Mr. Ryouta Nakano Profile

Ryouta Nakano

at Univ of Electro-Communications

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 | 6 December 2005 Paper

Object detection using independent local feature extractor

Ryouta Nakano, Kazuhiro Hotta, Haruhisa Takahashi

Proceedings Volume 6051, 605106 (2005) https://doi.org/10.1117/12.647967

KEYWORDS: Independent component analysis, Target detection, Principal component analysis, Image filtering, Target recognition, Feature extraction, Sensors, Detector development, Automatic target recognition, Binary data

Read Abstract +

This paper presents an object detection method using independent local feature extractor. In general, it can be considered that objects are the combination of characteristic parts. Therefore, if local parts specialized for recognition target are obtained automatically from training samples, it is expected that good object detector is developed. For this purpose, we use Independent Component Analysis (ICA) which decomposes a signal into independent elementary signals. The basis vectors obtained by ICA are used as independent local feature extractors specified for detection target. The feature extractors are applied to candidate region, and their outputs are used in classification. However, the extracted features are independent local features. Therefore the relative information between neighboring positions of independent features may be more effective for object detection than simple independent features. To extract the relative information, higher order local autocorrelation features are used. To classify detection target and non-target, we use Support Vector Machine which is known as binary classifier. The proposed method is applied to car detection problem. Superior results are obtained by comparison with Principal Component Analysis.

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