techniques for manipulating and analyzing digital images. Common image processing tasks include:
1. Image Transformation
2. Image Enhancement
3. Noise Reduction Techniques
4. Morphological Operations
2. Feature Extraction
It involves identifying distinctive elements within an image for analysis and its techniques include:
1. Edge Detection Techniques
- Computer Vision Algorithms
- Edge Detection Techniques
- Canny Edge Detector
- Sobel Operator
- Laplacian of Gaussian (LoG)
2. Corner and Interest Point Detection
3. 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
To implement computer vision tasks effectively, various libraries are used:
Deep Learning
Deep learning has enhanced computer vision by allowing machines to understand and analyze visual data.
1. Convolutional Neural Networks (CNNs)
Convolutional Neural Networks are designed for learning spatial hierarchies of features from images.
2. Generative Adversarial Networks (GANs)
It consists of two networks that work against each other to create realistic 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)
They are a probabilistic form of autoencoders that learn a distribution over the latent space instead of mapping inputs to a fixed point.
- Autoencoders
- Variational Autoencoders (VAEs)
- Denoising Autoencoders (DAE)
- Convolutional Autoencoder (CAE)
4. Vision Transformers (ViT)
They are inspired by transformer models and process images as sequences of patches using self-attention mechanisms.
5. Vision Language Models
They integrate visual and textual information to perform image processing and natural language understanding.
- 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
It involves analyzing an image and assigning it a specific label or category based on its content.
- Image Classification
- Using Support Vector Machine (SVM)
- Using RandomForest
- Using CNN
- Using TensorFlow
- Using PyTorch Lightning
Various types of Image Classification:
2. Object Detection
It involves identifying and locating objects within an image by drawing bounding boxes around them.
- 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
It involves partitioning an image into distinct regions or segments to identify objects or boundaries at a pixel level.
Various types of image segmentation: