Improvement of GrabCut form Kumamoto Castle Ishigaki region extraction method

2021 ◽  
Author(s):  
Yuuki Yamasaki ◽  
Masahiro Migita ◽  
Go Koutaki ◽  
Masashi Toda ◽  
Tsuyoshi Kishigami
Keyword(s):  
Extraction Method ◽  
Region Extraction

Underwater Image Transition Region Extraction and Segmentation Based on SVM

2013 ◽  
Vol 397-400 ◽  
pp. 2171-2176 ◽  
Author(s):  
Cong Ping Chen ◽  
Lei Zou ◽  
Wei Wang
Keyword(s):  
Support Vector Machine ◽  
Transition Region ◽  
Extraction Method ◽  
Support Vector ◽  
Gray Level ◽  
Local Difference ◽  
Underwater Image ◽  
Local Complexity ◽  
Region Extraction ◽  
Segmentation Threshold

By analyzing the gray level features of transition region, a new underwater image transition region extraction method based on Support Vector Machine (SVM) is presented. At first, a vector is constructed to fully describe the transition region, which includes local complexity, local difference and neighborhood homogeneity. Then, SVM is applied to train and classify the set of feature vectors, so that the transition region of the underwater image is extracted. Finally, the segmentation threshold is determined by mean of the histogram of the transition region, and the binary result was yielded. The experimental results show that the proposed algorithm can achieve a better transition region extraction and segmentation performance, and automatically select the optimal threshold for transition region extraction.

A region extraction method using a distance function between images

1986 ◽  
Vol 17 (5) ◽  
pp. 75-83
Author(s):  
Tomoharu Nagao ◽  
Takeshi Agui ◽  
Masayuki Nakajima
Keyword(s):  
Distance Function ◽  
Extraction Method ◽  
Region Extraction

scholarly journals Research on the Extraction Method of Book Number Region Based on Bayesian Optimization and Deep Learning

2021 ◽  
Vol 15 ◽  
pp. 1150-1158
Author(s):  
Qianqian Zhang ◽  
Jianglei Sun ◽  
Jing Zhao ◽  
Zilin Xia ◽  
Kai Zhang
Keyword(s):  
Extraction Method ◽  
Recognition Rate ◽  
Function Optimization ◽  
Identification Accuracy ◽  
Bayesian Optimization ◽  
Network Parameter ◽  
Network Training ◽  
Region Extraction ◽  
Artificial Intelligence Technology ◽  
Access To Books

The continuous development of artificial intelligence technology has promoted the construction of smart libraries and their intelligent services. In the process of intelligent access to books, the extraction of the requested book number region has become an important part of the process. The requested book number is generally affixed to the bottom of the spine of the book, which is small in size, and the height of the book is not always the same, so it’s difficult to identify. By the way, due to the images’ resolution, shooting angle and other practical problems, the difficulty of the extraction work will be increased. To improve the identification accuracy, in this paper, Bayesian Optimization (BO) and one kind of deep neural networks ‘Faster R-CNN’ are combined for the extraction work mentioned above. The data preparation, network training, optimization variable selection, establishment of BO objective function, optimization training, and network parameter evaluation have been introduced in detail. The performance of the designed algorithm has been tested with actual images of book spines taken in the academy library and compared with several other conventional recognition algorithms. The experimental results show that the requested book number region extraction method based on Bayesian optimization and deep neural network is effective and reliable, and its recognition rate can reach 91.82%, which has advantages in both recognition rate and extraction time compared with other algorithms.

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