Parallel genetic algorithm based adaptive thresholding for image segmentation under uneven lighting conditions

Kanungo, P.; Nanda, Pradipta Kumar; Ghosh, Ashish

doi:10.1109/icsmc.2010.5642269

Cited by 16 publications

(14 citation statements)

References 17 publications

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“…Segmentation of real world scenes, that have uneven illumination condition, is challenging and has recently been addressed by the notion of adaptive thresholding [13] and Lorentz information measure. Adaptive thresholding schemes for uneven lighting conditions have been proposed by Pradhan et al [16] and a Parallel Genetic Algorithm based segmentation scheme has been proposed by Kanungo et al [19]. The proposed schemes have produced satisfactory results.…”

Section: Introductionmentioning

confidence: 79%

Adaptive windowing and granular computing based image segmentation

Srikumar

Wagh

Nanda

2011

2011 International Conference on Energy, Automation and Signal

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In this paper, adaptive windowing based segmentation scheme has been proposed. The image has been partitioned into different windows and the windows to be segmented, have been fixed by three criteria. The first one is based on pyramid approach where, the preselected windows are merged based on entropy measure. The second one is based on incremental window selection method. In the third criterion, the preselected windows are merged based on entropy measure. The windows thus fixed are considered as sub-images and each sub-image has been segmented based on the notion of rough entropy and granular computing. The algorithm could segment the images with uneven lighting condition.

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Section: Introductionmentioning

confidence: 79%

Adaptive windowing and granular computing based image segmentation

Srikumar

Wagh

Nanda

2011

2011 International Conference on Energy, Automation and Signal

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“…Since most segmentation methods set a specific detection environment, they are unsuitable for ores in complex scenes. OTSU [14], [15] based on statistical measurement is a classic global threshold segmentation, having the advantages of speediness, stabilization and high universality [16], [17]. Hua et al [18] obtained the aggregate size by using OTSU.…”

Section: International Journal On Informatics Visualization Vol 4 (20mentioning

confidence: 99%

“…In this paper Otsu's method is used for calculating the global threshold value. An optimal threshold (or set of thresholds) is selected by the discriminant criterion namely, by maximizing the discriminant measure q (or the measure of separability of the resultant classes in gray levels) [14], [15]. Here a global threshold T is computed from grayscale image.…”

Section: International Journal On Informatics Visualization Vol 4 (20mentioning

confidence: 99%

Image Processing Techniques on Radiological Images of Human Lungs Effected by COVID-19

Venkateswararao

2020

JOIV : Int. J. Inform. Visualization

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“…A good adaptive thresholding algorithm produce the same segmentation result for uneven lighting image as a global thresholding algorithm produce when applied to a perfectly evenly lighting image [9]. Image thresholding is still a relevant research field and a number of method has been proposed in literature to address the problem of uneven lighting condition [2,5,6,7,9]. Generally, local thresholding methods are considered superior to the global ones for uneven illumination condition.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation for Uneven Lighting Images based on Initial Window Size Selection

Saini¹

2016

IJCA

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Thresholding is a simple but effective technique for image segmentation. In this paper, a novel method is presented to address the problem of image segmentation for uneven lighting images that is based on dynamic size of window. In pyramid approach (window merging), segmentation accuracy depends on the initial size selection. The proposed method is based on the concept of window growing approach, in which after selecting the initial window, selection criterion is tested. If a sub-image or window does not satisfies the selection criterion, instead of merging with neighboring window (pyramid approach), window is incremented by small value. This process is repeated until it satisfies the given selection criterion. Thereafter segment the window by thresholding method. In the proposed method, initial size of window is computed at run time, which is based on image statistics. This method provides superior image segmentation over existing thresholding methods for images that are degraded, uneven illumination and suffer from the problem like shadow.

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Parallel genetic algorithm based adaptive thresholding for image segmentation under uneven lighting conditions

Cited by 16 publications

References 17 publications

Adaptive windowing and granular computing based image segmentation

Adaptive windowing and granular computing based image segmentation

Image Processing Techniques on Radiological Images of Human Lungs Effected by COVID-19

Image Segmentation for Uneven Lighting Images based on Initial Window Size Selection

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