Mr. Suha Kose Profile

Suha Kose

at Ecole Polytechnique Fédérale de Lausanne

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 | 9 October 2018 Presentation + Paper

Target detection with deep learning in polarimetric imaging

Süha Kağan Köse, Selman Ergünay, Beat Ott, Peter Wellig, Yusuf Leblebici

Proceedings Volume 10794, 107940O (2018) https://doi.org/10.1117/12.2325358

KEYWORDS: Polarimetry, Polarization, Systems modeling, Performance modeling, Data modeling, Cameras, Polarizers, Target detection, Optical filters, Machine learning

Read Abstract +

Polarimetric imaging techniques demonstrate enhanced capabilities in advanced object detection tasks with their capability to discriminate man-made objects from natural background surfaces. While spectral signatures carry information only about material properties, the polarization state of an optical field contains information related to surface features of objects, such as, shape and roughness. With these additional benefits, polarimetric imaging reveal physical properties operable for advanced object detection tasks which are not possible to acquire by using conventional imaging. In this work, the primary objective is to utilize the state-of-the-art deep learning models designed for object detection tasks using images obtained by polarimetric systems. In order to train deep learning models, it is necessary to have a sufficiently large dataset consisting of polarimetric images with various classes of objects in them. We started by constructing such dataset with adequate number of visual and infrared (SWIR) polarimetric images obtained using polarimetric imaging systems and masking relevant parts for object detection models. We managed to achieve a high performance score while detecting vehicles with metallic surfaces using polarimetric imaging. Even with limited number of training samples, polarimetric imaging demonstrated superior performance comparing to models trained using conventional imaging techniques. We observed that using models trained with both polarimetric and conventional imaging techniques in parallel gives the best performance score since these models were able to compensate for each other's lacking points. In the subsequent stages, we plan to expand the study to the application of spiking neural network (SNN) architectures for implementing the detection/classification tasks.

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