Nonparametric Distribution Prior Model for Image Segmentation

Ming DAI; Zhiheng ZHOU; Tianlei WANG; Yongfan GUO

doi:10.1587/transinf.2019EDP7122

Nonparametric Distribution Prior Model for Image Segmentation

Ming DAI, Zhiheng ZHOU, Tianlei WANG, Yongfan GUO

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In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.2 pp.416-423

Publication Date: 2020/02/01

Publicized: 2019/10/21

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDP7122

Type of Manuscript: PAPER

Category: Image Processing and Video Processing

Authors

Ming DAI
  South China University of Technology
Zhiheng ZHOU
  South China University of Technology
Tianlei WANG
  Wuyi University
Yongfan GUO
  South China University of Technology

Keyword

level set method, active contour model, prior information, image segmentation

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Ming DAI, Zhiheng ZHOU, Tianlei WANG, Yongfan GUO, "Nonparametric Distribution Prior Model for Image Segmentation" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 2, pp. 416-423, February 2020, doi: 10.1587/transinf.2019EDP7122.
Abstract: In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7122/_p

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@ARTICLE{e103-d_2_416,
author={Ming DAI, Zhiheng ZHOU, Tianlei WANG, Yongfan GUO, },
journal={IEICE TRANSACTIONS on Information},
title={Nonparametric Distribution Prior Model for Image Segmentation},
year={2020},
volume={E103-D},
number={2},
pages={416-423},
abstract={In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.},
keywords={},
doi={10.1587/transinf.2019EDP7122},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - Nonparametric Distribution Prior Model for Image Segmentation
T2 - IEICE TRANSACTIONS on Information
SP - 416
EP - 423
AU - Ming DAI
AU - Zhiheng ZHOU
AU - Tianlei WANG
AU - Yongfan GUO
PY - 2020
DO - 10.1587/transinf.2019EDP7122
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2020
AB - In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.
ER -