The Third IEEE Workshop on Embedded Computer Vision (ECVW 2007) 

Program

8:45-9:00
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Organization / Announcements

9:00-10:15
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Keynote 
Dr. Wayne Wolf, Princeton University
(Details given at the end)

10:15-10:30
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Break

10:30-11:30
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Applications (chair: Dr. Mainak Sen)

Real-Time License Plate Recognition on an Embedded DSP-Platform, 
Clemens Arth, Florian Limberger, Horst Bischof

PrivacyCam: a Privacy Preserving Camera Using uCLinux on the Blackfin
DSP,
Ankur Chattopadhyay, Terry Boult

Real time planar surface segmentation in disparity space, 
Ninad Thakoor, Sungyong Jung, Jean Gao

11:30-12:30
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Architecture (chair: Dr. Will Plishker)

A Specialized Processor Suitable for AdaBoost-Based Detection with
Haar-like Features, Masayuki Hiromoto, Kentaro Nakahara, Hiroki Sugano,
Yukihiro Nakamura, Ryusuke Miyamoto

OpenVL: Towards A Novel Software Architecture for Computer Vision, 
Changsong Shen, James Little, Sidney Fels

Hardware implementation of an SAD based stereo vision algorithm,
Kristian Ambrosch, Martin Humenberger, Wilfried Kubinger, Andreas
Steininger

12:30-2:00
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Lunch

2:00-3:00
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Invited Speaker 
(Mark Dickinson, Director Systems Solutions Group (SSG), Altera
Corporation)

3:00-3:10
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Break

3:15-4:45
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Analysis

Multimodal Mean Adaptive Backgrounding for Embedded Real-Time Video
Surveillance, 
Scott Wills, Linda Wills, Senyo Apewokin, Brian Valentine, Antonio
Gentile

Robust Local Features and their Application in Self-Calibration and
Object Recognition on Embedded Systems, 
Clemens Arth, Christian Leistner, Horst Bischof 

A Human Action Recognition System for Embedded Computer Vision
Application, 
Hongying Meng, Nick Pears, Chris Bailey

Performance Benchmark of DSP and FPGA Implementations of Low-Level
Vision Algorithms, 
Daniel Baumgartner, Peter Roessler, Wilfried Kubinger

4:45-5:00
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Best Paper Award / Announcements

	 

Keynote     
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Dr. Wayne Wolf, Princeton University

Two Topics in Embedded Computer Vision: Markets and
Architectures


Abstract : This talk looks at the embedded computer vision field
from two sides. We first consider some of the commercial applications of
embedded computer vision based on the experience of Verificon
Corporation. The challenges presented by real-world systems range from
poor lighting to the expectations of customers. We will then move onto
consider the relationship between algorithms and architectures in
embedded computer vision. New generations of VLSI processors will
provide huge computational capabilities at very low cost. However,
algorithms must be properly adapted to make the best use of these
embedded platforms. We will introduce some current and future
system-on-chip platforms and consider what sorts of algorithms do and
don't work well on these devices.