Image Algebra and Automatic Shape Recognition

Crimmins, Thomas R.; Brown, W. Norman

doi:10.1109/taes.1985.310539

Cited by 93 publications

(29 citation statements)

References 2 publications

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“…This refers to the set of pixels in a image that represent the shape of the target, obtained through a series of image algebra operations such as defined by Crimmins [1].…”

Section: Descriptionmentioning

confidence: 99%

Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery

Stanhope

Daida

1998

Lecture Notes in Computer Science

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Abstract. We use the genetic programming (GP) paradigm for two tasks. The first task given a GP is the generation of rules for the target / clutter classification of a set of synthetic aperture radar (SAR) images, the second, the generation of rules for the identification of tanks in a second set of SAR images. To perform these tasks, previously defined feature sets are generated on the various images, and GP is used to select relevant features and methods of analyzing these features. GP results are then compared with previous work using the feature sets.

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“…This refers to the set of pixels in a image that represent the shape of the target, obtained through a series of image algebra operations such as defined by Crimmins [1].…”

Section: Descriptionmentioning

confidence: 99%

Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery

Stanhope

Daida

1998

Lecture Notes in Computer Science

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“…Then the hit-or-miss transform [2] (X0 A) n [Xc0 (W\A)] provides the set of points p at which A "fits exactly" inside an input set X; i.e., A + p C X and ( W\A ) + p s X". Crimmins and Brown [8] proved that this shape recognition transform is the prototype for a large class of binary or graytone image transforms, called window-tranforms (W-transforms), defined below.…”

Section: Window-transforms For Shape Recognitionmentioning

confidence: 99%

“…Theorem 7 (8]: Let W E Z2. An SP system q is called a W-transform iff there exists X C 6 ( W) such that, VX G z2, 'P(X) = {p EZ~:(W n X -p) E X}.…”

Section: Window-transforms For Shape Recognitionmentioning

confidence: 99%

“…A few examples can be found in [2], [S], [16]- [19], the session on morphology of the conference [20], and the tutorial parts of [lo], [ 121, 1141. ports a general purpose vision (software or hardware) module that can perform erosions/dilations. Although several morphology-based algebraic systems have been proposed (e.g., see [2], [8], [9], [29]) for image processing, it is known from [30], [31] that erosions/dilations are insufficient by themselves to represent all possible image operations. Image algebras have been developed [30], [3 l]- [33], [34] which represent all digital image-to-image operations as a finite composition of a few basic operations, which include erosions/dilations.…”

Section: Introductionmentioning

confidence: 99%

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A representation theory for morphological image and signal processing

Maragos

1989

IEEE Trans. Pattern Anal. Machine Intell.

204

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Abstract-This paper presents a unifying theory for many concepts and operations encountered in or related to morphological image and signal analysis. This unification requires a set-theoretic melhodology, where signals are modeled as sets, systems (signal transformations) are viewed as set mappings, and translation-invariant systems are uniquely characterized by special collections of input signals. This approach leads to a general representation theory, in which any translation-invariant, increasing, upper semicontinuous system can be represented exactly as a minimal nonlinear superposition of morphological erosions or dilations.In this representation, many similarities and a few differences are observed between systems processing binary or multilevel signals, and continuous-domain or discrete-domain signals.The theory is used to analyze some special cases of image/signal analysis systems, such as morphological filters, median and order-statistic filters, linear filters, and shape recognition transforms.Although the developed theory is algebraic, its prototype operations are well suitable for shape analysis; hence, the results of this study also apply to systems that extract information about the geometrical structure of signals.Zndex Terms--Image/signal processing, mathematical morphology, nonlinear/linear filtering, shape analysis, systems representation.

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“…The number of structuring elements combined with the possible choices of morphological operator sequences makes morphological filter design something of a black art. This is why a few researchers have attempted to (at least partially) automatise MM algorithm design [6,33,42]. Some of that work has used Genetic Algorithms (GAs) [15] for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Morphological algorithm design for binary images using genetic programming

Quintana

Poli

Claridge

2006

Genet Program Evolvable Mach

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Abstract. This paper presents a Genetic Programming (GP) approach to the design of Mathematical Morphology (MM) algorithms for binary images. The algorithms are constructed using logic operators and the basic MM operators, i.e. erosion and dilation, with a variety of structuring elements. GP is used to evolve MM algorithms that convert a binary image into another containing just a particular feature of interest. In the study we have tested three fitness functions, training sets with different numbers of elements, training images of different sizes, and 7 different features in two different kinds of applications. The results obtained show that it is possible to evolve good MM algorithms using GP.

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Image Algebra and Automatic Shape Recognition

Cited by 93 publications

References 2 publications

Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery

Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery

A representation theory for morphological image and signal processing

Morphological algorithm design for binary images using genetic programming

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