Measurements and Instrumentation for Machine Vision The book "Measurements and Instrumentation for Machine Vision" provides an overview of the principles and techniques used in machine vision systems to measure and instrument the physical world. The book covers topics such as sensors and sensor fusion, signal processing and feature extraction, and camera calibration and distortion correction. It also discusses the challenges of measuring and instrumenting the physical world and the importance of understanding the limitations and capabilities of these techniques in order to design and implement successful machine vision systems. The book begins by exploring the concept of measurement and its role in machine vision, highlighting the need for accurate and reliable measurements in order to achieve high-quality images and perform tasks such as object recognition and tracking. It then delves into the various types of sensors used in machine vision, including cameras, lidars, and sonars, and discusses their strengths and weaknesses. The author also covers the topic of sensor fusion, which involves combining data from multiple sensors to improve accuracy and robustness. Next, the book examines the process of signal processing, which is essential for extracting useful information from raw sensor data. This includes filtering, thresholding, and feature extraction, among other techniques. The author also discusses the importance of feature selection and how it can be used to improve system performance. Camera calibration and distortion correction are also covered in detail, as these are critical steps in ensuring that images are accurately captured and processed. The book provides practical examples of how to perform these tasks and how to correct for distortions that may occur due to lens or sensor defects.

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