基于马尔可夫随机场的闪光图像分割
wf(2016)
Abstract
目标界面位置信息是闪光照相中关注的内容之一,而闪光图像的低信噪比影响了微结构界面位置的准确提取。研究了基于马尔可夫随机场的闪光图像分割算法,在闪光图像分割过程中采用马尔可夫模型描述被分割像素之间的相关性,减少了由噪声所引起的孤立虚假目标,提出利用中空邻域模板内的起伏定义标号场模型中的基团势函数,改进了闪光图像的分割方法,提高了微结构分割精度。数值实验表明,改进后的马尔可夫随机场分割方法能取得更好的分割结果。
MoreTranslated text
Key words
image segmentation,flash X-ray radiography,Markov random field
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined