Wavelet based transition region extraction for image segmentation

Parida, Priyadarsan; Bhoi, Nilamani

doi:10.1016/j.fcij.2017.10.005

Cited by 27 publications

(13 citation statements)

References 24 publications

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“…(iii) Proposed method 3: The Proposed method 3 [8] is a hybridization of Proposed method 1 and Proposed method 2 in the transform domain. For suppressing the background textures, the wavelet transform is utilized in Proposed method 3.…”

Section: Contributionsmentioning

confidence: 99%

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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%

“…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

Self Cite

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“…It is an integrated application of Multi-Scale Retinex with color restoration at different brightness levels, using Perspective Transform to get the front view of image taken from arbitrary camera angle, and using Hough Transform to determine the starting scale mark and ending scale mark. Methodologically, tick-mark recognition can be divided two steps as follows: 1) Image Segmentation Segmentation is a classical algorithm for image recognition mainly including threshold image segmentation [3] [4], region image segmentation [5] [6], edge image segmentation [7] [8] etc. As tick-marks have several properties as followings: symmetrically distributed on the edge of instruments, fixed distance between top of tick-marks and instrument center and fixed contrast ratio of tickmarks and their background, threshold image segmentation is relatively suitable for tick-mark recognition.…”

Section: Introductionmentioning

confidence: 99%

Double Image Segmentation Algorithm for Tick-Mark Recognition of Analog Instrument

Liu¹,

Zhou²,

Xie³

2021

WJET

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Tick-mark recognition is a key step for automatic reading of analog instrument with computer vision techniques which is called for in many automatic processes among industries. As many factors result in the tick-mark distortion in the sampled image such as the generally stained surface of instrument, or/and reflection and refraction of light, the accuracy and robustness of tick-mark reading are directly or indirectly weakened. Double image segmentation algorithm (DISA) is employed to solve this problem which makes coarse segmentation with structural parameters of instruments, and makes accurate segmentation with gray difference between tick-marks and their background. Tests have been conducted with 640 * 480 pixel industry camera and 4 kinds of pointer pressure meter, and the results show that read-out rate of tick-mark in pointer meter reached 100% at sufficient illumination and 76% at insufficient illumination for air packed instrument. In addition with oil packed instrument, DISA still experienced 64% recognition in comparison with 0 success record by Hough Transformation.

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“…The coefficients are used to extract a transition region-based feature matrix. An inverse wavelet transform is applied to the modified coefficients to obtain an edge image, which has more than one-pixel width [3]. Traditionally, the Otsu threshold is used to obtain the transition region; the wavelet domain method is used to extract a stable transition region, resulting in better segmentation.…”

Section: Introductionmentioning

confidence: 99%

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

Wang

Dey

et al. 2020

Biocybernetics and Biomedical Engineering

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Comfort shoe-last design relies on the key points of last curvature. Traditional plantar pressure image segmentation methods are limited to their local and global minimization issues. In this work, an improved fully convolutional networks (FCN) employing SegNet (SegNet+FCN 8s) is proposed. The algorithm design and operation are performed using the visual geometry group (VGG). The method has high efficiency for the segmentation in positive indices of global accuracy (0.8105), average accuracy (0.8015), and negative indices of average cross-ratio (0.6110) and boundary F1 index (0.6200). The research has potential applications in improving the comfort of shoes.

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Wavelet based transition region extraction for image segmentation

Cited by 27 publications

References 24 publications

Development of transition region based methods for image segmentation

Development of transition region based methods for image segmentation

Double Image Segmentation Algorithm for Tick-Mark Recognition of Analog Instrument

Deep-segmentation of plantar pressure images incorporating fully convolutional neural networks

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