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Practical machine learning and image processing : for facial recognition, object detection, and pattern recognition using Python /
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms dis...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley, California] :
Apress,
[2019]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Singh, Himanshu, |e author. | |
| 245 | 1 | 0 | |a Practical machine learning and image processing : |b for facial recognition, object detection, and pattern recognition using Python / |c Himanshu Singh. |
| 264 | 1 | |a [Berkeley, California] : |b Apress, |c [2019] | |
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| 505 | 0 | |a Setup environment -- Introduction to image processing -- Basics of Python and SciKit Image -- Advanced image processing using OpenCV -- Image processing using machine learning -- Real-time use cases -- Appendix: Important concepts and terminology. | |
| 520 | |a Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You?ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You?ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you?ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. You will: Discover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects. | ||
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| 650 | 6 | |a Traitement d'images |x Techniques numériques. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Traitement optique de l'information. | |
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