Image Recognition And Classification
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Deep Learning For Computer Vision
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2019-04-04
Deep Learning For Computer Vision written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-04 with Computers categories.
Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Practical Machine Learning And Image Processing
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Author : Himanshu Singh
language : en
Publisher: Apress
Release Date : 2019-02-26
Practical Machine Learning And Image Processing written by Himanshu Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-26 with Computers categories.
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 conceptsin 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. What You Will Learn 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 Who This Book Is For Data scientists and software developers interested in image processing and computer vision.
Machine Learning In Image Analysis And Pattern Recognition
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Author : Munish Kumar
language : en
Publisher:
Release Date : 2021
Machine Learning In Image Analysis And Pattern Recognition written by Munish Kumar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.
Pattern Recognition And Classification
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Author : Geoff Dougherty
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-28
Pattern Recognition And Classification written by Geoff Dougherty and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-28 with Computers categories.
The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.
Convolutional Neural Networks With Swift For Tensorflow
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Author : Brett Koonce
language : en
Publisher:
Release Date : 2021
Convolutional Neural Networks With Swift For Tensorflow written by Brett Koonce and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Dive into and apply practical machine learning and dataset categorization techniques while learning Tensorflow and deep learning. This book uses convolutional neural networks to do image recognition all in the familiar and easy to work with Swift language. It begins with a basic machine learning overview and then ramps up to neural networks and convolutions and how they work. Using Swift and Tensorflow, you'll perform data augmentation, build and train large networks, and build networks for mobile devices. You'll also cover cloud training and the network you build can categorize greyscale data, such as mnist, to large scale modern approaches that can categorize large datasets, such as imagenet. Convolutional Neural Networks with Swift for Tensorflow uses a simple approach that adds progressive layers of complexity until you have arrived at the current state of the art for this field. You will: Categorize and augment datasets Build and train large networks, including via cloud solutions Deploy complex systems to mobile devices.
Deep Learning For Hyperspectral Image Analysis And Classification
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Author : Linmi Tao
language : en
Publisher: Springer Nature
Release Date : 2021-02-20
Deep Learning For Hyperspectral Image Analysis And Classification written by Linmi Tao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-20 with Computers categories.
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
Practical Deep Learning For Cloud Mobile And Edge
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Author : Anirudh Koul
language : en
Publisher: O'Reilly Media
Release Date : 2019-10-14
Practical Deep Learning For Cloud Mobile And Edge written by Anirudh Koul and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Image Classification
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Author : Mark Magic
language : zh-CN
Publisher: Independently Published
Release Date : 2019-04-18
Image Classification written by Mark Magic and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-18 with categories.
This book implemented six different algorithms to classify images with the prediction accuracy of the testing data as the primary criterion (the higher the better) and the time consumption as the secondary one (the shorter the better). The accuracies varied between about 30% and 90%, while the time consumptions varied from several seconds to more than one hour. Considering both of the criteria, the Pre-Trained AlexNet Features Representation plus a Classifier, such as the k-Nearest Neighbors (KNN) and the Support Vector Machines (SVM), was concluded as the best algorithm.The six algorithms are: Tiny Images Representation + Classifiers; HOG (Histogram of Oriented Gradients) Features Representation + Classifiers; Bag of SIFT (Scale Invariant Feature Transform) Features Representation + Classifiers; Training a CNN (Convolutional Neural Network) from scratch; Fine Tuning a Pre-Trained Deep Network (AlexNet); and Pre-Trained Deep Network (AlexNet) Features Representation + Classifiers.The codes were written with Python in Jupyter Notebook, and they could be executed on both CPUs and GPUs.本书使用了六种不同的算法来对图像进行分类。其中测试数据的预测准确度为主要标准（越高越好），所花费的时间为次要标准（越短越好）。预测准确度大约在30％和90％之间变化，而所花费的时间从几秒钟到一个多小时不等。同时考虑这两个标准，预训练的 AlexNet 特征表示加上分类器，例如k个最近邻（KNN）和支持向量机（SVM），被认为是最佳的算法。这六种算法分别是：微小图像表示+分类器；方向梯度直方图（HOG）特征表示+分类器；尺度不变特征变换（SIFT）口袋特征表示+分类器；从头训练卷积神经网络（CNN）；微调预训练的深度网络（AlexNet）；以及预训练的深度网络（AlexNet）特征表示+分类器。这些代码全部用 Python 编写，并在 Jupyter Notebook 中运行。这些代码都可以运行在 CPU 和 GPU 上。
Pattern Recognition And Image Processing
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Author : D Luo
language : en
Publisher: Elsevier
Release Date : 1998-09-01
Pattern Recognition And Image Processing written by D Luo and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-09-01 with Computers categories.
This book delivers a course module for advanced undergraduates, postgraduates and researchers of electronics, computing science, medical imaging, or wherever the study of identification and classification of objects by electronics-driven image processing and pattern recognition is relevant. Object analysis first uses image processing to detect objects and extract their features, then identifies and classifies them by pattern recognition. Its manifold applications include recognition of objects in satellite images which enable discrimination between different objects, such as fishing boats, merchant ships or warships; machine spare parts e.g. screws, nuts etc. (engineering); detection of cancers, ulcers, tumours and so on (medicine); and recognition of soil particles of different types (agriculture or soil mechanics in civil engineering). - Outlines the identification and classification of objects by electronics-driven image processing and pattern recognition - Discusses object detection, shape, roundness and sharpness analysis, orientation analysis and arrangement analysis - Delivers a course module for advanced undergraduates, postgraduates and researchers of electronics, computing science and medical imaging
Techniques For Image Processing And Classifications In Remote Sensing
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Author : Robert A. Schowengerdt
language : en
Publisher: Academic Press
Release Date : 1983
Techniques For Image Processing And Classifications In Remote Sensing written by Robert A. Schowengerdt and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Computers categories.
Digital image processing. Digital image classification. Remote sensing and image processing bibliography. Digital image data formats. The table look-up algorithm and interactive image processing. Examination quetions.