國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Building computer vision applications using artificial neural networks/ by Shamshad Ansari.
其他題名:	with examples in OpenCV and TensorFlow with Python /
作者:	Ansari, Shamshad.
出版者:	Berkeley, CA :Apress : : 2023.,
面頁冊數:	xxii, 526 p. :illustrations, digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Computer vision. -
電子資源:	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:

561800
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 /
LDR:02861nam a2200337 a 4500 001 1120015
003 DE-He213
005 20231117163116.0
006 m d
007 cr nn 008maaau
008 240612s2023 cau s 0 eng d
020 $a 9781484298664 $q (electronic bk.)
020 $a 9781484298657 $q (paper)
024 7 $a 10.1007/978-1-4842-9866-4 $2 doi
035 $a 978-1-4842-9866-4
040 $a GP $c GP
041 0 $a eng
050 4 $a TA1634
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.37 $2 23
090 $a TA1634 $b .A617 2023
100 1 $a Ansari, Shamshad. $e author. $3 1318830
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.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2023.
300 $a xxii, 526 p. : $b illustrations, digital ; $c 24 cm.
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.
650 0 $a Computer vision. $3 561800
650 0 $a Neural networks (Computer science) $3 528588
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Python. $3 1115944
650 2 4 $a Open Source. $3 1113081
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-9866-4
950 $a Professional and Applied Computing (SpringerNature-12059)