Ms. Huimin Ma Profile

Huimin Ma

at Tsinghua Univ

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 | 6 May 2019 Paper

Road mark detection based on local point cloud projection and R-FCN

Tianxiong Lv, Huimin Ma

Proceedings Volume 11069, 110693S (2019) https://doi.org/10.1117/12.2524376

KEYWORDS: Roads, 3D image processing, Image segmentation, LIDAR, 3D image restoration

Read Abstract +

For the road mark in the presence of abrasion, adhesion, and occlusion, it is difficult to accurately locate the road mark by typical point cloud clustering and segmentation method. An automatic road mark detection method based on local point cloud projection and 2D deep learning object detection is proposed. Firstly, the local ground point cloud regions are obtained by prior information; Secondly, we perform orthogonal projection on the point cloud to project the point cloud into two-dimensional image by affine transformation and use R-FCN detection method to detect the road mark in the image. The holes on the image are filtered by local maximum filtering. A line-by-line search strategy based on maximum reflection rate is proposed to refine the detected bounding boxes to improve the detection accuracy; Finally, we implement inverse affine transformation and local coordinate search to restore the road mark coordinates from twodimensional image to 3D point cloud. In the point cloud dataset collected from China highway, our experimental results show that the proposed method can achieve road mark detection under complex scenes such as occlusion, abrasion and adhesion. Comparing with other methods, the detection recall, accuracy and efficiency have been improved greatly.

Proceedings Article | 6 July 2015 Paper

The analysis of frequency domain characteristics of emotional images in eye-tracking experiment

Boqiang Fan, Huimin Ma, Xiang Wang

Proceedings Volume 9631, 96311O (2015) https://doi.org/10.1117/12.2196909

KEYWORDS: Analytical research, Machine vision, Computer vision technology, Eye, Human vision and color perception, Diagnostics, Image analysis, Statistical analysis, Image processing, Error analysis

Read Abstract +

Although recently eye-tracking method has been introduced into behavioral experiments based on dot-probe paradigm, some characteristics in eye-tracking data do not draw as much attention as traditional characteristics like reaction time. It is also necessary to associate eye-tracking data to characteristics of images shown in experiments. In this research, new variables, such as fixation length, times of fixation and times of eye movement, in eye-tracking data were extracted from a behavioral experiment based on dot probe paradigm. They were analyzed and compared to traditional reaction time. After the analysis of positive and negative scenery images, parameters such as hue frequency spectrum PAR (Peak to Average Ratio) were extracted and showed difference between negative and positive images. These parameters of emotional images could discriminate scenery images according to their emotions in an SVM classifier well. Besides, it was found that images’ hue frequency spectrum PAR is obviously relevant to eye-tracking statistics. When the dot was on the negative side, negative images’ hue frequency spectrum PAR and horizontal eye-jumps confirmed to hyperbolic distribution, while that of positive images was linear with horizontal eye-jumps. The result could help to explain the mechanism of human’s attention and boost the study in computer vision.

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