Computer Vision Tutorial

Computer Vision is a branch of Artificial Intelligence (AI) that enables computers to interpret and extract information from images and videos, similar to human perception. It involves developing algorithms to process visual data and derive meaningful insights.

Why Learn Computer Vision?

1. High Demand in the Job Market: Essential for careers in AI, machine learning, and data science across industries like healthcare, automotive, and robotics.
2. Revolutionizing Industries: Powers advancements in self-driving cars, medical diagnostics, agriculture, and manufacturing by automating visual tasks.
3. Solving Real-World Problems: Enhances public safety, improves medical imaging, and optimizes industrial processes.

This Computer Vision tutorial is designed for both beginners and experienced professionals, covering key concepts of computer vision, including Image Processing, Feature Extraction, Object Detection and Recognition, and Image Segmentation.

Before diving into computer vision, it is recommended to have a foundational understanding of:

1. Machine Learning
2. Deep Learning
3. OpenCV

These resources will help you build the necessary background for understanding and implementing computer vision techniques effectively

Mathematical Prerequisites for Computer Vision

1. Linear Algebra

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

2. Probability and Statistics

• Probability Distributions
• Bayesian Inference and Bayes' Theorem
• Markov Chains
• Kalman Filters

3. Signal Processing

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

Image Processing

Image processing refers to a set of techniques for manipulating and analyzing digital images. The techniques include:

1. Image Transformation is process of modifying or changing an images.

• Geometric Transformations
• Fourier Transform
• Intensity Transformation

2. Image Enhancement improve the visual quality or clarity of image to highlight important features or details to minimize noise or distortions.

• Histogram Equalization
• Contrast Enhancement
• Image Sharpening
• Color Correction

3. Noise Reduction Techniques removes unwanted noise from images while preserving important features like edges and texture.

• Gaussian Smoothing
• Median Filtering
• Bilateral Filtering
• Wavelet Denoising

4. Morphological Operations process images based on their structure and shape. Common morphological operations include:

• Erosion and Dilation
• Opening
• Closing
• Morphological Gradient

Feature Extraction

1. Edge Detection Techniques identify significant changes in the intensity or color, that corresponds to the boundaries of objects with an image.

• Canny Edge Detector
• Sobel Operator
• Prewitt Operator
• Laplacian of Gaussian (LoG)

2. Corner and Interest Point Detection identify points in an image that are distinctive and can be detected across different views, transformations or scales.

• Harris Corner Detection
• Shi-Tomasi Corner Detector

3. Feature Descriptors generates a compact representation of local image region around keypoints making it easier to correspond features across different images.

• SIFT (Scale-Invariant Feature Transform)
• SURF (Speeded-Up Robust Features)
• ORB (Oriented FAST and Rotated BRIEF)
• HOG (Histogram of Oriented Gradients)

Deep Learning for Computer Vision

Deep learning has revolutionized the field of computer vision by enabling machines to understand and interpret visual data in ways that were previously unimaginable.

1. Convolutional Neural Networks (CNNs)

Convolutional Neural Networks are designed to learn spatial hierarchies of features from image. Key components include:

• Convolutional Layers
• Pooling Layers
• Fully Connected Layers

2. Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) consists of two networks (generator and discriminator) that work against each other to create realistic images. There are various types of GANs, each designed for specific tasks and improvements:

• Deep Convolutional GAN (DCGAN)
• Conditional GAN (cGAN)
• Cycle-Consistent GAN (CycleGAN)
• Super-Resolution GAN (SRGAN)
• Wasserstein GAN (WGAN)
• StyleGAN

3. Variational Autoencoders (VAEs)

Variational Autoencoders (VAEs) are probabilistic version of autoencoders, which forces the model to learn a distribution over the latent space rather than a fixed point. Other autoencoders used in computer vision are:

• Vanilla Autoencoders
• Denoising Autoencoders (DAE)
• Convolutional Autoencoder (CAE)

4. Vision Transformers (ViT)

Vision Transformers (ViT) are inspired by transformers models to treat images and sequence of patches and process them using self-attention mechanisms. Common vision transformers include:

• DeiT (Data-efficient Image Transformer)
• Swin Transformer
• CvT (Convolutional Vision Transformer)
• T2T-ViT (Tokens-to-Token Vision Transformer)

5. Vision Language Models

Vision language models integrate visual and textual information to perform image processing and natural language understanding.

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

Computer Vision Tasks

1. Image Classification assigns a label or category to an entire image based on its content.

• Multiclass classification classifies an image into multiple predefined classes.
• Multilabel classification involves assigning multiple labels to a single image.
• Zero-shot classification classifies images into categories that model has never seen during training.

You can perform image classification using following methods.

• Image Classification using Support Vector Machine (SVM)
• Image Classification using RandomForest
• Image Classification using CNN
• Image Classification using TensorFlow
• Image Classification using PyTorch Lightning
• Image Classification using InceptionResNetV2

To learn about the datasets for image classification, you can go through the article on Dataset for Image Classification.

2. Object Detection involves identifying and locating objects within an image by drawing bounding boxes around them. Object detection include following concepts:

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

Type of Object Detection Approaches

1. Single-Stage Object Detection

• YOLO (You Only Look Once)
• SSD (Single Shot Multibox Detector)

2. Two-Stage Object Detection

• Region-Based Convolutional Neural Networks (R-CNNs)
• Fast R-CNN
• Faster R-CNN
• Mask R-CNN

You can perform object detection using the following methods:

• Object Detection using TensorFlow
• Object Detection using PyTorch

3. Image Segmentation involves partitioning an image into distinct regions or segments to identify objects or boundaries at a pixel level. Types of image segmentation are:

• Semantic Segmentation
• Instance Segmentation
• Panoptic Segmentation

You can perform image segmentation using the following methods:

• Image Segmentation using K Means Clustering
• Image Segmentation using UNet
• Image Segmentation using UNet++
• Image Segmentation using TensorFlow
• Image Segmentation with Mask R-CNN

To learn more related to this, you can refer to: Computer Vision Tasks

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

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.