Feature based transition region extraction for image segmentation: Application to worm separation from leaves

Parida, Priyadarsan; Bhoi, Nilamani

doi:10.1016/j.fcij.2018.08.001

Cited by 2 publications

(3 citation statements)

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“…(iv) Proposed method 4: The Proposed method 4 [9] also utilizes the wavelet transform along with uses local standard deviation and Otsu thresholding for transition region extraction. The method suppresses the texture and extracts an effectively continuous transition region achieving better segmentation result.…”

Section: Contributionsmentioning

confidence: 99%

“…(ii) Proposed method 7: The Proposed method 7 [9] is an extension of Proposed method 4 processed in CIE-Lab colour space. The method is applied to one agricultural application i.e.…”

Section: Colour Image Transition Region Based Segmentation Approachesmentioning

confidence: 99%

“…Priyadarsan Parida / Electronic Letters on Computer Vision and Image Analysis 18(2):[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] 2019 …”

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Development of transition region based methods for image segmentation

Parida

2020

ELCVIA

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In this thesis, some transition region based segmentation approaches have developed to perform image segmentation for grayscale and colour images.In computer vision and image understanding applications, image segmentation is an important pre-processing step. The main goal of the segmentation process is the separation of foreground region from background region. The segmentation approaches are application specific and do not work well for both grayscale and colour image segmentation. For any image consisting of foreground and background, some transition regions exist between the foreground and background regions. Effective extraction of transition region leads to a better segmentation image segmentation for both grayscale and colour images.

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Section: Contributionsmentioning

confidence: 99%

“…(ii) Proposed method 7: The Proposed method 7 [9] is an extension of Proposed method 4 processed in CIE-Lab colour space. The method is applied to one agricultural application i.e.…”

Section: Colour Image Transition Region Based Segmentation Approachesmentioning

confidence: 99%