Differences

This shows you the differences between two versions of the page.

--- project [2013/09/18 21:49] – wildes
+++ project [2013/09/18 21:54] (current) – wildes
@@ Line 12: / Line 12: @@
 ==== Laboratory Facilities ====
-Computer Science and Engineering Department laboratory facilites will be available for support of projects. Details TBA.
+Computer Science and Engineering Department laboratory facilites will be available for support of projects. Documentation of the lab is available under the Syllabus link on the sidebar to this page.
 ==== Suggested Topics ====
@@ Line 23: / Line 23: @@
   * Adaptive stereo vision: In our study of stereopsis, we will learn that a useful strategy is to begin our estimation procedures with coarse image data (e.g., imagery with low spatial resolution) and subsequently refine our solution through systematic incorporation of more refined image data (e.g., imagery with higher spatial resolution). We will refer to this paradigm as course-to-fine refinement. An interesting question that arises in this paradigm is how to decide on the level of refinement that is appropriate for a given image or even a given image region. For this project, the student will explore methods for automatically adapting the coarse-to-fine refinement of stereo estimates based on the input binocular image data and implement as well as test at least one such procedure.
+  * Primitive event recognition: Image sequences comprise a vast amount of data. For timely processing, it is critical that early operations begin to recognize significant patterns in this data. For example, when processing a video sequence, it might be desirable to make distinctions regarding those portions of the imagery that correspond to moving objects so that subsequent processing might attend to such regions. For this project, the student will suggest and investigate a set of simple, dynamic patterns or events that it might be advantageous to support through early processing (e.g., what is moving, what is not, what is noise, etc.). The student will develop an analysis that shows how the patterns can be distinguished on the basis of simple vision operations as well as implement and test corresponding algorithms.
   * Module combination: Typically, a solitary computer vision module provides incomplete information about the world; however, taken in tandem (two or more) modules might combine forces to provide a better understanding of the impinging environment. For example, binocular stereo typically performs best in regions with well define features or texture patterns, but poorly in smoothly shaded regions; in contrast shape from shading can perform reasonably in smoothly shaded regions, but less well in highly textured regions. For this project, students will select a pair of complimentary vision modules (e.g., stereo and shading, feature-based and area-based image correspondence/matching for stereo, binocular stereo and motion, etc.) and study how they can be combined in an advantageous fashion. The student will develop an analysis that shows how the modules can be combined as well as implement and test corresponding algorithms.