Practical Computer Vision (eBook)

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.

With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset.

By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.

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A practical guide designed to get you from basics to current state of art in computer vision systems.About This BookMaster the different tasks associated with Computer Vision and develop your own Computer Vision applications with easeLeverage the power of Python, Tensorflow, Keras, and OpenCV to perform image processing, object detection, feature detection and moreWith real-world datasets and fully functional code, this book is your one-stop guide to understanding Computer VisionWho This Book Is ForThis book is for machine learning practitioners and deep learning enthusiasts who want to understand and implement various tasks associated with Computer Vision and image processing in the most practical manner possible. Some programming experience would be beneficial while knowing Python would be an added bonus.What You Will LearnLearn the basics of image manipulation with OpenCVImplement and visualize image filters such as smoothing, dilation, histogram equalization, and moreSet up various libraries and platforms, such as OpenCV, Keras, and Tensorflow, in order to start using computer vision, along with appropriate datasets for each chapter, such as MSCOCO, MOT, and Fashion-MNISTUnderstand image transformation and downsampling with practical implementations.Explore neural networks for computer vision and convolutional neural networks using KerasUnderstand working on deep-learning-based object detection such as Faster-R-CNN, SSD, and moreExplore deep-learning-based object tracking in actionUnderstand Visual SLAM techniques such as ORB-SLAMIn DetailIn this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset.By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.Style and approachStep-by-step guide filled with real-world, practical examples for understanding and applying various Computer Vision techniques