Mr. Kai Ma Profile

Kai Ma

at Yunnan Normal Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 10 October 2020 Poster + Presentation + Paper

Colorimetric characterization of digital camera based on RBF neural network

Kai Ma, Junsheng Shi

Proceedings Volume 11550, 1155019 (2020) https://doi.org/10.1117/12.2574973

KEYWORDS: Neural networks, Digital cameras, Color difference, Cameras, Digital imaging, Color reproduction, Data processing, Artificial neural networks

Read Abstract +

Modern chromatic equipment has been widely used. With the popularity of color digital equipment, accurate acquisition of color has a wide range of uses. The colorimetric characterization of equipment is the basic link of color management system, and how to accurately convert the color of various color devices has become a basic problem. The color space of the camera depends on the equipment. Therefore, the colorimetric characterization of digital camera is an important method to improve the color reproduction of images, which is the basis of color conversion between various devices. In the process of camera colorimetric characterization, the traditional neural network method, such as Back propagation(BP) neural network, needs a large number of sample data to obtain a large number of data, and the processing is complex. Because of its fast training speed, small amount of data and small color difference, RBF neural network can be used to solve the problem of colorimetric characterization of digital camera. On the 140 color blocks, half is used as training data set and half as test data set. In the first part, RBF neural network is used to train the data set, and the second part is used to experiment with the traditional BP neural network under the same data set. The experimental results show that the average color difference of training samples is 1.79ΔΕ CMC(1:1), the average color difference of the test sample is 4.89 ΔΕ. Compared with the traditional polynomial fitting method and BP network, RBF neural network has smaller color difference in colorimetric characterization.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years