Dr. Mei-Li Shen Profile

Dr. Mei-Li Shen

at Changchun Univ of Science and Technology

SPIE Involvement:

Author

Publications (2)

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 | 20 January 2006 Paper

Target track system design based on circular projection

Hua-Jun Song, Ming Zhu, Shuo Hu, Mei-Li Shen

Proceedings Volume 6027, 60272Q (2006) https://doi.org/10.1117/12.668298

KEYWORDS: Digital signal processing, Detection and tracking algorithms, Image processing, Signal processing, Field programmable gate arrays, Image analysis, Target detection, Target recognition, Data processing, Data communications

Read Abstract +

Template matching is the process of searching the present and the location of a reference image or an object in a scene image. Template matching is a classical problem in a scene analysis: given a reference image of an object, decide whether that object exists in a scene image under analysis, and find its location if it does. The template matching process involves cross-correlating the template with the scene image and computing a measure of similarity between them to determine the displacement. The conventional matching method used the spatial cross-correlation process which is computationally expensive. Some algorithms are proposed for this speed problem, such as pyramid algorithm, but it still can't reach the real-time for bigger model image. Moreover, the cross-correlation algorithm can't be effective when the object in the image is rotated. Therefore, the conventional algorithms can't be used for practical purpose. In this paper, an algorithm for a rotation invariant template matching method based on different value circular projection target tracking algorithm is proposed. This algorithm projects the model image as circular and gets the radius and the sum of the same radius pixel value. The sum of the same radius pixel value is invariable for the same image and the any rotated angle image. Therefore, this algorithm has the rotation invariant property. In order to improve the matching speed and get the illumination invariance, the different value method is combined with circular projection algorithm. This method computes the different value between model image radius pixel sum and the scene image radius pixel sum so that it gets the matching result. The pyramid algorithm also is been applied in order to improve the matching speed. The high speed hardware system also is been design in order to meet the real time requirement of target tracking system. The results show that this system has the good rotate invariance and real-time property.

Proceedings Article | 20 January 2006 Paper

Study on urinary sediments classification and identification techniques

Mei-li Shen, Dian-ren Chen

Proceedings Volume 6027, 60271A (2006) https://doi.org/10.1117/12.667944

KEYWORDS: Image filtering, Image processing, Image segmentation, Microscopes, Image classification, Feature extraction, Quantitative analysis, Databases, CCD cameras, Diagnostics

Read Abstract +

In this paper, a kind of computer microscopic urine sediment analyzer is introduced with industry computer as image processor and controller. The system categorizes and recognizes the visible urine sediment components based on the technology of image processing and support vector machine (SVM). Firstly, microscope enlarges the visible components in the urine sediment quantitative analysis board. Then, light signals is transformed as video electrical signals by CCD camera and the image sampling board samples and saves it as files. The system preprocessing the sampled image using different methods including color image transformed gray image, filtering, image sharpening, image enhancing, segmenting visible component, edge tracking and repairing and so on. Moreover, sampled image feature is extracted, trained and classified. Using support vector machine method classifies and counts the urine sediment visible components and gets the number in the unit volume. The system not only realizes urine sediment visible components classifying and recognition, but also describes its feature from morphology. The SVM trains those features and cross validation in order to get the optimal SVM kernel function and parameters. In the end, it classifies tested image according to the model. Experimental results show that this method is provided with the characteristics of method directness, strong robustness and good stability.

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