E-Book, Englisch, 460 Seiten
ISBN: 978-1-4398-5599-7
Verlag: CRC Press
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
—Tom Malzbender, Hewlett-Packard Laboratories, Palo Alto, California, USA
Useful for those working in fields including industrial quality control, research, and security applications, Measuring Shape is a handbook for the practical application of shape measurement. Covering a wide range of shape measurements likely to be encountered in the literature and in software packages, this book presents an intentionally diverse set of examples that illustrate and enable readers to compare methods used for measurement and quantitative description of 2D and 3D shapes. It stands apart through its focus on examples and applications, which help readers quickly grasp the usefulness of presented techniques without having to approach them through the underlying mathematics.
An elusive concept, shape is a principal governing factor in determining the behavior of objects and structures. Essential to recognizing and classifying objects, it is the central link in manmade and natural processes. Shape dictates everything from the stiffness of a construction beam, to the ability of a leaf to catch water, to the marketing and packaging of consumer products. This book emphasizes techniques that are quantitative and produce a meaningful yet compact set of numerical values that can be used for statistical analysis, comparison, correlation, classification, and identification.
Written by two renowned authors from both industry and academia, this resource explains why users should select a particular method, rather than simply discussing how to use it. Showcasing each process in a clear, accessible, and well-organized way, they explore why a particular one might be appropriate in a given situation, yet a poor choice in another. Providing extensive examples, plus full mathematical descriptions of the various measurements involved, they detail the advantages and limitations of each method and explain the ways they can be implemented to discover important correlations between shape and object history or behavior. This uncommon assembly of information also includes sets of data on real-world objects that are used to compare the performance and utility of the various presented approaches.
Zielgruppe
Industrial manufacturers in the automotive, computer, energy, and other sectors; all pharmaceutical companies and government agencies in security and military fields.
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Signalverarbeitung, Bildverarbeitung, Scanning
- Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung
Weitere Infos & Material
The Meaning(s) of Shape
Why Shape Matters
Classification and Identification
Correlating Shape with History or Performance
Object Recognition versus Scene Understanding
The Role(s) of Computers
Digital Images
Image Processing to Correct Limitations
Image Processing for Enhancement
Thresholding and Binary Images
Measurement
Sections and Projections
Voxel Arrays
Short-Range Photogrammetry
Computer Graphics, Modeling, Statistical Analysis, and More
Two-Dimensional Measurements (Part 1)
Template Matching and Optical Character Recognition (OCR)
Describing NoncircularityDimension as a Shape Measurement
Skeletons and Topology
LandmarksOther Methods
Two-Dimensional Measurements (Part 2)
The Medial Axis Transform (MAT)
Fourier Shape Descriptors
Wavelet Analysis
MomentsCross-Correlation
Choosing a Technique
Three-Dimensional Shapes
Acquiring Data
Measuring Voxel Arrays
Imaging Surfaces
Surface Metrology
Image RepresentationTopography
Classification, Comparison, and Correlation
Field Guides
Defining the Task
Cluster Analysis
Populations
Comparing Normal and Non-Normal Data Sets
Bayes’ Rule
Neural Nets
Syntactical Analysis
Correlations
Example: Animal Cookies
Heuristic Classification
Conclusions