TUM Library

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Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Authors -- 1: Computer and Human Vision Systems -- 1.1 The Human Eye -- 1.2 Computer versus Human Vision Systems -- 1.3 Evolution of Computer Vision -- 1.4 Computer/Machine Vision and Image Processing -- 1.5 Applications of Computer Vision -- 1.6 Summary -- Exercises -- 2: Digital Image Fundamentals -- 2.1 Digital Image -- 2.2 Monochrome and Color Images -- 2.3 Image Brightness and Contrast -- 2.4 2D, 3D, and 4D Images -- 2.5 Digital Image Representation -- 2.6 Digital Image File Formats

2.7 Fundamental Image Operations -- 2.7.1 Points, Edges, and Vertices -- 2.7.2 Point Operations -- 2.7.3 Thresholding -- 2.7.4 Brightness -- 2.7.5 Geometric Transformations -- 2.7.6 Spatial Transformation -- 2.7.7 Affine Transformation -- 2.7.8 Image Interpolation -- 2.7.8.1 Nearest-Neighbor Interpolation -- 2.7.8.2 Bilinear Interpolation -- 2.7.8.3 Bicubic Interpolation -- 2.8 Fundamental Steps in Digital Image Processing -- 2.9 Summary -- Exercises -- 3: Machine Vision System Components -- 3.1 Machine Vision System -- 3.2 Machine Vision Camera -- 3.2.1 CCD and CMOS Image Sensors

3.2.2 TDI Sensor -- 3.2.3 Camera Type -- 3.2.3.1 Area Scan Cameras -- 3.2.3.2 Line Scan Cameras -- 3.2.3.3 Smart Cameras -- 3.2.4 Camera Lens -- 3.2.4.1 Resolution, Contrast, and Sharpness -- 3.3 Lenses and Their Parameters -- 3.3.1 Types of Lenses -- 3.3.2 Lens Mounts -- 3.3.3 Lens Selection Examples -- 3.3.3.1 Field of View (Image Size) Is Much Larger Than Camera Sensor Size -- 3.3.3.2 Field of View Is Smaller or Close to Camera Sensor Size -- 3.4 Machine Vision Lighting -- 3.4.1 Light Sources in Machine Vision -- 3.4.2 Illumination Techniques -- 3.4.2.1 BackLighting -- 3.4.2.2 FrontLighting

3.4.2.3 Diffused Lighting -- 3.4.2.4 Oblique Lighting -- 3.4.2.5 Dark Field Lighting -- 3.4.2.6 Infrared and Ultraviolet Light -- 3.4.3 Illumination Summary -- 3.5 Filters -- 3.6 Machine Vision Software -- 3.6.1 Integration and Compatibility -- 3.6.2 Ease of Use and Cost to Operate -- 3.6.3 Vendor Support and Stability -- 3.7 Machine Vision Automation -- 3.8 Integration of Machine Vision Components -- 3.9 Summary -- Exercises -- 4: Machine Vision Applications in Quality Control -- 4.1 Overview of Quality Control -- 4.2 Quality Inspection and Machine Vision -- 4.3 Designing a Machine Vision System

4.4 Machine Vision Systems in Industry -- 4.5 Categorization of Machine Vision Solutions -- 4.5.1 Dimensional Measurement -- 4.5.1.1 Dimensional Measurement of Oil Seal -- 4.5.1.2 Dimensional Measurement of Reed Valve -- 4.5.2 Presence/Absence Inspection -- 4.5.2.1 Blister Pack Inspection -- 4.5.2.2 Bottle Cap Inspection -- 4.5.3 Character Inspection -- 4.5.3.1 Label and Barcode Inspection -- 4.5.3.2 Drug Pack Inspection -- 4.5.4 Profile Inspection -- 4.5.4.1 Profile Inspection of Spline Gear -- 4.5.4.2 Profile Inspection for Packaging Integrity -- 4.5.5 Surface Inspection -- 4.5.6 Robot Guidance

4.6 Summary

Machine Vision systems combine image processing with industrial automation. One of the primary areas of application of Machine Vision in the Industry is in the area of Quality Control. Machine vision provides fast, economic and reliable inspection that improves quality as well as business productivity. Building machine vision applications is a challenging task as each application is unique, with its own requirements and desired outcome. A Guide to Machine Vision in Quality Control follows a practitioner's approach to learning machine vision. The book provides guidance on how to build machine vision systems for quality inspections. Practical applications from the Industry have been discussed to provide a good understanding of usage of machine vision for quality control. Real-world case studies have been used to explain the process of building machine vision solutions. The book offers comprehensive coverage of the essential topics, that includes: Introduction to Machine Vision Fundamentals of Digital Images Discussion of various machine vision system components Digital image processing related to quality control Overview of automation The book can be used by students and academics, as well as by industry professionals, to understand the fundamentals of machine vision. Updates to the on-going technological innovations have been provided with a discussion on emerging trends in machine vision and smart factories of the future. Sheila Anand is a PhD graduate and Professor at Rajalakshmi Engineering College, Chennai, India. She has over three decades of experience in teaching, consultancy and research. She has worked in the software industry and has extensive experience in development of software applications and in systems audit of financial, manufacturing and trading organizations. She guides Ph.D. aspirants and many of her research scholars have since been awarded their doctoral degree. She has published many papers in national and international journals and is a reviewer for several journals of repute. L Priya is a PhD graduate working as Associate Professor and Head, Department of Information Technology at Rajalakshmi Engineering College, Chennai, India. She has nearly two decades of teaching experience and good exposure to consultancy and research. She has delivered many invited talks, presented papers and won several paper awards in International Conferences. She has published several papers in International journals and is a reviewer for SCI indexed journals. Her areas of interest include Machine Vision, Wireless Communication and Machine Learning.

OCLC-licensed vendor bibliographic record.