Marked Point Process Model for Curvilinear Structures Extraction

Seong-Gyun Jeong (1) , Yuliya Tarabalka (2, 1) , Josiane Zerubia (1)

1 AYIN - Models of spatio-temporal structure for high-resolution image processing
2 TITANE - Geometric Modeling of 3D Environments

In this paper, we propose a new marked point process (MPP) model and the associated optimization technique to extract curvilinear structures. Given an image, we compute the intensity variance and rotated gradient magnitude along the line segment. We constrain high level shape priors of the line segments to obtain smoothly connected line configuration. The optimization technique consists of two steps to reduce the significance of the parameter selection in our MPP model. We employ Monte Carlo sampler with delayed rejection to collect line hypotheses over different parameter spaces. Then, we maximize the consensus among line detection results to reconstruct the most plausible curvilinear structures without parameter estimation process. Experimental results show that the algorithm effectively localizes curvilinear structures on a wide range of datasets.

Mots clés: curvilinear structure extraction; marked point process; Monte Carlo sampling with delayed rejection; aggregation algorithm

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Seong-Gyun Jeong, Yuliya Tarabalka, Josiane Zerubia. Marked Point Process Model for Curvilinear Structures Extraction. EMMCVPR 2015, Jan 2015, Hong Kong, Hong Kong SAR China. pp.436-449, ⟨10.1007/978-3-319-14612-6_32⟩. ⟨hal-01084939⟩