Search Tips Advanced Search Handbook of Computer Vision Algorithms in Image Algebra by Gerhard X. Ritter; Joseph N. Wilson CRC Press, CRC Press LLC ISBN: 0849326362 Pub Date: 05/01/96 Search this book: Preface Acknowledgments Chapter 1—Image Algebra 1.1. Introduction 1.2. Point Sets 1.3. Value Sets 1.4. Images 1.5. Templates 1.6. Recursive Templates 1.7. Neighborhoods 1.8. The p-Product 1.9. References Chapter 2—Image Enhancement Techniques 2.1. Introduction 2.2. Averaging of Multiple Images 2.3. Local Averaging 2.4. Variable Local Averaging 2.5. Iterative Conditional Local Averaging 2.6. Max-Min Sharpening Transform 2.7. Smoothing Binary Images by Association 2.8. Median Filter 2.9. Unsharp Masking Title invariant 2.10. Local Area Contrast Enhancement 2.11. Histogram Equalization 2.12. Histogram Modification 2.13. Lowpass Filtering 2.14. Highpass Filtering 2.15. References Chapter 3—Edge Detection and Boundary Finding Techniques 3.1. Introduction 3.2. Binary Image Boundaries 3.3. Edge Enhancement by Discrete Differencing 3.4. Roberts Edge Detector 3.5. Prewitt Edge Detector 3.6. Sobel Edge Detector 3.7. Wallis Logarithmic Edge Detection 3.8. Frei-Chen Edge and Line Detection 3.9. Kirsch Edge Detector 3.10. Directional Edge Detection 3.11. Product of the Difference of Averages 3.12. Crack Edge Detection 3.13. Local Edge Detection in Three-Dimensional Images 3.14. Hierarchical Edge Detection 3.15. Edge Detection Using K-Forms 3.16. Hueckel Edge Operator 3.17. Divide-and-Conquer Boundary Detection 3.18. Edge Following as Dynamic Programming 3.19. References Chapter 4—Thresholding Techniques 4.1. Introduction 4.2. Global Thresholding 4.3. Semithresholding 4.4. Multilevel Thresholding 4.5. Variable Thresholding 4.6. Threshold Selection Using Mean and Standard Deviation 4.7. Threshold Selection by Maximizing Between-Class Variance 4.8. Threshold Selection Using a Simple Image Statistic 4.9. References Chapter 5—Thining and Skeletonizing 5.1. Introduction 5.2. Pavlidis Thinning Algorithm 5.3. Medial Axis Transform (MAT) 5.4. Distance Transforms 5.5. Zhang-Suen Skeletonizing 5.6. Zhang-Suen Transform — Modified to Preserve Homotopy 5.7. Thinning Edge Magnitude Images 5.8. References Chapter 6—Connected Component Algorithms 6.1. Introduction 6.2. Component Labeling for Binary Images 6.3. Labeling Components with Sequential Labels 6.4. Counting Connected Components by Shrinking 6.5. Pruning of Connected Components 6.6. Hole Filling 6.7. References Chapter 7—Morphological Transforms and Techniques 7.1. Introduction 7.2. Basic Morphological Operations: Boolean Dilations and Erosions 7.3. Opening and Closing 7.4. Salt and Pepper Noise Removal 7.5. The Hit-and-Miss Transform 7.6. Gray Value Dilations, Erosions, Openings, and Closings 7.7. The Rolling Ball Algorithm 7.8. References Chapter 8—Linear Image Transforms 8.1. Introduction 8.2. Fourier Transform 8.3. Centering the Fourier Transform 8.4. Fast Fourier Transform 8.5. Discrete Cosine Transform 8.6. Walsh Transform 8.7. The Haar Wavelet Transform 8.8. Daubechies Wavelet Transforms 8.9. References Chapter 9—Pattern Matching and Shape Detection 9.1. Introduction 9.2. Pattern Matching Using Correlation 9.3. Pattern Matching in the Frequency Domain 9.4. Rotation Invariant Pattern Matching 9.5. Rotation and Scale Invariant Pattern Matching Table of Contents Products | Contact Us | About Us | Privacy | Ad Info | Home Use of this site is subject to certain Terms & Conditions, Copyright © 1996-2000 EarthWeb Inc. All rights reserved. Reproduction whole or in part in any form or medium without express written permission of EarthWeb is prohibited. Read EarthWeb's privacy statement. iff “if and only if” ¬ “not”  “there exists” “there does not exist”  “for each” s.t. “such that” Sets Theoretic Notation and Operations Symbol Explanation X, Y, Z Uppercase characters represent arbitrary sets. x, y, z Lowercase characters represent elements of an arbitrary set. X, Y, Z Bold, uppercase characters are used to represent point sets. x, y, z Bold, lowercase characters are used to represent points, i.e., elements of point sets. The set = {0, 1, 2, 3, }. The set of integers, positive integers, and negative integers. The set = {0, 1, , n - 1}. The set = {1, 2, , n}. The set = {-n+1, , -1, 0, 1, , n - 1}. The set of real numbers, positive real numbers, negative real numbers, and positive real numbers including 0. The set of complex numbers. An arbitrary set of values. The set unioned with {}. The set unioned with {}. The set unioned with {-,}.  The empty set (the set that has no elements). 2 X The power set of X (the set of all subsets of X).  “is an element of”  “is not an element of” 4 “is a subset of” Union X * Y = {z : z  X or z  Y} Let be a family of sets indexed by an indexing set ›. = {x : x  X » for at least one »  ›} = X 1 * X 2 * * X n = {x : x  X i for some i  } X Y Intersection X ) Y = {z : z  X and z  Y} Let be a family of sets indexed by an indexing set ›. = {x : x  X » for all »  ›} = X 1 ) X 2 ) ) X n = {x : x  X i for all i  } X × Y Cartesian product X × Y {(x, y) : x  X, y  Y} = {(x 1 ,x 2 , ,x n ) : x i  X i } = {(x 1 ,x 2 ,x 3 , ) : x i  X i } The Cartesian product of n copies of , i.e., . X \ Y Set difference Let X and Y be subsets of some universal set U, X \ Y = {x  X : x  Y}. X2 Complement X2 = U \ X, where U is the universal set that contains X. card(X) The cardinality of the set X. choice(X) A function that randomly selects an element from the set X. Point and Point Set Operations Symbol Explanation x + y If x, y  , then x + y = (x 1 + y 1 , , x n + y n ) x - y If x, y  , then x - y = (x 1 - y 1 , , x n - y n ) x · y If x, y  , then x · y = (x 1 y 1 , , x n y n ) x/y If x, y  , then x/y = (x 1 /y 1 , , x n /y n ) x ¦ y If x, y  , then x ¦ y = (x 1 ¦ y 1 , , x n ¦ y n ) x ¥ y If x, y  , then x ¥ y = (x 1 ¥ y 1 , , x n ¥ y n ) x ³ y In general, if x, y  , and = (x 1 ³y 1 , , x n ³y n ) k³x If k  and x  and , then k³x = (k³x 1 , , k³x n ) x"y If x, y  , then x"y = x 1 y 1 + x 2 y 2 + ··· + x n y n x × y If x, y  , then x × y = (x 2 y 3 - x 3 y 2 , x 3 y 1 - x 1 y 3 , x 1 y 2 - x 2 y 1 ) If x  and y  then = (x 1 , , x n , y 1 , , y m ) -x If x  , then -x = (-x 1 , , -x n )  x If x  , then If x  , then  x = ( x 1 , ,  x n )  x If x  , then  x = ( x 1 , ,  x n ) [x] If x  , then [x] = ([x 1 ], , [x n ]) pi(x) If x = (x 1 , x 2 , , x n )  , then pi (x) = x i £x If x  , then £x = x 1 + x 2 + ··· + x n x If x  , then x = x 1 x 2 ··· x n ¦x If x  , then ¦x = x 1 ¦ x 2 ¦ ··· ¦ x n ¥x If x  , then ¥x = x 1 ¥ x 2 ¥ ··· ¥ x n ||x|| 2 If x  , then ||x|| 2 = ||x|| 1 If x  , then ||x|| 1 = |x 1 | + |x 2 | + ··· + |x n | ||x||  If x  , then ||x||  = |x 1 | ¦ |x 2 | ¦ ··· ¦ |x n | dim(x) If x  , then dim(x) = n X + Y If X, Y , then X + Y = {x + y : x  X and y  Y} X - Y If X, Y , then X - Y = {x - y : x  X and y  Y} X + p If X , then X + p = {x + p : x  X} X - p If X , then X - p = {x - p : x  X} X * Y If X, Y , then X * Y = {z : z  X or z  Y} X\Y If X, Y , then X\Y = {z : z  X and z  Y} X ” Y If X, Y , then X ” Y = {z : z  X * Y and z  X ) Y} X × Y If X, Y , then X × Y = {(x, y) : x  X and y  Y} -X If X , then -X = {-x : x  X} If X , then = {z : z  and z  X} sup(X) If X , then sup(X) = the supremum of X. If X = {x 1 , x 2 , , x n }, then sup(X) = x 1 ¦ x 2 ¦ ¦ x n X For a point set X with total order , x 0 = X Ô x x 0 ,  x  X \ {x 0 } inf(X) If X , then inf(X) = the infimum of X . If X = {x 1 , x 2 , , x n }, , then sup(X) = x 1 ¥ x 2 ¥ ¥ x n X For a point set X with total order , x 0 = X Ô x 0 x,  x  X \ {x 0 } choice(X) If X then, choice(X)  X (randomly chosen element) card(X) If X , then card(X) = the cardinality of X Morphology In following table A, B, D, and E denote subsets of . Symbol Explanation A* The reflection of A across the origin 0 = (0, 0, 0)  . A2 The complement of A; i.e., A2 = {x  : x  A}. A b A b = {a + b : a  A} A × B Minkowski addition is defined as A × B = {a + b : a  A, b  B}. (Section 7.2) A/B Minkowski subtraction is defined as A/B = (A2 × B*)2. (Section 7.2) A B The opening of A by B is denoted A B and is defined by A B = (A/B) × B. (Section 7.3) A " B The closing of A by B is denoted A " B and is defined by A " B = (A × B)/B. (Section 7.3) A C Let C = (D, E) be an ordered pair of structuring elements. The hit-and-miss transform of the set A is given by A C = {p : D p 4 A and E p 4 A2}. (Section 7.5) Functions and Scalar Operations Symbol Explanation f : X ’ Y f is a function from X into Y. domain(f) The domain of the function f : X ’ Y is the set X. range(f) The range of the function f : X ’ Y is the set {f (x) : x  X}. f -1 The inverse of the function f. Y X The set of all functions from X into Y, i.e., if f  Y X , then f : X ’ Y. f| A Given a function f : X ’ Y and a subset A 4 X, the restriction of f to A, f| A : A ’ Y, is defined by f| A (a) = f(a) for a  A. f| g Given: f : A ’ Y and g : B ’ Y, the extension of f to g is defined by . g f Given two functions f : X ’ Y and g : Y ’ Z, the composition g f : X ’ Z is defined by (g f)(x) = g(f (x)), for every x  X. f + g Let f and g be real or complex-valued functions, then (f + g)(x) = f(x) + g(x). f · g Let f and g be real or complex-valued functions, then (f · g)(x) = f(x) · g(x). k · f Let f be a real or complex-valued function, and k be a real or complex number, then f  , (k · f)(x) = k · (f (x)). |f| |f|(x) = |f(x)|, where f is a real (or complex)-valued function, and |f(x)| denotes the absolute value (or magnitude) of f(x). 1 X The identity function 1 X : X ’ X is given by 1 X (x) = x. The projection function p j onto the jth coordinate is defined by p j (x 1 , ,x j , ,x n ) = x j . card(X) The cardinality of the set X. choice(X) A function which randomly selects an element from the set X. x ¦ y For x, y  , x ¦ y is the maximum of x and y. x ¥ y For x, y  , x ¥ y is the minimun of x and y.  x For x  the ceiling function  x returns the smallest integer that is greater than or equal to x.  x For x  the floor function  x returns the largest integer that is less than or equal to x. [x] For x  the round function returns the nearest integer to x. If there are two such integers it yields the integer with greater magnitude. x mod y For x, y  , x mod y = r if there exists k, r  with r < y such that x = yk + r. Ç S (x) The characteristic function Ç S is defined by . Images and Image Operations Symbol Explanation a, b, c Bold, lowercase characters are used to represent images. Image variables will usually be chosen from the beginning of the alphabet. a  The image a is an -valued image on X. The set is called the value set of a and X the spatial domain of a. 1  Let be a set with unit 1. Then 1 denotes an image, all of whose pixel values are 1. 0  Let be a set with zero 0. Then 0 denotes an image, all of whose pixel values are 0. a| Z The domain restriction of a  to a subset Z of X is defined by a| Z = a ) (Z × ). a|| S The range restriction of a  to the subset S 4 is defined by a|| S = a ) (X × S). The double-bar notation is used to focus attention on the fact that the restriction is applied to the second coordinate of a 4 X × . a| (Z,S) If a  , Z 4 X, and S 4 , then the restriction of a to Z and S is defined as a| (Z,S) = a ) (Z × S). a| b Let X and Y be subsets of the same topological space. The extension of a  to b  is defined by . (a|b), (a 1 |a 2 | ···, |a n ) Row concatenation of images a and b, respectively the row concatenation of images a 1 , a 2 , , a n . Column concatenation of images a and b. f(a) If a  and f : ’ Y, then the image f(a)  Y X is given by f a, i.e., f(a) = {(x, c(x)) : c(x) = f(a(x)), x  X}. a f If f : Y ’ X and a  , the induced image a f  is defined by a f = {(y, a(f(y))) : y  Y}. a ³ b If ³ is a binary operation on , then an induced operation on can be defined. Let a, b  ; the induced operation is given by a ³ b = {(x, c(x)) : c(x) = a(x) ³ b(x), x  X}. k ³ a Let k  , a  , and ³ be a binary operation on . An induced scalar operation on images is defined by k ³ a = {(x, c(x)) : c(x) = k ³ a(x),x  X}. a b Let a, b  ; a b = {(x, c(x)) : c(x) = a(x) b(x) , x  X}. log b a Let a, b  log b a = {(x, c(x)) : c(x) = log b(x) a(x), x  X}. a* Pointwise complex conjugate of image a, a* (x) = (a(x))*. “a “a denotes reduction by a generic reduce operation . The following four items are specific examples of the global reduce operation. Each assumes a  and X = {x 1 , x 2 , , x n }. = a(x 1 ) + a(x 2 ) + ··· + a(x n ) = a(x 1 ) · a(x 2 ) ····· a(x n ) = a(x 1 ) ¦ a(x 2 ) ¦ ··· ¦ a(x n ) = a(x 1 ) ¥ a(x 2 ) ¥ ··· ¥ a(x n ) a " b Dot product, a " b = £(a · b) = (a(x) · b(x)). ã Complementation of a set-valued image a. a c Complementation of a Boolean image a. a2 Transpose of image a. Templates and Template Operations Symbol Explanation s, t, u Bold, lowercase characters are used to represent templates. Usually characters from the middle of the alphabet are used as template variables. t  A template is an image whose pixel values are images. In particular, an -valued template from Y to X is a function t : Y ’ . Thus, t  and t is an -valued image on Y. t y Let t  . For each y  Y, t y = t(y). The image t y  is given by t y = {(x, t y (x)) : x  X}. S(t y ) If and t  , then the support of t is denoted by S(t y ) and is defined by S(t y ) = {x  X : t y (x) ` 0}. S  (t y ) If t  , then S  (t y ) = {x  X : t y (x) ` }. S - (t y ) If t  , then S - (t y ) = {x  X : t y (x) ` -}. S ± (t y ) If t  , then S ± (t y ) = {x  X : t y (x) ` ±}. t(p) A parameterized -valued template from Y to X with parameters in P is a function of the form t : P ’ . t2 Let t  . The transpose t2  is defined as . Image-Template Operations In the table below, X is a finite subset of . Symbol Explanation a t Let ( , ³, ) be a semiring and a  , t  , then the generic right product of a with t is defined as a . t a With the conditions above, except that now t  , the generic left product of a with t is defined as . a t Let Y 4 , a  , and t  , where . The right linear product (or convolution) is defined as . t a With the conditions above, except that t  , the left linear product (or convolution) is defined as . a t For a  and t  , the right additive maximum is defined by . t a For a  and t  , the left additive maximum is defined by . [...]... points of a given point Figure 1. 2.2 The von Neumann neighborhood N(x) and the Moore neighborhood M(x) of a point x There are many other point operations that are useful in expressing computer vision algorithms in succinct algebraic form For instance, in certain interpolation schemes it becomes necessary to switch from points with real-valued coordinates (floating point coordinates) to corresponding... constraints They are not limitations of image algebra, and they should not be confused with the capability of image algebra as a mathematical tool for image manipulation Image algebra is a heterogeneous or many-valued algebra in the sense of Birkhoff and Lipson [53, 1] , with multiple sets of operands and operators Manipulation of images for purposes of image enhancement, analysis, and understanding involves... of Contents Next Title Chapter 1 Image Algebra - 1. 1 Introduction Since the field of image algebra is a recent development it will be instructive to provide some background information In the broad sense, image algebra is a mathematical theory concerned with the transformation and analysis of images Although much emphasis is focused on the analysis and transformation of digital images, the main... establishment of a comprehensive and unifying theory of image transformations, image analysis, and image understanding in the discrete as well as the continuous domain [1] The idea of establishing a unifying theory for the various concepts and operations encountered in image and signal processing is not new Over thirty years ago, Unger proposed that many algorithms for image processing and image analysis... operations not only on images, but also on different types of values and quantities associated with these images Thus, the basic operands of image algebra are images and the values and quantities associated with these images Roughly speaking, an image consists of two things, a collection of points and a set of values associated with these points Images are therefore endowed with two types of information, namely... include image algebra Fortran (IAF) [45], an image algebra Ada (IAA) translator [46], image algebra Connection Machine *Lisp [47, 48], an image algebra language (IAL) implementation on transputers [49, 50], and an image algebra C++ class library (iac++) [ 51, 52] Unfortunately, there is often a tendency among engineers to confuse or equate these languages with image algebra An image algebra programming... not image algebra, which is a mathematical theory An image algebra- based programming language typically implements a particular subalgebra of the full image algebra In addition, simplistic implementations can result in poor computational performance Restrictions and limitations in implementation are usually due to a combination of factors, the most pertinent being development costs and hardware and software... on which image processing is based and that is compatible with both sequential and parallel architectures Enthusiasm for image algebra must be tempered by the knowledge that image algebra, like any other field of mathematics, will never be a finished product but remain a continuously evolving mathematical theory concerned with the unification of image processing and computer vision tasks Much of the... the spatial relationship of the points, and also some type of numeric or other descriptive information associated with these points Consequently, the field of image algebra bridges two broad mathematical areas, the theory of point sets and the algebra of value sets, and investigates their interrelationship In the sections that follow we discuss point and value sets as well as images, templates, and neighborhoods... that of pixel neighborhood arithmetic and The role of image algebra in computer vision and image processing tasks and theory should not be confused with the government’s Ada programming language effort The goal of the development of the Ada programming language was to provide a single high-order language in which to implement embedded systems The special architectures being developed nowadays for image . Preface Acknowledgments Chapter 1 Image Algebra 1. 1. Introduction 1. 2. Point Sets 1. 3. Value Sets 1. 4. Images 1. 5. Templates 1. 6. Recursive Templates 1. 7. Neighborhoods 1. 8. The p-Product 1. 9. References Chapter 2 Image. Table of Contents Next Chapter 1 Image Algebra 1. 1. Introduction Since the field of image algebra is a recent development it will be instructive to provide some background information. In the. useful in expressing computer vision algorithms in succinct algebraic form. For instance, in certain interpolation schemes it becomes necessary to switch from points with real-valued coordinates