An edge detection technique using genetic algorithm-based optimization

Bhandarkar, Suchendra M.; Zhang, Yiqing; Potter, Walter D.

doi:10.1016/0031-3203(94)90003-5

Cited by 117 publications

(43 citation statements)

References 20 publications

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“…Thirdly, as in the case of some other local smoothing filters, the detected edges resulting from our algorithms may be thick and fragmented at some places. This would call for some global smoothing/sharpening techniques (Acton and Bovik, 1992;Bhandarkar et al, 1994;Tan et al, 1989Tan et al, , 1991 to be used in conjunction with the algorithms presented here. We will address these issues in our future research.…”

Section: Discussionmentioning

confidence: 99%

“…These operators are only suitable for detecting limited types of edges and are highly susceptible to noise often resulting in fragmented edges. More recent edge detection techniques are based on optimal filtering (Canny, 1987;Dickey and Shanmugam, 1977;Lee and Wasilkowski, 1991;Sarkar and Boyer, 1990;Shen and Castan, 1992), random field models (Cressie, 1991;Hansen and Elliot, 1982;Huang and Tseng, 1988), surface fitting (Haralick, 1984;Nalwa and Binford, 1986;Sinha and Schunk, 1992), heuristic state-space search (Ashkar and Modestino, 1978;Martelli, 1976;Montanari, 1971), anisotropic diffusion (Perona and Malik, 1990;Saint-Marc et al, 1991), residual analysis and global cost minimization using hill-climbing search (Tan et al, 1989), simulated annealing (Tan et al, 1991), mean field annealing (Acton and Bovik, 1992) and the genetic algorithm (Bhandarkar et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

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An edge detection technique using local smoothing and statistical hypothesis testing

Qiu

Bhandarkar

1996

Pattern Recognition Letters

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An edge detection technique using local smoothing and statistical hypothesis testing

Qiu

Bhandarkar

1996

Pattern Recognition Letters

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“…In any case, it is desirable that user's intervention, in terms of time and (manual) workload, be as limited as possible. Among learning-from-examples techniques, in which the problem of image segmentation can be reformulated as an optimization problem, genetic algorithms (GAs) [14,15] have been used for segmentation by Bhandarkar et al [16], who defined a multi-term cost function which is minimized using a GA-evolved edge configuration. An adaptive approach in which GAs are used to optimize the performances of the Phoenix segmentation algorithm [17] is described in [18].…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm-based interactive segmentation of 3D medical images

Cagnoni

Dobrzeniecki

Poli

et al. 1999

Image and Vision Computing

102

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This article describes a method for evolving adaptive procedures for the contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user first manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. Such contours are then used as training examples for a genetic algorithm to evolve a contour detector. By applying the detector to the rest of the image sequence it is possible to obtain a full segmentation of the structure. The same detector can then be used to segment other image sequences of the same sort. Segmentation is driven by a contour-tracking strategy that relies on an elastic-contour model whose parameters are also optimized by the genetic algorithm. We report results obtained on a software-generated phantom and on real tomographic images of different sorts. ᭧

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“…This scheme could be implemented by incorporating individual PDCs into higher level structures in a number of ways. There are many examples of genetic algorithm applications using novel structures (Bhandarkar et a!., 1994;Buckley & Hayashi, 1994;D'Angelo eta!., 1995;Hobbs, 1994;Potts eta!., 1994;Roth & Levine, 1994;Srikanth et al, 1995).…”

Section: Possible Alternative Driversmentioning

confidence: 99%

A genetic algorithm for designing optimal patch configurations in GIS

Brookes¹

2001

International Journal of Geographical Information Science

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Geographical Information Systems (GIS) are used for several types of spatial planning but so far they have not been used for optimal patch design. Optimal patch design is a generic spatial problem in which the objective is to design spatially explicit landuse maps when both the composition and configuration of patches are important criteria. There are many applications in conservation, forestry management, watershed management and the management of large military estates.This thesis describes a new autonomous computer program, the genetic algorithm for optimal patch design (GAPD). GAPD combines four components: a genetic search algorithm, a parameterised region growing (PRG) program, raster GIS measurement functions and multi-criteria decision-making methods. The key component is the PRG which translates between the aspatial domain of the search algorithm and the spatial domain of the GIS. GAPD generates landuse maps that optimise the configuration and composition of patches to meet multiple objectives for a given set of input maps and criteria.The theories of landscape ecology are used to establish a framework for formulating optimal patch design problems. The thesis describes the conceptual design ofGAPD and its implementation and test, first as a prototype for solving single patch problems and then as a fully functional system for solving multi-objective multi-patch problems. The feasibility of GAPD was established by investigations of issues concerning the representation and measurement of configuration in raster data structures and by testing the efficiency and effectiveness of GAPD with simple problems. GAPD was further evaluated in five hypothetical problems designed to cover a range of different scenarios.The results are promising and show that GAPD has potential as a decision support tool.The final section recommends a number of topics for further research covering technical developments of GAPD, the application of GAPD to real problems and investigations of general issues of optimal patch design. Other termsBoundary effects. Effects caused by the finite limits of rasters. A patch cannot grow outside the raster so patch shape is constrained near the raster boundary.Composition. The amount of different patches or elements within patches. Configuration.The spatial distribution and shape of patches.Core area. The inner area of a patch not affected by edge effects. Where edge effects are strong the core area is much smaller than total patch area.Edge effects. Ecological, and other, effects occurring at the edge of patches. Edge effects create a transition zone between the interior and exterior of patches.Effectiveness (of a search procedure). A measure of the quality of the solutions. A search is effective if it finds good solutions.Efficiency (of a search procedure). A measure of the quality of the process. A search is efficient if it finds good solutions with limited resources. More rigorously, efficiency is the ratio of the alternatives examined to the total number of alternatives in the sear...

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An edge detection technique using genetic algorithm-based optimization

Cited by 117 publications

References 20 publications

An edge detection technique using local smoothing and statistical hypothesis testing

An edge detection technique using local smoothing and statistical hypothesis testing

Genetic algorithm-based interactive segmentation of 3D medical images

A genetic algorithm for designing optimal patch configurations in GIS

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