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Building computer vision application...

Ansari, Shamshad.

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Building computer vision applications using artificial neural networks = with examples in OpenCV and TensorFlow with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Building computer vision applications using artificial neural networks/ by Shamshad Ansari.
Reminder of title:	with examples in OpenCV and TensorFlow with Python /
Author:	Ansari, Shamshad.
Published:	Berkeley, CA :Apress : : 2023.,
Description:	xxii, 526 p. :illustrations, digital ;24 cm.
[NT 15003449]:	Chapter 1: Prerequisite and Software Installation -- Chapter 2: Core Concepts of Image and Video Processing -- Chapter 3: Techniques of Image Processing -- Chapter 4: Building Artificial Intelligence System For Computer Vision -- Chapter 5: Deep Learning or Artificial Neural Network -- Chapter 6: Deep Learning in Object Detection -- Chapter 7: Practical Example 1- Object Tracking in Videos -- Chapter 8: Practical Example 2- Face Recognition -- Chapter 9: Industrial Application - Realtime Defect Detection in Industrial -- Chapter 10: Computer Vision Modeling on the Cloud.
Contained By:	Springer Nature eBook
Subject:	Computer vision. -
Online resource:	https://doi.org/10.1007/978-1-4842-9866-4
ISBN:	9781484298664

Building computer vision applications using artificial neural networks = with examples in OpenCV and TensorFlow with Python /
Ansari, Shamshad.

Building computer vision applications using artificial neural networkswith examples in OpenCV and TensorFlow with Python /[electronic resource] :by Shamshad Ansari. - Second edition. - Berkeley, CA :Apress :2023. - xxii, 526 p. :illustrations, digital ;24 cm.

Chapter 1: Prerequisite and Software Installation -- Chapter 2: Core Concepts of Image and Video Processing -- Chapter 3: Techniques of Image Processing -- Chapter 4: Building Artificial Intelligence System For Computer Vision -- Chapter 5: Deep Learning or Artificial Neural Network -- Chapter 6: Deep Learning in Object Detection -- Chapter 7: Practical Example 1- Object Tracking in Videos -- Chapter 8: Practical Example 2- Face Recognition -- Chapter 9: Industrial Application - Realtime Defect Detection in Industrial -- Chapter 10: Computer Vision Modeling on the Cloud.

Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated. This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networks You will: Understand image processing, manipulation techniques, and feature extraction methods Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO Utilize large scale model development and cloud infrastructure deployment Gain an overview of FaceNet neural network architecture and develop a facial recognition system.

ISBN: 9781484298664

Standard No.: 10.1007/978-1-4842-9866-4doiSubjects--Topical Terms:

540671
Computer vision.

LC Class. No.: TA1634

Dewey Class. No.: 006.37

Building computer vision applications using artificial neural networks = with examples in OpenCV and TensorFlow with Python /
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245 1 0 $a Building computer vision applications using artificial neural networks $h [electronic resource] : $b with examples in OpenCV and TensorFlow with Python / $c by Shamshad Ansari.
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505 0 $a Chapter 1: Prerequisite and Software Installation -- Chapter 2: Core Concepts of Image and Video Processing -- Chapter 3: Techniques of Image Processing -- Chapter 4: Building Artificial Intelligence System For Computer Vision -- Chapter 5: Deep Learning or Artificial Neural Network -- Chapter 6: Deep Learning in Object Detection -- Chapter 7: Practical Example 1- Object Tracking in Videos -- Chapter 8: Practical Example 2- Face Recognition -- Chapter 9: Industrial Application - Realtime Defect Detection in Industrial -- Chapter 10: Computer Vision Modeling on the Cloud.
520 $a Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated. This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python. Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networks You will: Understand image processing, manipulation techniques, and feature extraction methods Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO Utilize large scale model development and cloud infrastructure deployment Gain an overview of FaceNet neural network architecture and develop a facial recognition system.
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