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Computer Vision: Principles, Algorithms, Applications, Learning

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and;

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Deep Learning in Computer Vision: Principles and Applications

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;

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Mastering OpenCV 4 with Python

Create computer vision and deep learning web applications Who this book is forThis book is designed for computer vision developers, engineers;

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Deep Learning in Computer Vision

Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions;

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Machine Learning for OpenCV 4

understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning;

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Emerging Trends in Image Processing, Computer Vision and Pattern Recognition

Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which;

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Embedded Vision

Embedded vision is the integration of computer vision into machines that use algorithms to decode meaning from observed images or;

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Computer Vision for the Web

Unleash the power of the Computer Vision algorithms in JavaScript to develop vision-enabled web content About This Book * Explore the;

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Cognitive Internet of Medical Things for Smart Healthcare

Internet of Medical Things. These applications geared towards IoMT cloud analysis, machine learning, computer vision and deep learning have enabled;

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Challenges and Applications for Implementing Machine Learning in Computer Vision

efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a;

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Challenges and Applications for Implementing Machine Learning in Computer Vision

efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a;

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Multimodal Scene Understanding

focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources;

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Computer Vision Projects with PyTorch

will be able to build your own computer vision projects using transfer learning and PyTorch. What You Will Learn Solve problems;

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Computer Vision

Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes;

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Learning OpenCV

Learning OpenCV puts you in the middle of the rapidly expanding field of computer vision. Written by the creators of the free open source OpenCV;

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Reconstruction-Free Compressive Vision for Surveillance Applications

creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision;

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Reconstruction-Free Compressive Vision for Surveillance Applications

creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision;

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Data Clustering and Image Segmentation Through Genetic Algorithms

As computers are being used more and more to solve complex problems, the application of biology or natural evolution principles to the;

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Practical Computer Vision Applications Using Deep Learning with CNNs

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the;

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Ellipse Fitting for Computer Vision

in computer vision applications. For this reason, the study of ellipse fitting began as soon as computers came into use for image analysis in the;

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Learn Computer Vision Using Opencv: With Deep Learning Cnns and Rnns

Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest;

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Practical Machine Learning and Image Processing

biggest computer vision library: OpenCV. You'll see the OpenCV algorithms and how to use them for image processing. The next section looks at;

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Practical Machine Learning and Image Processing

biggest computer vision library: OpenCV. Yoüll see the OpenCV algorithms and how to use them for image processing. The next section looks at;

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Structured Learning and Prediction in Computer Vision

Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These;

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Image Understanding Using Sparse Representations

paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications;

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Advances in Artificial Intelligence and Its Applications

processing applications; educational applications; biomedical applications; image processing and computer vision; search and optimization;

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Computer Vision for Visual Effects

Vision for Visual Effects will educate students, engineers and researchers about the fundamental computer vision principles and state-of-the-art;

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