Mastering Computer Vision with TensorFlow 2.x

Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language

Key Features

• Gain a fundamental understanding of advanced computer vision and neural network models in use today
• Cover tasks such as low-level vision, image classification, and object detection
• Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit

Book Description

Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.

What you will learn

• Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model
• Use TensorFlow for various visual search methods for real-world scenarios
• Build neural networks or adjust parameters to optimize the performance of models
• Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting
• Evaluate your model and optimize and integrate it into your application to operate at scale
• Get up to speed with techniques for performing manual and automated image annotation

Who this book is for

This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

應用神經網絡架構，使用Python編程語言構建最先進的計算機視覺應用程式

主要特點：

- 獲得對當今先進的計算機視覺和神經網絡模型的基本理解
- 涵蓋低級視覺、圖像分類和物體檢測等任務
- 在雲平台上開發深度學習模型，並使用TensorFlow Lite和OpenVINO工具包進行優化

書籍描述：

計算機視覺使機器能夠以人類水平的理解力來視覺化、處理和分析圖像和視頻。本書專注於使用TensorFlow幫助您學習高級計算機視覺任務，如圖像獲取、處理和分析。您將從計算機視覺和深度學習的關鍵原則入手，建立堅實的基礎，然後探索神經網絡架構，了解其工作原理，而不僅僅將其視為黑盒子。接下來，您將探索VGG、ResNet、Inception、R-CNN、SSD、YOLO和MobileNet等架構。隨著進一步的學習，您將學習使用轉移學習進行視覺搜索方法。您還將涵蓋高級計算機視覺概念，如語義分割、使用GAN進行圖像修復、物體跟踪、視頻分割和動作識別。之後，本書專注於如何使用機器學習和深度學習概念執行邊緣檢測和人臉識別等任務。然後，您將了解如何在個人電腦和各種雲平台上開發強大的神經網絡模型。最後，您將學習執行模型優化方法，以在邊緣設備上進行實時推理。通過閱讀本書，您將對計算機視覺有深入的理解，並能夠自信地開發模型以自動化任務。

您將學到什麼：

- 探索特徵提取和圖像檢索方法，並可視化神經網絡模型的不同層次
- 使用TensorFlow進行各種實際場景的視覺搜索方法
- 構建神經網絡或調整參數以優化模型的性能
- 了解TensorFlow DeepLab用於對圖像進行語義分割和DCGAN用於圖像修復
- 評估您的模型，並將其優化並集成到應用程式中以實現規模化操作
- 熟悉手動和自動圖像標註技術

本書適合對計算機視覺專業人士、圖像處理專業人士、機器學習工程師和人工智能開發人員具有一定機器學習和深度學習知識的讀者，開始閱讀本書需要熟悉TensorFlow和Python知識。

Krishnendu (Krish) is passionate about research on computer vision and solving AI problems to make our life simpler. His core expertise is deep learning - computer vision, IoT, and agile software development. Krish is also a passionate app developer and has a dash cam-based object and lane detection and turn by turn navigation and fitness app in the iOS app store - Nity Map AI Camera & Run timer.

Krishnendu（Krish）熱衷於研究計算機視覺並解決人工智慧問題，以使我們的生活更加簡單。他的核心專長是深度學習 - 計算機視覺、物聯網和敏捷軟體開發。Krish也是一位熱情的應用程式開發者，在iOS應用商店中有一款基於行車記錄儀的物體和車道偵測、逐步導航和健身應用程式 - Nity Map AI Camera & Run timer。

1. Computer Vision and Tensorflow Fundamentals
2. Content Recognition using Local Binary Pattern
3. Face Recognition and Tracking using Viola Jones Algorithm & OpenCV
4. Deep learning on images
5. Neural Network Architecture & Models
6. Visual Search using Transfer Learning
7. Object Detection using YOLO
8. Semantic Segmentation and Neural Style Transfer
9. Action Recognition using Multitask Deep Learning
10. Object Classification and Detection using RCNN
11. Deep Learning on Edge Devices with GPU/CPU Optimization
12. Cloud Computing Platform for Computer Vision

- Computer Vision and Tensorflow Fundamentals (計算機視覺和Tensorflow基礎)
- Content Recognition using Local Binary Pattern (使用局部二值模式進行內容識別)
- Face Recognition and Tracking using Viola Jones Algorithm & OpenCV (使用Viola Jones算法和OpenCV進行人臉識別和追蹤)
- Deep learning on images (圖像上的深度學習)
- Neural Network Architecture & Models (神經網絡架構和模型)
- Visual Search using Transfer Learning (使用遷移學習進行視覺搜索)
- Object Detection using YOLO (使用YOLO進行物體檢測)
- Semantic Segmentation and Neural Style Transfer (語義分割和神經風格轉換)
- Action Recognition using Multitask Deep Learning (使用多任務深度學習進行動作識別)
- Object Classification and Detection using RCNN (使用RCNN進行物體分類和檢測)
- Deep Learning on Edge Devices with GPU/CPU Optimization (在邊緣設備上進行GPU/CPU優化的深度學習)
- Cloud Computing Platform for Computer Vision (用於計算機視覺的雲計算平台)