Display XML

This page is currently set to use the 3.8 version of the BYU ECEn Department XML/XSLT web templates. For more information about the ECEn Templates you can take a look at the documentation, or contact the web team.

What is This Page?

This is an HTML-ified version of the final assembled XML for the page, after all includes and some escapes have already been processed. To download the actual raw assembled XML---or the individual files of which this XML was orgininally composed, go to the Other Views/Formats Page page.

You can also go back to the normal page view.

The XML...

<page>
<title>
<text>BYU ECEn Department: Robotic Vision Lab</text>
</title>
<name>Robotic Vision Lab</name>
<now-showing-tab>Machine Vision</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>ECO Features</name>
<item>
<name>Algorithms</name>
<related-page>Machine_Vision/ECOFeatures/ECOFeatures.html</related-page>
</item>
<item>
<name>Object Detection</name>
<related-page>Machine_Vision/ObjectDetection/ObjectDetection.html</related-page>
</item>
<item>
<name>Fish Recognition</name>
<related-page>Machine_Vision/FishRecognition/FishRecognition.html</related-page>
</item>
<item>
<name>Stem End and Calyx</name>
<related-page>Machine_Vision/StemEndCalyx/StemEndCalyx.html</related-page>
</item>
<item>
<name>Shrimp Shape</name>
<related-page>Machine_Vision/ShrimpShape/ShrimpShape.html</related-page>
</item>
<item>
<name>Facial Expression Recognition</name>
<related-page>Machine_Vision/FacialExpression/FacialExpression.html</related-page>
</item>
<item>
<name>BYU Fish Dataset</name>
<related-page>Machine_Vision/BYUFish/BYU_Fish.html</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Visual Inspection Systems</name>
<item>
<name>Color Grading</name>
<related-page>Machine_Vision/ColorGrading/ColorGrading.html</related-page>
</item>
<item>
<name>Quality Grading</name>
<related-page>Machine_Vision/QualityGrading/QualityGrading.html</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Turn Angle Distribution Analysis</name>
<item>
<name>Fish Identification</name>
<related-page>Machine_Vision/FishID/FishID.html</related-page>
</item>
<item>
<name>Human Detection</name>
<related-page>Machine_Vision/HumanDetection/HumanDetection.html</related-page>
</item>
<item>
<name>Oyster Grading</name>
<related-page>Machine_Vision/OysterShape/OysterShape.html</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Surface and Volume Estimation</name>
<item>
<name>Produce Area and Volume Measurement</name>
<related-page>Machine_Vision/ProduceSurface/ProduceSurface.html</related-page>
</item>
<item>
<name>3-D Surface Approximation</name>
<related-page>Machine_Vision/3DSurface/3DSurface.html</related-page>
</item>
</sub-menu>
<sub-menu>
<name>Library Automation Systems</name>
<item>
<name>Library Shelf Reading</name>
<related-page>Machine_Vision/BookSpine/BookSpine.html</related-page>
</item>
<item>
<name>Automated Library Inventory System (ALIS)</name>
<related-page>Machine_Vision/ALIS/ALIS.html</related-page>
</item>
</sub-menu>
</menu>
<news>
<title>
<text>Machine Vision Applications</text>
</title>
<article>
<title>
<text>ECO Features</text>
</title>
<highlight-picture-file>Machine_Vision/StemEndCalyx/pic3small.jpg</highlight-picture-file>
<content>
<text> A majority of consumers list flavor, unbruised and umblemished, and crispness as being the most important characteristics of apples. Considering the huge annual production and the fresh fruit consumer demand for crisp unblemished apples, it is required to create reliable automatic inspection processes to identify bruises and blemishes before allowing products to be sent to the fresh fruit market. Relevant research shows that distinguishing stem end and calyx from true defects is the main challenge for automatic apple blemish detection systems. Previous research methods for automated calyx and stem end classification have depended on a human expert to design the features the identification algorithm uses. We used our Evolution-COnstructed (ECO) features to automatically find features that are then used by Adaboost to correctly distinguish bruises and blemishes from the stem end and calyx of apples. We have also used this newly developed ECO Features to recognize and remove invasive fish species, detect human for surveillance applications, and evaluate quality of oyster and shrimp. </text>
</content>
</article>
<article>
<title>
<text>Color Quality Analysis</text>
</title>
<highlight-picture-file>Machine_Vision/ColorGrading/pic4small.JPG</highlight-picture-file>
<content>
<text> Color grading is a crucial step in the processing of fruits and vegetables that directly affects profitability, because the quality of agricultural products is often associated with their color. We developed an effective and user-friendly color mapping concept for automated color grading that is well suited for commercial production. This color mapping method uses preselected colors of interest specific to a given application to calculate a unique set of coefficients for color space conversion. The three-dimensional RGB color space is converted into a small set of color indices unique to the application. In contrast with more complex color grading techniques, our method makes it easy for a human operator to specify and adjust color-preference settings. We used this method to determine the maturity level of tomatoes and dates. </text>
</content>
</article>
<article>
<title>
<text>Turn Angle Distribution Analysis</text>
</title>
<highlight-picture-file>Machine_Vision/FishID/ContourSmall.jpg</highlight-picture-file>
<content>
<text>
<p>Shape information can be used for object classification, recognition, and shape evaluation. Using shape information, we have been able to recognize fish species, detect human intrusion, and grade oyster shape quality.</p>
<p>Measurements of the abundance, distribution, and movement of fish are critical to fishery management. On rivers in the western United States, migrating fish frequently encounter a variety of man-made barriers, including hydroelectric and diversion dams and associated reservoirs. The detrimental impact on fish stocks are substantial. Out FishID system helps monitoring fish migration patterns and measuring abundance. We have used the same shape analysis technique for oyster shell quality evaluation. We measure oyster size and separate shape quality into good, broken, banana, irregular shapes.</p>
</text>
</content>
</article>
</news>
</page>