Abstract We consider the problem of geo-locating static cameras from long-term time-lapse imagery. This problem has received significant attention recently, with most methods making strong assumptions on the geometric structure of the scene. We explore a simple, robust cue that relates overall image intensity to the zenith angle of the sun (which need not be visible). We characterize the accuracy of geolocation based on this cue as a function of different models of the zenith-intensity relationship and the amount of imagery available.

Abstract We characterize a class of videos consisting of very small but potentially complicated motions. We find that in these scenes, linear appearance variations have a direct relationship to scene motions. We show how to interpret appearance variations captured through a PCA decomposition of the image set as a scene-specific non-parametric motion basis. We propose fast, robust tools for dense flow estimates that are effective in scenes with small motions and potentially large image noise.

Abstract: We introduce the integral-pixel camera model, where measurements integrate over large and potentially overlapping parts of the visual field. This models a wide variety of novel camera designs, including omnidirectional cameras, compressive sensing cameras, and novel programmable-pixel imaging chips.

Abstract Global satellite imagery provides nearly ubiquitous views of the Earth's surface, and the tens of thousands of webcams provide live views from near Earth viewpoints. Combining these into a single application creates live views in the global context, where cars move through intersections, trees sway in the wind, and students walk across campus in real-time. This integration of the camera requires registration, which takes time, effort, and expertise.

A decision support system for fault classification is presented. The fault classification scheme is developed to provide guidance in process improvement and fault-based testing. The research integrates results in fault classification, source code analysis, and fault-based testing research. Initial results indicate that existing change type and fault classification schemes are insufficient for this purpose. Development of sufficient schemes and their evaluation are discussed.

Abstract—This position paper argues that fault classification
provides vital information for software analytics, and that
machine learning techniques such as clustering can be applied to
learn a project- (or organization-) specific fault taxonomy.
Anecdotal evidence of this position is presented as well as
possible areas of research for moving toward the posited goal.