Mr. Zhenxiang He Profile

Zhenxiang He

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 | 29 December 2022 Open Access

An attention directed generative adversarial network for retinal vessel segmentation

Zhenxiang He, Nianzu Lv, Wei Sun

Proceedings Volume 12506, 125063B (2022) https://doi.org/10.1117/12.2661840

KEYWORDS: Image segmentation, Image enhancement, Blood vessels, Image processing algorithms and systems, Selenium, Convolution, Image processing, Gold, Binary data, Machine learning

Read Abstract +

Observing the structure of retinal blood vessels can help doctors diagnose the disease of patients, so the accurate segmentation of retinal blood vessels has important research significance. However, there are many problems in retinal vessel segmentation, such as complex and small vascular structure and low image contrast, which lead to low segmentation accuracy. To solve the above problems, this paper proposes an attention-directed adversarial network for retinal vascular segmentation. The purpose is to guide the network to learn useful information for vascular segmentation and ignore useless redundant information. The attention directed generative adversarial networks consist of generator and discriminator. The generator uses U-Net architecture and combines the high-low feature attention modules. The high-low level feature attention modules act on the high-level and low-level feature maps so that the model can strengthen the high-level and low-level spatial features respectively, eliminate redundant information, and guide the model to pay more attention to the vascular foreground information. The batch normalization layer in the generator is also removed to avoid the impact of unstable batch statistics on the segmentation results when the generator is trained in small batches. The discriminator consists of a stack of residual modules, which together with the generator form a conditional generation adversarial network. The experimental results show that the Se, Sp, Acc and AUC of this paper’s method tested on the DRIVE dataset are 82.88%, 97.45%, 95.59%, and 97.86%, respectively, and all the indexes are better than the current mainstream retinal vessel segmentation algorithms.

DOWNLOAD PAPER

Proceedings Article | 29 December 2022 Open Access

Person re-identification method based on multi-scale residual pooling

Zhenxiang He, Xiaorong Liu, He Zhu

Proceedings Volume 12506, 125066I (2022) https://doi.org/10.1117/12.2661830

KEYWORDS: Feature extraction, Cameras, Convolutional neural networks, Image fusion, Convolution, Visualization, Contrast transfer function, Optimization (mathematics), Video

Read Abstract +

Person re-identification is a cross camera pedestrian retrieval problem. The data retrieved by pedestrians can be images, videos, and text. The current person re-identification methods are insufficient in expression of pedestrian features and poor robustness, resulting in low model accuracy. This paper proposes a Multi-scale Residual Pooling for person re-identification. ResNet50 is used as the basic network to obtain the multi-scale features. Global average pooling and maximum average pooling are performed on the input features at different network levels. Each group of average pooled and maximum pooled features is subtracted to remove the influence of image background clutter. The subtracted difference features are added to the maximum pooled features to obtain a more discriminative residual pooled fusion feature, making the network focus on the whole body contour of pedestrians and the difference between pedestrians and background. On this basis, triplet loss and cross-entropy loss are combined to optimize the model, and reordering technology is used to optimize the network. The experimental results showed that the Rank1 index of this paper’s method tested on the Market1501 and Duke MTMC-reID datasets reaches 96.41% and 91.43%, respectively, and mAP (mean Average Precision) reaches 94.52% and 89.30%, respectively. which is better than the current mainstream algorithms.

DOWNLOAD PAPER

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