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4 books on Computer Vision [PDF]
October 24, 2024 | 24 |
Books on computer vision describe the principles, algorithms and technologies used for image and video analysis, object recognition and image generation.
1. Computer Vision: Algorithms and Applications
2023 by Richard Szeliski
From this book, I found out that real-world applications of computer vision range from simple image enhancements to complex tasks like 3D reconstruction. CV techniques are applied in medical imaging and consumer-level tasks like image stitching. Engineering methodologies are integral in solving fundamental computer vision challenges. For example statistical models play a crucial role in solving vision problems and physical models of the imaging process can be used to describe scenes.
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2. Practical Machine Learning for Computer Vision
2021 by Valliappa Lakshmanan, Martin Görner, Ryan Gillard
This book shows how machine learning models can be used for tasks like object detection and image captioning. It features TensorFlow and Keras as key tools for building and deploying machine learning models in computer vision. It also underlines that data preprocessing and model evaluation are vital steps in crafting effective CV solutions and interpretability is an important aspect of computer vision models for ensuring their practical use.
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3. Computer Vision Metrics: Survey, Taxonomy, and Analysis
2014 by Scott Krig
This author provides OpenCV library description and practical resources for applying computer vision technologies in real tasks. It covers over 100 methods for feature description in CV. The taxonomy of these features includes local, regional and global categories. Fine-tuning the feature-descriptors helps achieve specific goals like robustness and invariance. You'll understand why accuracy, efficiency and distance metrics are important in creating computer vision algorithms.
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4. Computer Vision: Models, Learning, and Inference
2012 by Simon J. D. Prince
From this book you'll know that probabilistic models are central to learning and inference in computer vision, that training data helps infer relationships between images and their underlying structures. Face recognition and 3D structure extraction can be achieved by learning from image data. Modern techniques like graph cuts and multiple view geometry are key in solving camera calibration and object tracking problems. In this book over 70 algorithms are described in detail, covering a wide range of computer vision challenges.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Computer Vision: Algorithms and Applications
2023 by Richard Szeliski
From this book, I found out that real-world applications of computer vision range from simple image enhancements to complex tasks like 3D reconstruction. CV techniques are applied in medical imaging and consumer-level tasks like image stitching. Engineering methodologies are integral in solving fundamental computer vision challenges. For example statistical models play a crucial role in solving vision problems and physical models of the imaging process can be used to describe scenes.
Download PDF
2. Practical Machine Learning for Computer Vision
2021 by Valliappa Lakshmanan, Martin Görner, Ryan Gillard
This book shows how machine learning models can be used for tasks like object detection and image captioning. It features TensorFlow and Keras as key tools for building and deploying machine learning models in computer vision. It also underlines that data preprocessing and model evaluation are vital steps in crafting effective CV solutions and interpretability is an important aspect of computer vision models for ensuring their practical use.
Download PDF
3. Computer Vision Metrics: Survey, Taxonomy, and Analysis
2014 by Scott Krig
This author provides OpenCV library description and practical resources for applying computer vision technologies in real tasks. It covers over 100 methods for feature description in CV. The taxonomy of these features includes local, regional and global categories. Fine-tuning the feature-descriptors helps achieve specific goals like robustness and invariance. You'll understand why accuracy, efficiency and distance metrics are important in creating computer vision algorithms.
Download PDF
4. Computer Vision: Models, Learning, and Inference
2012 by Simon J. D. Prince
From this book you'll know that probabilistic models are central to learning and inference in computer vision, that training data helps infer relationships between images and their underlying structures. Face recognition and 3D structure extraction can be achieved by learning from image data. Modern techniques like graph cuts and multiple view geometry are key in solving camera calibration and object tracking problems. In this book over 70 algorithms are described in detail, covering a wide range of computer vision challenges.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
