Provides detailed descriptions of the algorithms and architectures used in major computer image generation devices. Examines anti-aliasing, data-base design, and the generation of special effects. Critically reviews several VLSIC architectures for image generation. Describes current and possible future applications of this technology, including visual flight simulation for pilot training.

Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.

How computer graphics transformed the computer from a calculating machine into an interactive medium, as seen through the histories of five technical objects. Most of us think of computer graphics as a relatively recent invention, enabling the spectacular visual effects and lifelike simulations we see in current films, television shows, and digital games. In fact, computer graphics have been around as long as the modern computer itself, and played a fundamental role in the development of our contemporary culture of computing. In Image Objects, Jacob Gaboury offers a prehistory of computer graphics through an examination of five technical objects--an algorithm, an interface, an object standard, a programming paradigm, and a hardware platform--arguing that computer graphics transformed the computer from a calculating machine into an interactive medium. Gaboury explores early efforts to produce an algorithmic solution for the calculation of object visibility; considers the history of the computer screen and the random-access memory that first made interactive images possible; examines the standardization of graphical objects through the Utah teapot, the most famous graphical model in the history of the field; reviews the graphical origins of the object-oriented programming paradigm; and, finally, considers the development of the graphics processing unit as the catalyst that enabled an explosion in graphical computing at the end of the twentieth century. The development of computer graphics, Gaboury argues, signals a change not only in the way we make images but also in the way we mediate our world through the computer--and how we have come to reimagine that world as computational.

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch Key FeaturesUnderstand the different architectures for image generation, including autoencoders and GANsBuild models that can edit an image of your face, turn photos into paintings, and generate photorealistic imagesDiscover how you can build deep neural networks with advanced TensorFlow 2.x featuresBook Description The emerging field of Generative Adversarial Networks (GANs) has made it possible to generate indistinguishable images from existing datasets. With this hands-on book, you’ll not only develop image generation skills but also gain a solid understanding of the underlying principles. Starting with an introduction to the fundamentals of image generation using TensorFlow, this book covers Variational Autoencoders (VAEs) and GANs. You’ll discover how to build models for different applications as you get to grips with performing face swaps using deepfakes, neural style transfer, image-to-image translation, turning simple images into photorealistic images, and much more. You’ll also understand how and why to construct state-of-the-art deep neural networks using advanced techniques such as spectral normalization and self-attention layer before working with advanced models for face generation and editing. You'll also be introduced to photo restoration, text-to-image synthesis, video retargeting, and neural rendering. Throughout the book, you’ll learn to implement models from scratch in TensorFlow 2.x, including PixelCNN, VAE, DCGAN, WGAN, pix2pix, CycleGAN, StyleGAN, GauGAN, and BigGAN. By the end of this book, you'll be well versed in TensorFlow and be able to implement image generative technologies confidently. What you will learnTrain on face datasets and use them to explore latent spaces for editing new facesGet to grips with swapping faces with deepfakesPerform style transfer to convert a photo into a paintingBuild and train pix2pix, CycleGAN, and BicycleGAN for image-to-image translationUse iGAN to understand manifold interpolation and GauGAN to turn simple images into photorealistic imagesBecome well versed in attention generative models such as SAGAN and BigGANGenerate high-resolution photos with Progressive GAN and StyleGANWho this book is for The Hands-On Image Generation with TensorFlow book is for deep learning engineers, practitioners, and researchers who have basic knowledge of convolutional neural networks and want to learn various image generation techniques using TensorFlow 2.x. You’ll also find this book useful if you are an image processing professional or computer vision engineer looking to explore state-of-the-art architectures to improve and enhance images and videos. Knowledge of Python and TensorFlow will help you to get the best out of this book.

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key Features Train different kinds of deep learning model from scratch to solve specific problems in Computer Vision Combine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and more Includes tips on optimizing and improving the performance of your models under various constraints Book Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learn Set up an environment for deep learning with Python, TensorFlow, and Keras Define and train a model for image and video classification Use features from a pre-trained Convolutional Neural Network model for image retrieval Understand and implement object detection using the real-world Pedestrian Detection scenario Learn about various problems in image captioning and how to overcome them by training images and text together Implement similarity matching and train a model for face recognition Understand the concept of generative models and use them for image generation Deploy your deep learning models and optimize them for high performance Who this book is for This book is targeted at data scientists and Computer Vision practitioners who wish to apply the concepts of Deep Learning to overcome any problem related to Computer Vision. A basic knowledge of programming in Python—and some understanding of machine learning concepts—is required to get the best out of this book.

The Computer Image is a unique book and CD-ROM package which provides a comprehensive overview of three converging areas of the computer image - computer graphics, image processing and computer vision.

This volume presents the proceedings of the 7th International Confer ence of the Computer Graphics Society, CG International '89, held at the University of Leeds, UK, June 27-30, 1989. Since 1982 this confer ence has continued to attract high-quality research papers in all aspects of computer graphics and its applications. Originally the conference was held in Japan (1982-1987), but in 1988 was held in Geneva, Switzerland. Future conferences are planned for Singapore in 1990, USA in 1991, Japan in 1992, and Canada in 1993. Recent developments in computer graphics have concentrated on the following: greater sophistication of image generation techniques; advances in hardware and emphasis on the exploitation of parallelism, integration of robotics and AI techniques for animation, greater integ ration of CAD and CAM in CIM, use of powerful computer graphics techniques to represent complex physical processes (visualization), advances in computational geometry and in the representation and modelling of complex physical and mathematical objects, and improved tools and methods for HC!. These trends and advances are reflected in this present volume. A number of papers deal with important research aspects in many of these areas.