Color laparoscopic image region segmentation after contrast enhancement including SRCNN by image regions

2021 ◽  
Author(s):  
Norifumi Kawabata ◽  
Toshiya Nakaguchi
Keyword(s):  
Contrast Enhancement ◽  
Image Region ◽  
Region Segmentation

Primary‐view perception on a gray image: Region segmentation and association

2001 ◽  
Vol 24 (2) ◽  
pp. 221-235 ◽  
Author(s):  
Hui‐Yu Huang ◽  
Yung‐Sheng Chen ◽  
Wen‐Hsing Hsu
Keyword(s):  
Image Region ◽  
Region Segmentation ◽  
Gray Image

Image Region Segmentation Based on Color Coherence Quantization

2011 ◽  
pp. 207-219 ◽  
Author(s):  
Guang-Nan He ◽  
Yu-Bin Yang ◽  
Yao Zhang ◽  
Yang Gao ◽  
Lin Shang
Keyword(s):  
Image Region ◽  
Region Segmentation

FPGA Implementation of Resistive-Fuse Networks for Coarse Image-Region Segmentation

2006 ◽  
Vol 12 (3) ◽  
pp. 307-316 ◽  
Author(s):  
Teppei Nakano ◽  
Takashi Morie ◽  
Hideaki Ishizu ◽  
Hiroshi Ando ◽  
Atsushi Iwata
Keyword(s):  
Fpga Implementation ◽  
Image Region ◽  
Region Segmentation

Keywords Assignment to Fixed Image Region Segmentations Using Fuzzy SVMs

2012 ◽  
Vol 429 ◽  
pp. 236-241 ◽  
Author(s):  
Ye Ji ◽  
Yan Chen ◽  
Yan Cao ◽  
Li Li Qu
Keyword(s):  
Single Image ◽  
Image Region ◽  
Region Segmentation ◽  
Generalization Ability ◽  
Fixed Region ◽  
Combined Feature ◽  
Better Than

The basic idea of this paper is multiple keywords can be assigned to image through the method of fixed region segmentation. We divide a single image into the 4-level regions. For each of them, the combined feature is extracted and inputted into the trained Fuzzy SVMs to classify, which has been proved better than conventional SVMs in the generalization ability. The values of classification in each category are calculated. Based on these values, the keywords are assigned.

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