Hybrid Structural and Texture Distinctiveness Vector Field Convolution for Region Segmentation

The segmentation of objects has been an area of interest in numerous fields. The use of texture has been explored to improve convergence in the presence of cluttered backgrounds or objects with distinct textures, where intensity variations are insufficient. Additionally, saliency and feature maps have been applied for contour initialization. However, taking advantage of texture to improve initialization and convergence has not been extensively explored. To address this, we propose a hybrid structural and texture distinctiveness vector field convolution (STVFC) approach, where both the structural characteristics and the concept of texture distinctiveness are incorporated into a multi-functional vector field convolution (VFC) model. In this novel approach, texture distinctiveness is used to enable automatic initialization and is incorporated with intensity variation to improve and accelerate convergence towards the object boundary. Experiments using three public datasets, containing natural images and Brodatz textures, demonstrated that STVFC achieved better or comparable segmentation accuracy.

The overall architecture of the proposed hybrid structural and texture-guided vector field convolution approach

The overall architecture of the proposed hybrid structural and texture-guided vector field convolution approach (STVFC) which can be broken down into four key steps. 1) Sparse texture model learning, 2) statistical texture distinctiveness modeling, 3) multi-functional vector field convolution, and 4) texture-guided contour initialization.

Code

If you are interested in our software for segmenting regions based on hybrid structural and texture distinctiveness vector field convolution (STVFC), we would be happy to share demo code (MATLAB) for research purposes only
- coming soon -