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The XML...

<page>
<title>
<text>BYU ECEn Department: Robotic Vision Lab</text>
</title>
<name>Robotic Vision Lab</name>
<now-showing-tab>Main</now-showing-tab>
<style>main page</style>
<tabs>
<tab>
<name>Main</name>
<related-page>index.phtml</related-page>
</tab>
<tab>
<name>Robotic Vision</name>
<related-page>robotic_vision.phtml</related-page>
</tab>
<tab>
<name>Machine Vision</name>
<related-page>machine_vision.phtml</related-page>
</tab>
<tab>
<name>Medical Imaging</name>
<related-page>bio_medical.phtml</related-page>
</tab>
<tab>
<name>Class Projects</name>
<related-page>class_projects.phtml</related-page>
</tab>
</tabs>
<title-bar-links>
<link>
<related-page>http://www.ee.byu.edu/contact/</related-page>
<name>Contact</name>
</link>
<link>
<related-page>http://www.ee.byu.edu/help</related-page>
<name>Help</name>
</link>
</title-bar-links>
<features>
<feature>
<highlight-picture-file>Support_Info//images/rvlab2c.JPG</highlight-picture-file>
<content>
<picture>
<file>Support_Info/images/ecen_logo_halfsize.jpg</file>
<alternate-text>BYU - Electrical and Computer Engineering</alternate-text>
<style>positioned left</style>
<style>text wraps</style>
</picture>
<line-break />
<special-text>
<style>feature title</style>
<text>Robotic Vision Lab</text>
</special-text>
<line-break />
<line-break />
<special-text>
<style>feature sub-title</style>
<text>Research Lab</text>
</special-text>
<line-break />
<line-break />
<text>Department of Electrical and Computer Engineering</text>
<line-break />
<text>Room 250 B34</text>
<line-break />
<text>Provo, Utah 84602</text>
<line-break />
<text>Tel: 1 + 801-422-4119</text>
</content>
</feature>
</features>
<menu>
<style>main menu</style>
<sub-menu>
<name>Recent Projects</name>
<item>
<name>SYBA Descriptor</name>
<related-page>Robotic_Vision/SYBA/SYBAFeature.html</related-page>
</item>
<item>
<name>ECO Features</name>
<related-page>Machine_Vision/ECOFeatures/ECOFeatures.html</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Faculty</name>
<item>
<name>Dr. D.J. Lee</name>
<related-page>http://www.ee.byu.edu/faculty/djlee/</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Visiting Researchers</name>
<item>
<name>Visiting Researchers</name>
<related-page>Support_Info/Students/research.html</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Students</name>
<item>
<name>Graduate Students</name>
<related-page>Support_Info/Students/grad.html</related-page>
</item>
<item>
<name>Undergraduate Students</name>
<related-page>Support_Info/Students/under.html</related-page>
</item>
</sub-menu>
<item>
<name>University Home</name>
<related-page>http://www.byu.edu/</related-page>
</item>
<item>
<name>College Home</name>
<related-page>http://www.et.byu.edu/</related-page>
</item>
<item>
<name>Department Home</name>
<related-page>http://www.ece.byu.edu/</related-page>
</item>
</menu>
<news>
<title>
<text>Robotic Vision Lab</text>
</title>
<article>
<title>
<text>What there is to see</text>
</title>
<highlight-picture-file>Class_Projects/IGVC/images/Y_Clops.jpg</highlight-picture-file>
<content>
<text> The Robotic Vision Lab endeavors to reach new frontiers in the field of robotic vision, through building a strong foundation of essential basics to both graduate and undergraduate students while encouraging creativity and dedication to expand on those basics. As computing technology advances in the last two decades, computer vision for many industrial, military, and surveillance applications has become reality. Vision computation plays a critical role in many robot related applications such as manufacturing automation, obstacle detection, and autonomous navigation. Most researchers focus on developing sophisticated mathematical models and algorithms to solve vision-related problems, which require extensive computational power. This approach, although successful, is not suitable for many embedded vision applications such as micro unmanned air and ground vehicles (UAV and UGV) that have strict size, power, and processing speed requirements. For navigation and obstacle avoidance of these unmanned vehicles, a rough, quickly calculated estimation is arguably more useful than a more accurate, but slowly calculated estimate. There are many machine vision applications that could benefit from real-time three-dimensional information, especially if provided by lightweight, low power, passive sensors such as vision sensors. Basic low-quality image sensors can be found on systems from cell phones to entertainment game consoles to security systems to high-tech micro unmanned aerial vehicles. Each of these systems may have limited computational resources for many possible reasons: constraints on weight, size, power, and cost, or perhaps the dedication of the bulk of computing resources to a different primary task. Tasks such as obstacle avoidance, pose identification, and landscape mapping could be implemented on resource-constrained systems using three-dimensional information to realize applications like device-less human control, autonomous vehicle control, augmented video surveillance, threat analysis, handicap assistance, and a host of other applications. The development of new technologies aids the advancement of these applications, but even after they are realized, most will continue to benefit from the employment of algorithms that allow tradeoffs in accuracy and required resources. </text>
</content>
</article>
</news>
</page>