Measuring Shape

Book description

"John Russ is the master of explaining how image processing gets applied to real-world situations. With Brent Neal, he’s done it again in Measuring Shape, this time explaining an expanded toolbox of techniques that includes useful, state-of-the-art methods that can be applied to the broad problem of understanding, characterizing, and measuring shape. He has a gift for finding the kernel of a particular algorithm, explaining it in simple terms, then giving concrete examples that are easily understood. His perspective comes from solving real-world problems and separating out what works in practice from what is just an abstract curiosity." 

—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.

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Table of Contents
  6. Introduction
  7. 1. The Meaning(s) of Shape
    1. Why Shape Matters
      1. Measuring versus Comparing
      2. Shape and Human Vision
    2. Classification and Identification
      1. Hierarchical Classification
      2. The Threshold Logic Unit
      3. Faces and Fingerprints
      4. The General Problem
    3. Correlating Shape with History or Performance
      1. Shape Matching and Morphing
    4. Object Recognition versus Scene Understanding
  8. 2. The Role(s) of Computers
    1. Digital Images
      1. Pixel Array Size
      2. Color
      3. Camera Specifications
    2. Image Processing to Correct Limitations
      1. Color Adjustment
      2. Noise Reduction I: Speckle Noise
      3. Noise Reduction II: Periodic Noise
      4. Nonuniform Brightness
      5. Contrast and Brightness Adjustments
      6. Distortion Correction
      7. Blur Removal
    3. Image Processing for Enhancement
      1. Edges
      2. Texture
      3. Cross-Correlation
    4. Thresholding and Binary Images
      1. Automatic Threshold Setting
      2. Morphological Processing I: Erosion and Dilation
      3. Morphological Processing II: Outlines, Holes, and Skeletons
      4. The Euclidean Distance Map and Watershed Segmentation
      5. Boolean Combinations
      6. Encoding Boundary Information
    5. Measurement
      1. Counting
      2. Measuring Size
      3. Measuring Location
      4. Measuring Density
      5. Measuring Shape
    6. Sections and Projections
      1. Stereology and Geometric Probability
      2. Volume
      3. Surface Area and Length
      4. Topology
    7. Voxel Arrays
      1. Three-Dimensional Measurements
    8. Short-Range Photogrammetry
    9. Computer Graphics, Modeling, Statistical Analysis, and More
  9. 3. Two-Dimensional Measurements (Part 1)
    1. Template Matching and Optical Character Recognition (OCR)
      1. Syntactical Analysis
      2. Reading License Plates
      3. Universal Product Code (UPC)
      4. Cross-Correlation
    2. Describing Noncircularity
      1. More Dimensionless Ratios to Measure Shape
      2. Example: Leaves
      3. Example: Graphite in Cast Iron
    3. Dimension as a Shape Measurement
      1. Using the Fractal Dimension
    4. Skeletons and Topology
      1. Branching Patterns
    5. Landmarks
      1. Human Faces
    6. Other Methods
      1. Curvature Scale Space
      2. Some Additional Approaches
  10. 4. Two-Dimensional Measurements (Part 2)
    1. The Medial Axis Transform (MAT)
      1. Syntactical Analysis with the MAT
      2. The Shock Graph
    2. Fourier Shape Descriptors
      1. Shape Unrolling
      2. Complex Coordinates
      3. Vector Classification
      4. Symmetry Estimation Using the Boundary
      5. Symmetry Estimation Using the Interior Pixels
    3. Wavelet Analysis
    4. Moments
      1. Example: Tiger Footprints
      2. Zernike Moments
    5. Cross-Correlation
    6. Choosing a Technique
      1. Example: Arrow Points
  11. 5. Three-Dimensional Shapes
    1. Acquiring Data
      1. Registration and Alignment
    2. Measuring Voxel Arrays
      1. Topology, Skeletons, and Shape Factors
      2. Projections (Silhouettes) of Shapes
      3. Spherical Harmonics
      4. Spherical Wavelets
    3. Imaging Surfaces
      1. Stereoscopy
      2. Shape from Shading
      3. Other Methods
    4. Surface Metrology
      1. Roundness
      2. Straightness
      3. Noncontacting Measurement
    5. Image Representation
    6. Topography
      1. Fractal Dimension
  12. 6. Classification, Comparison, and Correlation
    1. Field Guides
      1. Template Matching and Cross-Correlation
      2. A Simple Identification Example
    2. Defining the Task
      1. Decision Thresholds
      2. Covariance
      3. Example: Mixed Nuts
    3. Cluster Analysis
      1. Dendrograms
      2. Using Rank Order Instead of Value
      3. K-Means and K-Neighbors
      4. Spanning Trees and Clusters
    4. Populations
      1. Gaussian (Normal) Distributions
    5. Comparing Normal and Non-Normal Data Sets
      1. Nonparametric Comparison
      2. Nonparametric Estimation of Covariance
      3. Outliers
    6. Bayes’ Rule
    7. Neural Nets
    8. Syntactical Analysis
    9. Correlations
      1. Physical Examples
      2. Biological Examples
    10. Example: Animal Cookies
      1. Additional Measurements
    11. Heuristic Classification
    12. Conclusions
  13. References
  14. Index

Product information

  • Title: Measuring Shape
  • Author(s): F. Brent Neal, John C. Russ
  • Release date: December 2017
  • Publisher(s): CRC Press
  • ISBN: 9781351833141