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1. Object Detection and Recognition in Digital Images: Theory and Practice
Description
Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields.Key features:
- Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.
- Places an emphasis on tensor and statistical based approaches within object detection and recognition.
- Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods.
- Contains numerous case study examples of mainly automotive applications.
- Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform.
2. Programming Computer Vision with Python: Tools and algorithms for analyzing images
Feature
O Reilly MediaDescription
If you want a basic understanding of computer visions underlying theory and algorithms, this hands-on introduction is the ideal place to start. Youll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, and other computer vision applications as you follow clear examples written in Python.
Programming Computer Vision with Python explains computer vision in broad terms that wont bog you down in theory. You get complete code samples with explanations on how to reproduce and build upon each example, along with exercises to help you apply what youve learned. This book is ideal for students, researchers, and enthusiasts with basic programming and standard mathematical skills.
- Learn techniques used in robot navigation, medical image analysis, and other computer vision applications
- Work with image mappings and transforms, such as texture warping and panorama creation
- Compute 3D reconstructions from several images of the same scene
- Organize images based on similarity or content, using clustering methods
- Build efficient image retrieval techniques to search for images based on visual content
- Use algorithms to classify image content and recognize objects
- Access the popular OpenCV library through a Python interface
3. Image Processing and Acquisition using Python (Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series)
Description
Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processingone of the first books to integrate these topics together. By improving readers knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples.
A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The last part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry.
4. Machine Learning for OpenCV: Intelligent image processing with Python
Description
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide.
About This Book
- Load, store, edit, and visualize data using OpenCV and Python
- Grasp the fundamental concepts of classification, regression, and clustering
- Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide
- Evaluate, compare, and choose the right algorithm for any task
Who This Book Is For
This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.
What You Will Learn
- Explore and make effective use of OpenCV's machine learning module
- Learn deep learning for computer vision with Python
- Master linear regression and regularization techniques
- Classify objects such as flower species, handwritten digits, and pedestrians
- Explore the effective use of support vector machines, boosted decision trees, and random forests
- Get acquainted with neural networks and Deep Learning to address real-world problems
- Discover hidden structures in your data using k-means clustering
- Get to grips with data pre-processing and feature engineering
In Detail
Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind.
OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for.
Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning.
By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Style and approach
OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.
Table of Contents
- A Taste of Machine Learning
- Working with Data in OpenCV and Python
- First Steps in Supervised Learning
- Representing Data and Engineering Features
- Using Decision Trees to Make a Medical Diagnosis
- Detecting Pedestrians with Support Vector Machines
- Implementing a Spam Filter with Bayesian Learning
- Discovering Hidden Structures with Unsupervised Learning
- Using Deep Learning to Classify Handwritten Digits
- Combining Different Algorithms into an Ensemble
- Selecting the Right Model with Hyperparameter Tuning
- Wrapping Up
5. Computer Vision: Models, Learning, and Inference
Feature
Used Book in Good ConditionDescription
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. - Covers cutting-edge techniques, including graph cuts, machine learning, and multiple view geometry. - A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition, and object tracking. - More than 70 algorithms are described in sufficient detail to implement. - More than 350 full-color illustrations amplify the text. - The treatment is self-contained, including all of the background mathematics. - Additional resources at www.computervisionmodels.com.6. Pattern Recognition and Machine Learning (Information Science and Statistics)
Feature
SpringerDescription
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
7. Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches (The Elsevier and Miccai Society Book Series)
Description
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.
Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.
Learn:
- Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects
- Methods and theories for medical image recognition, segmentation and parsing of multiple objects
- Efficient and effective machine learning solutions based on big datasets
- Selected applications of medical image parsing using proven algorithms
- Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects
- Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
- Includes algorithms for recognizing and parsing of known anatomies for practical applications
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