Parameterized ZNCC Image Correlation for Rotary-Angle Measurement
MEASUREMENT SCIENCE AND TECHNOLOGY(2025)
Sun Yat Sen Univ
Abstract
High-accuracy rotary-angle measurement is critical for precise positioning and control of robots, machine arms, and machine tools in industry. Image-based measurement is a very promising technology for angle positioning owing to its simple system, low cost, and good robustness to environments. Image correlation, such as the zero-mean normalized cross correlation (ZNCC) method, can obtain high accuracy image-positioning results, but their efficiency is too low to directly use in industries. This paper proposes to measure the absolute rotary angle using a parameterized ZNCC (PZNCC) image correlation method. The PZNCC expresses the ZNCC coefficient by the image-shift parameters explicitly, so that the precise match point corresponding to the global ZNCC maximum can be efficiently obtained and no image-resampling process is required. As a result, PZNCC is approximately 5 times faster than the classic inverse compositional Gauss-Newton algorithm. Experiments show that rotary angle measurement based on PZNCC can obtain absolute angle positions with an accuracy of 1.16 arcsec (RMSE).
MoreTranslated text
Key words
rotary angle measurement,image-based measurement,image correlation,normalized cross-correlation
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