基于記憶分子動理論優(yōu)化算法的多目標截面投影Otsu圖像分割

肖樂意; 歐陽紅林; 范朝冬

doi:10.11999/JEIT170301

基于記憶分子動理論優(yōu)化算法的多目標截面投影Otsu圖像分割

doi: 10.11999/JEIT170301

1.
(湖南大學電氣與信息工程學院長沙 410082) ②(湘潭大學信息工程學院湘潭 411105)

基金項目:

國家自然科學基金(61573299)，湖南省自然科學基金(2016JJ3125)，湖南省教育廳科學研究項目(15C1327)

計量
- 文章訪問數(shù): 1480
- HTML全文瀏覽量: 204
- PDF下載量: 210
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-04-05
- 修回日期: 2017-08-28
- 刊出日期: 2018-01-19

Multi-objective Cross Section Projection Otsu's Method Based on Memory Knetic-molecular Theory Optimization Algorithm

1.
(College of Electrical and Information Engineering, Hunan University, Changsha 410082, China)
2.
(College of Information Engineering, Xiangtan University, Xiangtan 411105, China)

Funds:

The National Natural Science Foundation of China (61573299), The Natural Science Foundation of Hunan Province (2016JJ3125), The Foundation of Hunan Educational Committee (15C1327)

摘要

摘要: 傳統(tǒng)截面投影Otsu法后處理過程中的閾值Q為預先設定的常量，對含噪程度不同的圖像普適性較差。該文提出一種基于記憶分子動理論優(yōu)化算法的多目標截面投影Otsu法。該方法將閾值Q作為變量，結合分割閾值T，基于最大類間方差和最大峰值信噪比準則建立多目標圖像分割模型，以兼顧圖像分割的準確性和抗噪性；為免閾值增加而影響算法效率，將人工記憶原理引入分子動理論優(yōu)化算法，設計了一種基于記憶分子動理論優(yōu)化算法的多目標圖像分割模型求解方法。實驗表明：該方法分割準確、抗噪性強、魯棒性好，對含不同噪聲的圖像更具普適性。
- 圖像分割 /
- 最大類間方差 /
- 多目標優(yōu)化 /
- 分子動理論優(yōu)化算法 /
- 記憶原理
Abstract: The threshold value of Q in the post process of traditional cross section projection Otsus method is a constant, which is not universal applicability for images with different noises. To solve this problem, this paper proposes a multi-objective cross section projection Otsu's method based on memory knetic-molecular theory ptimization algorithm. Based on the maximum between-class variance criterion and the maximum Peak Signal to Noise Ratio (PSNR) criterion, a multi-objective image segmentation model is established to take into account the segmentation accuracy and anti-noise capability for image segmentation by combining threshold Q with segmentation threshold T. In order to improve the efficiency of the algorithm, a memory knetic-molecular theory optimization algorithm is proposed for the multi-objective cross section projection Otsu's method by introducing the artificial memory principles into knetic-molecular theory optimization algorithm. The experimental results show that this method has significant advantages in segmentation accuracy, anti-noise capability and robustness, and is more universal applicability for images with different noises.
- Image segmentation /
- Maximum between-class variance /
- Multi-objective optimization /
- Kinetic- molecular theory optimization algorithm /
- Memory principles

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