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Computer vision is the technology that enables computers and machines to visually perceive and understand the world. Its applications are wide-ranging and have significant implications for technology and society.
In manufacturing, machine vision is used for quality control, identifying defects in products, and ensuring accuracy in assembly processes. In healthcare, it aids in medical imaging, diagnosis, and surgical assistance. In robotics, machine vision enables machines to navigate and interact with their environment. In transportation, it contributes to autonomous vehicles, traffic monitoring, and license plate recognition. In security, it assists in surveillance, facial recognition, and object detection. Machine vision also finds applications in agriculture, retail, entertainment, and more.
Overall, machine vision improves efficiency, accuracy, and safety, transforming industries and enhancing our daily lives.
Dr. Caide Xiao was born in China in and obtained his bachelor's degree in physics from Centre China Normal University in 1979 and was a lecturer of medical physics in Yunyang University. Following his PhD in optical biosensors from Tsinghua University he has subsequently been a research fellow and visiting scholar at the Biotechnology Research Institute in Montreal, Oakland University in Rochester, West Virginia University and the University of Calgary.
Preface
Acknowledgements
Author biography
1 Mathematical tools for computer vision
1.1 Probability, entropy and Kullback-Leibler divergence
1.1.1 Probability and Shannon entropy
1.1.2 Kullback-Leibler divergence and cross entropy
1.1.3 Conditional probability and joint entropies
1.1.4 Jensen's inequality
1.1.5 Maximum likelihood estimation and over fitting
1.1.6 Application of expectation-maximization algorithm to find a PDF
1.2 Using a gradient descent algorithm for linear regression
1.3 Automatic gradient calculations and learning rate schedulers
1.4 Dataset, dataloader, GPU and models saving
1.5 Activation functions for nonlinear regressions
References
2 Image classifications by convolutional neural networks
2.1 Classification of hand written digits in the MNIST database
2.2 Mathematical operations of a convolution
2.3 Using ResNet9 for CIFAR-10 classification
2.4 Transfer learning with ResNet for STL-10 dataset
3 Image generation by GANs
3.1 The GAN theory
3.1.1 Implement a GAN for quadratic curve generation
3.1.2 Using a GAN with two fully connected layers to generate MINST Images
3.2 Applications of deep convolutional GANs
3.2.1 Mathematical operations of ConvTranspose2D
3.2.2 Applications of a DCGAN for MNIST and fashion MNIST
3.2.3 Using a DCGAN to generate fake anime-faces and fake CelebA images
3.3 Conditional deep convolutional GANs
3.3.1 Applications of a cDCGAN to MNIST and fashion MNIST datasets
3.3.2 Applications of a cDCGAN to generate fake Rock Paper Scissors images
4 Image generation by WGANs with gradient penalty
4.1 Using a WGAN or a WGAN-GP for generation of fake quadratic curves
4.2 Using a WGAN-GP for Fashion MNIST
4.3 WGAN-GP for CelebA dataset and Anime Face dataset
4.4 Implement of a cWGAN-GP for Rock Paper Scissors dataset
5 Image generation by VAEs
5.1 VAE and beta-VAE
5.2 Application of beta-VAE for fake quadratic curves
5.3 Application of beta-VAE for the MNIST dataset
5.4 Using VAE-GAN for fake images of MNIST and Fashion MNIST
6 Image generation by infoGANs
6.1 Using infoGAN to generate quadratic curves
6.2 Implementation of infoGAN for the MNIST dataset
6.3 infoGAN for fake Anime-face dataset images
6.4 Implementation of infoGAN to the rock paper scissors dataset
Reference
7 Object detection by YOLOv1/YOLOv3 models
7.1 Bounding boxes of Pascal VOC database for YOLOv1
7.2 Encode VOC images with bounding boxes for YOLOv1
7.2.1 VOC image augmentations with bounding boxes
7.2.2 Encoding bounding boxes to grid cells for YOLOv1 model training
7.2.3 Chess pieces dataset from Roboflow
7.3 ResNet18 model, IOU and a loss function
7.3.1 Using ResNet18 to replace YOLOv1 model
7.3.2 Intersection over union (IOU)
7.3.3 Loss function
7.4 Utility functions for model training
7.5 Applications of YOLOv3 for real-time object detection
8 YOLOv7 and YOLOv8 models
8.1 YOLOv7 for object detection for a custom dataset: MNIST4yolo
8.2 YOLOv7 for instance segmentation
8.3 Using YOLOv7 for human pose estimation (key point detection)
8.4 Applications of YOLOv8 models
8.4.1 Image object detection, segmentation, classification and pose estimation
8.4.2 Object counting on an image or a video frame
8.4.3 Car tracking and counting for a video file
8.4.4 Fine tuning YOLOv8 for objection detection and annotation of a custom dataset
8.5 Using YOLO-NAS models for object detection
9 U-Nets for image segmentation and diffusion models for image generation
9.1 Retinal vessel segmentation by a U-Net for DRIVE dataset
9.2 Using an attention U-Net diffusion model for quadratic curve generation
9.2.1 The forward process in a DDPM
9.2.2 The backward process in the DDPM
9.3 Using a pre-trained U-Net from Hugging Face to generate images
9.4 Generate photorealistic images from text prompts by stable diffusion
10 Applications of vision transformers
10.1 The architecture of a basic ViT model
10.2 Hugging Face ViT for CIFAR10 image classification
10.3 Zero shot image classification by OpenAI CLIP
10.4 Zero shot object detection by Hugging Face's OWL-ViT
11 Knowledge distillation, DINO, SAM, MiDaS and NeRF
11.1 Knowledge distillation for neural network compression
11.2 DINO: emerging properties in self-supervised vision transformers
11.3 DINOv2 for image retrieval, classification and feature visualization
11.4 Segment anything model: SAM and FastSAM
11.5 MiDaS for image depth estimation
11.6 Neural radiance fields for synthesis of 3D scenes
11.6.1 Camera intrinsic and extrinsic matrices
11.6.2 Using MLP with Gaussian Fourier feature mapping to reconstruct images
11.6.3 The physics principle of render volume density in NeRF
Appendix
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