Region-based Markov Random Field Research Articles

Performance of Region-Based Markov Random Field with XY Spins

Markov Random Field (MRF) models have become increasingly necessary especially in data-driven science. There are two kinds of MRF model applicable to image segmentation: edge-based and region-based...

Fine Building Segmentation in High-Resolution SAR Images Via Selective Pyramid Dilated Network

The building extraction from synthetic aperture radar (SAR) images has always been a challenging research topic. Recently, the deep convolution neural network brings excellent improvements in SAR segmentation. The fully convolutional network and other variants are widely transferred to the SAR studies because of their high precision in optical images. They are still limited by their processing in terms of the geometric distortion of buildings, the variability of building structures, and scattering interference between adjacent targets in the SAR images. In this article, a unified framework called selective spatial pyramid dilated (SSPD) network is proposed for the fine building segmentation in SAR images. First, we propose a novel encoder–decoder structure for the fine building feature reconstruction. The enhanced encoder and the dual-stage decoder, composed of the CBM and the SSPD module, extract and recover the crucial multiscale information better. Second, we design the multilayer SSPD module based on the selective spatial attention. The multiscale building information with different attention on multiple branches is combined, optimized, and adaptively selected for adaptive filtering and extracting features of complex multiscale building targets in SAR images. Third, according to the building features and SAR imaging mechanism, a new loss function called L-shape weighting loss (LWloss) is proposed to heighten the attention on the L-shape footprint characteristics of the buildings and reduce the missing detection of line buildings. Besides, LWloss can also alleviate the class imbalance problem in the optimization stage. Finally, the experiments on a large-scene SAR image dataset demonstrate the effectiveness of the proposed method and verify its superiority over other approaches, such as the region-based Markov random field, U-net, and DeepLabv3+.

Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.

A Region-based Coupled MRF Model for Coarse Image Region Segmentation toward its VLSI Implementation

Coupled Markov random field (MRF) models have been proposed so far for visual image processing. These are classified into boundary- and region-based models, where hidden variables that determine the interaction between the units corresponding to image pixels are given by line and label processes, respectively. In this paper, we have investigated a region-based coupled MRF model with phase variables as the hidden variables, and have modified the model for applications to coarse image-region segmentation by replacing some nonlinear functions in the update equations of the intensity and label processes with piecewise linear (PWL) functions. Using PWL functions facilitates VLSI implementation of coupled MRF models, and also makes their performance improved. We have verified that the modified region-based MRF model is superior to the resistive-fuse network, which is one of the boundary-based MRF models. We also propose an improvement of the modified region-based MRF model by introducing a new parameter, and evaluate the model performance in coarse image region segmentation.