Access Restriction

Author Kaneva, Biliana ♦ Torralba, Antonio ♦ Freeman, William T.
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Controlled Way ♦ Different Camera ♦ Repeatable Control ♦ Feature Ranking ♦ Image Feature ♦ Subject Matter ♦ Descriptor Performance ♦ Computer Vision Application ♦ Feature Descriptor ♦ Photorealistic Virtual World ♦ Photographic Data ♦ Virtual World ♦ Similar Feature Ranking ♦ Depth Information ♦ Image Transformation ♦ Ground Truth Data ♦ Different Scene ♦ Real-world Setting ♦ Virtual World Evaluation
Description 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. 1.
In: ICCV. (2011
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article