TY  - GEN
N2  - Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition⁰́b9s 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 you⁰́b9ll gain a thorough understanding of them. The book⁰́b9s 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, you⁰́b9ll 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.
DO  - 10.1007/978-1-4842-9866-4
DO  - doi
AB  - Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition⁰́b9s 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 you⁰́b9ll gain a thorough understanding of them. The book⁰́b9s 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, you⁰́b9ll 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.
T1  - Building computer vision applications using artificial neural networks :with examples in OpenCV and TensorFlow with Python /
AU  - Ansari, Shamshad,
ET  - Second edition.
CN  - TA1634
N1  - Includes index.
ID  - 1484355
KW  - Vision par ordinateur.
KW  - Réseaux neuronaux (Informatique)
KW  - OpenCV (Langage de programmation)
KW  - Logiciels d'application
KW  - Python (Langage de programmation)
KW  - Traitement d'images.
KW  - Apprentissage automatique.
KW  - Computer vision.
KW  - Neural networks (Computer science)
KW  - OpenCV (Computer program language)
KW  - Application software
KW  - Python (Computer program language)
KW  - Image processing.
KW  - Machine learning.
SN  - 9781484298664
SN  - 1484298667
TI  - Building computer vision applications using artificial neural networks :with examples in OpenCV and TensorFlow with Python /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-9866-4
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-9866-4
ER  -