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Object Detection by Stereo Vision Images
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Object Detection by Stereo Vision ImagesНазвание: Object Detection by Stereo Vision Images
Автор: R. Arokia Priya, Anupama V. Patil
Издательство: Wiley-Scrivener
Год: 2022
Страниц: 283
Язык: английский
Формат: pdf (true)
Размер: 49.86 MB

Since both theoretical and practical aspects of the developments in this field of research are explored, including recent state-of-the-art technologies and research opportunities in the area of object detection, this book will act as a good reference for practitioners, students, and researchers.

Current state-of-the-art technologies have opened up new opportunities in research in the areas of object detection and recognition of digital images and videos, robotics, neural networks, Machine Learning (ML), stereo vision matching algorithms, soft computing, customer prediction, social media analysis, recommendation systems, and stereo vision. This book has been designed to provide directions for those interested in researching and developing intelligent applications to detect an object and estimate depth. In addition to focusing on the performance of the system using high-performance computing techniques, a technical overview of certain tools, languages, libraries, frameworks, and APIs for developing applications is also given. More specifically, detection using stereo vision images/video from its developmental stage up till today, its possible applications, and general research problems relating to it are covered. Also presented are techniques and algorithms that satisfy the peculiar needs of stereo vision images along with emerging research opportunities through analysis of modern techniques being applied to intelligent systems.

Feature Extraction Using Python. The feature is expressed in such a manner that it computes some of the object’s most significant characteristics as a function of one or more measurements, each of which defines some calculated attribute of an entity. Feature is a method for transforming raw data into numerical characteristics that may be processed while maintaining the integrity of the actual data set. It yields better outcomes than just implementing Machine Learning to data that has not been processed. The amount of characteristics that may be classified is limited by feature selection. The classification task selects and employs certain features that are likely to aid in discrimination. Features that are not picked are not included. Feature extraction is critical because the unique traits made accessible for differentiation have a direct impact on the success of the classification. Chapter 2 show the implementation of different feature extraction techniques using the different modules of Python’s scikit-learn library.

Audience

Researchers in information technology looking at robotics, Deep Learning, Machine Learning, Big Data analytics, neural networks, pattern & data mining, and image and object recognition. Industrial sectors include automotive electronics, security and surveillance systems, and online retailers.

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