An adaptive multi-feature segmentation model for infrared image

作者：Zhang, Tingting; Han, Jin; Zhang, Yi; Bai, Lianfa^*

来源：Optical Review, 2016, 23(2): 220-230.

DOI：10.1007/s10043-016-0190-1

• AMFS
• SPF
• MFSPF
• Local statistic feature
• Adaptive weight coefficient
• Level set formulation

Active contour models (ACM) have been extensively applied to image segmentation, conventional region-based active contour models only utilize global or local single feature information to minimize the energy functional to drive the contour evolution. Considering the limitations of original ACMs, an adaptive multi-feature segmentation model is proposed to handle infrared images with blurred boundaries and low contrast. In the proposed model, several essential local statistic features are introduced to construct a multi-feature signed pressure MFSPF). In addition, we draw upon the adaptive weight coefficient to modify the level set formulation, which is formed by integrating MFSPF with local statistic features and signed pressure function with global information. Experimental results demonstrate that the proposed method can make up for the inadequacy of the original method and get desirable results in segmenting infrared images.

• 出版日期2016-4
• 单位南京理工大学