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Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV Abhinav Dadhich

(ebook) (audiobook) (audiobook) Książka w języku 1
Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV Abhinav Dadhich - okladka książki

Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV Abhinav Dadhich - okladka książki

Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV Abhinav Dadhich - audiobook MP3

Practical Computer Vision. Extract insightful information from images using TensorFlow, Keras, and OpenCV Abhinav Dadhich - audiobook CD

Autor:
Abhinav Dadhich
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
234
Dostępne formaty:
     PDF
     ePub
     Mobi
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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O autorze książki

Abhinav Dadhich is a Researcher and Application Developer on deep learning at Abeja Inc. Tokyo. His day is often filled with designing deep learning models for computer vision applications like image classification, object detection, segmentation etc. His passion lies in understanding and replicating human vision system. Previously, he has worked on 3D mapping and robot navigation. He has graduated with B.Tech. in EE from IIT Jodhpur, India and has done his M.Eng. in Information Science from NAIST, Japan. He puts up notes and codes for several topics on GitHub profile.

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