Dr. Yoram Swirski Profile

Dr. Yoram Swirski

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 | 8 December 2004 Paper

Recognition of tanks using laser radar (LADAR) images

Haim Garten, Yoram Tal, Yoram Swirski, Amir Imber

Proceedings Volume 5613, (2004) https://doi.org/10.1117/12.577831

KEYWORDS: LIDAR, Image segmentation, 3D image processing, Sensors, Automatic target recognition, Detection and tracking algorithms, Target recognition, Feature extraction, 3D acquisition, Fuzzy logic

Read Abstract +

Three-dimensional sensors based on Laser Radar (LADAR) technology possess vast potential for the future battlefield. This work presents an algorithm for the recognition of T62 and T72 tanks from 3D imagery. The algorithm consists of several stages: a) Pre-processing of LADAR images to remove range noise and to determine ground level. b) Segmentation to extract regions that fulfill certain pre-defined conditions. c) Extraction of specific tank features from each region. d) Applying a Fuzzy Logic classifier on the feature vector to discriminate between T62 or T72 tanks and other type of targets or natural clutter. A commercial airborne LADAR sensor was used to acquire images from an area of 40 square kilometers with a measurement density of 20 pixels per square meter and a range noise of 15 cm (1 sigma). The images included more than a hundred man-made objects (tanks, armored personnel carriers, trucks, cranes)along with natural clutter (vegetation and boulders). Among the targets were 18 tanks, two of which were covered with a camouflage net. The algorithm recognized the 16 uncovered tanks with a False Alarm Rate (FAR) of 0.025 per square kilometer. This FAR value is better than the respective FAR values derived for 2D Imaging where Automatic Target Recognition (ATR) techniques are applied. These results show promise for automatic recognition of various targets employing LADAR sensors.

Proceedings Article | 2 November 2004 Paper

Detection and delineation of buildings from airborne ladar measurements

Yoram Swirski, Karni Wolowelsky, Renen Adar, Zvi Figov

Proceedings Volume 5558, (2004) https://doi.org/10.1117/12.557642

KEYWORDS: Buildings, LIDAR, Orthophoto maps, Image segmentation, Data modeling, Vegetation, Defense technologies, Image processing algorithms and systems, Data fusion, 3D image processing

Read Abstract +

Automatic delineation of buildings is very attractive for both civilian and military applications. Such applications include general mapping, detection of unauthorized constructions, change detection, etc. For military applications, high demand exists for accurate building change updates, covering large areas, and over short time periods. We present two algorithms coupled together. The height image algorithm is a fast coarse algorithm operating on large areas. This algorithm is capable of defining blocks of buildings and regions of interest. The point-cloud algorithm is a fine, 3D-based, accurate algorithm for building delineation. Since buildings may be separated by alleys, whose width is similar or narrower than the LADAR resolution, the height image algorithm marks those crowded buildings as a single object. The point-cloud algorithm separates and accurately delineates individual building boundaries and building sub-sections utilizing roof shape analysis in 3D. Our focus is on the ability to cover large areas with accuracy and high rejection of non-building objects, like trees. We report a very good detection performance with only few misses and false alarms. It is believed that LADAR measurements, coupled with good segmentation algorithms, may replace older systems and methods that require considerable manual work for such applications.

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