A novel VLSI architecture to implement region merging algorithm for image segmentation

2004 ◽  
Author(s):  
J.D. Kranthi Kumar ◽  
S. Srinivasan
Keyword(s):  
Image Segmentation ◽  
Vlsi Architecture ◽  
Region Merging ◽  
Merging Algorithm

Fast Pixel-Superpixel Region Merging for SAR Image Segmentation

2020 ◽  
pp. 1-17
Author(s):  
Deliang Xiang ◽  
Fan Zhang ◽  
Wei Zhang ◽  
Tao Tang ◽  
Dongdong Guan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Region Merging ◽  
Sar Image Segmentation

FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES

2021 ◽  
pp. 2150012
Author(s):  
Kuo-Lung Lor ◽  
Chung-Ming Chen
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Linear Time ◽  
Mean Shift ◽  
Texture Features ◽  
Color Image Segmentation ◽  
Image Size ◽  
Region Merging ◽  
Tissue Segmentation ◽  
Regional Pattern

The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.

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