Dr. Xiaoping Qi Profile

Dr. Xiaoping Qi

at Institute of Optics and Electronics CAS

SPIE Involvement:

Author

Publications (3)

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 | 19 February 2024 Paper

Detection of dim small targets in strong backgrounds based on local feature maximum difference

Tao Ma, Jinhao Xu, Xiaoping Qi, Jianlin Zhang

Proceedings Volume 13063, 130632H (2024) https://doi.org/10.1117/12.3021453

Read Abstract +

The essence of weak and small target detection is to achieve separation between the target and the background. However, for extremely low contrast (<0.1) and extremely low signal-to-noise ratio (<=3dB, most of which are less than 0dB) targets in strong backgrounds, the intensity difference between the target and the background is very small. However, in strong backgrounds, the fluctuation of the target submerges it and makes it difficult to directly separate it. Therefore, in order to achieve target detection in such extreme scenarios, it is necessary to construct feature descriptions that can effectively separate them. Based on this idea, this article summarizes the target detection task as a local feature difference maximization model suitable for all spatial target detection, and uses the local maximum Pearson correlation coefficient as the feature extraction equation to calculate the correlation between the two patches. Based on the small correlation between the target and the local background, and the high correlation between the background and the background, the separation of the target and the background is completed. Then using a constant local signal-to-noise ratio feature extraction equation to enhance the Pearson correlation results. A large number of experimental results show that the model and algorithm proposed in this paper can effectively detect targets with extremely low signal-to-noise ratios in strong backgrounds.

Proceedings Article | 30 November 2012 Paper

A method of contrast enhancement processing on wide-bits image

Xiang Peng, Zhiyong Xu, Xiaoping Qi

Proceedings Volume 8558, 85581S (2012) https://doi.org/10.1117/12.999430

KEYWORDS: Image enhancement, Image processing, Image contrast enhancement, RGB color model, Associative arrays, Image compression, Visualization, Cameras, Digital image processing, Data storage

Read Abstract +

In order to obtain high-quality images in a project, modern cameras with wide-bits (12, 14 or 16 bits width) are usually used to acquire enough original information. Nevertheless, some kinds of data selection and transform processing are necessary to display such images on PC (8 bits width). Besides, the output image should include both high general contrast and clear details. This paper proposed a method with two major steps: for the first step is on the basis of partially overlapped sub-block histogram equalization (POSHE), and change the way of equalizing sub-block image, separate each sub-block recursively with different gray ranges. The second step is taking a kind of pseudo-color processing based on HIS space to enhance the visual effects, so that the image has rich layers and consistent with human’s perception. Experimental results show that the algorithm could keep the local details and the mean of the original brightness at the same time, enhanced the image effectively. Considered the adaptability on different scenarios, different objectives, and a reasonable amount of time complexity, this method could adapt the requirements of practical engineering applications.

Proceedings Article | 8 October 2007 Paper

Image segmentation based on level set method

Yimin Ouyang, Xiaoping Qi, Qiheng Zhang

Proceedings Volume 6737, 67370R (2007) https://doi.org/10.1117/12.736602

KEYWORDS: Image segmentation, Electro optical modeling, Interfaces, Image processing algorithms and systems, Automatic tracking, Visual process modeling, Data modeling, Infrared technology, Numerical analysis, Electronics

Read Abstract +

In this paper, A segmentation model that combines techniques of curve evolution, the Mumford-Shah model and level set method was presented, to detect the contour of object in a given image, the model can detect object whose boundary is not necessarily defined by gradient and whose gray structure may be complicated. First we construct signed distance function, adopted a method which based on the times that is odd or even numbers through close curve from the point along a direction (if need, may be along several directions) to construct sign table. Then we used improved Mumford-Shah model to segment image, we consider that the object to be segmented is made up of some different gray level, it is difficult to detect the object contour using the Mumford-Shah model, for general objects, the contour of the object is piecewise-contour of along the edge, and the gray difference among the object points nearby the contour is little, so we divide the curve into finite segment, compute gray average of narrow band in and out of the curve, and compute the gray difference between the inner narrow band and outer narrow band of the curve, using improved Mumford-Shah model to segment the object. Experiment results show that the proposed algorithm can be used to segment object without edge and with complex gray structure, and the performance of the algorithm is satisfactory.

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