Jet-Based local image descriptors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Shapecollage: occlusion-aware, example-based shape interpretation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
On performance analysis of optical flow algorithms
Proceedings of the 15th international conference on Theoretical Foundations of Computer Vision: outdoor and large-scale real-world scene analysis
Recognizing materials from virtual examples
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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Image features are widely used in computer vision applications. They need to be robust to scene changes and image transformations. Designing and comparing feature descriptors requires the ability to evaluate their performance with respect to those transformations. We want to know how robust the descriptors are to changes in the lighting, scene, or viewing conditions. For this, we need ground truth data of different scenes viewed under different camera or lighting conditions in a controlled way. Such data is very difficult to gather in a real-world setting. We propose using a photorealistic virtual world to gain complete and repeatable control of the environment in order to evaluate image features. We calibrate our virtual world evaluations by comparing against feature rankings made from photographic data of the same subject matter (the Statue of Liberty). We find very similar feature rankings between the two datasets. We then use our virtual world to study the effects on descriptor performance of controlled changes in viewpoint and illumination. We also study the effect of augmenting the descriptors with depth information to improve performance.