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Research with undergraduates

At University of Arizona Computer Science we strongly encourage capable and interested undergraduate students to become involved with research early on. Doing so is becoming increasingly critical for preparing for grad school, and participants find it very educational and rewarding. Participation can take many forms, including for pay, as part of academics (especially the honor's program), and simply joining a lab on a volunteer basis. Most undergraduate research students will end up doing some work loosely identified as being in each of these categories at some point.

Undergraduates interested in computer vision or multi-modal multi-media data modeling which currently includes projects grounded in biology and astronomy should contact Kobus by E-mail (kobus AT cs DOT arizona DOT edu). More information about the activities of the lab in general is available here.

Students who have participated in vision lab as undergraduates include Matthew Johnson (honor's student, graduated December 2003), Abin Shahab (honor's student, graduated May 2004), Ekatarina (Kate) Spriggs, Juhanni Torkkola, and Bobby Dionne.

Some of the projects that have involved undergraduates are showcased below.

Modeling and visualizing Alternaria
To the right is a labeled model of the fungus Alternaria generated by a stochastic L-system built by undgraduate researcher Kate Spriggs . For more information, follow this link.

Browsing large image collections
A screen shot of a program for browsing large digital art image databases that is being developed by undergraduate students in computer science at the U of A. (Art images courtesy of the Fine Arts Museum of San Francisco).
Contributions have been made by Matthew Johnson and John Bruce.

Word sense disambiguation with pictures
Many words in natural language are ambiguous as illustrated here by the word "bank". Typically, resolving such ambiguity is attempted by looking at nearby words in the passage being analyzed. U of A undergraduate students in computer science have played a key role in the development of a novel method for adding information from accompanying illustrations to help reduce the ambiguity. The system learns from a data base of images that certain word senses (e.g., meanings of bank found with outdoor photos), are associated with certain kinds of image features. This association is then used to incorporate information in illustrations to help disambiguate the word under consideration.
Contributions to this project have been made by Matthew Johnson.

Vision system for flying robots
These three images illustrate contributions made by computer science students to the U A multi-department effort to compete in international aerial robotics competition which is largely an event for undergraduates. Here computer controlled planes and/or helicopters work towards accomplishing a mission specified by the contest organizers. Part of the current task is to find a building having a particular symbol on it (a), and identify the the doors and windows of that building, and then identify which doors and windows are open so that a sub-vehicle can be launched through the portal. Figure (b) shows the symbol identification software being tested from a moving vehicle to simulate flight. Figure (c) shows a view from the computer science department with lines found in this image and the matching lines found in a companion image. The students use the shift (shown in green) between matching edges to estimate the distance to the edge, which is used to help analyze the structures.
Images are courtesy of Kate Taralova. For further information about the University of Arizona Aerial Robotics Club, click here .
The club has had support from many sources including ACIST formerly ITCDI. . For a more complete list of sponsors, click on the sponsor link from the club web site .

(a)

(b)

(c)

Evaluation of image segmentation algorithms
Two images which have been segmented by three different methods. U of A undergraduate students in computer science are involved in research to evaluate the quality of such methods. Segmentation quality is quantified by the degree to which the regions are useful to programs which automatically recognize what is in the images.
Contributions have been made by Abin Shahab.


Modeling and visualizing Alternaria To the right is a labeled model of the fungus Alternaria generated by a stochastic L-system built by undgraduate researcher Kate Spriggs . For more information, follow this link.
Browsing large image collections A screen shot of a program for browsing large digital art image databases that is being developed by undergraduate students in computer science at the U of A. (Art images courtesy of the Fine Arts Museum of San Francisco). Contributions have been made by Matthew Johnson and John Bruce.
Word sense disambiguation with pictures Many words in natural language are ambiguous as illustrated here by the word "bank". Typically, resolving such ambiguity is attempted by looking at nearby words in the passage being analyzed. U of A undergraduate students in computer science have played a key role in the development of a novel method for adding information from accompanying illustrations to help reduce the ambiguity. The system learns from a data base of images that certain word senses (e.g., meanings of bank found with outdoor photos), are associated with certain kinds of image features. This association is then used to incorporate information in illustrations to help disambiguate the word under consideration. Contributions to this project have been made by Matthew Johnson.
Vision system for flying robots These three images illustrate contributions made by computer science students to the U A multi-department effort to compete in international aerial robotics competition which is largely an event for undergraduates. Here computer controlled planes and/or helicopters work towards accomplishing a mission specified by the contest organizers. Part of the current task is to find a building having a particular symbol on it (a), and identify the the doors and windows of that building, and then identify which doors and windows are open so that a sub-vehicle can be launched through the portal. Figure (b) shows the symbol identification software being tested from a moving vehicle to simulate flight. Figure (c) shows a view from the computer science department with lines found in this image and the matching lines found in a companion image. The students use the shift (shown in green) between matching edges to estimate the distance to the edge, which is used to help analyze the structures. Images are courtesy of Kate Taralova. For further information about the University of Arizona Aerial Robotics Club, click here . The club has had support from many sources including ACIST formerly ITCDI. . For a more complete list of sponsors, click on the sponsor link from the club web site .	(a) (b) (c)
Evaluation of image segmentation algorithms Two images which have been segmented by three different methods. U of A undergraduate students in computer science are involved in research to evaluate the quality of such methods. Segmentation quality is quantified by the degree to which the regions are useful to programs which automatically recognize what is in the images. Contributions have been made by Abin Shahab.