Access Restriction

Author Krahnstoever, Nils ♦ Yeasim, Mohammed ♦ Sharma, Rajeev ♦ Sharma, R.
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 Spurious Variability ♦ Powerful Domain Knowledge ♦ Jointed Motion ♦ Kinematic Model ♦ Automatic Acquisition ♦ Many Researcher ♦ Visual Analysis ♦ Priori Assumption ♦ Manual Initialization ♦ Kinematic Structure ♦ Task-relevant Variation ♦ Articulated Motion ♦ Piecewise Rigid Segment ♦ View Point Fluctuation ♦ Prior Model Knowledge ♦ Underinvestigated Problem ♦ Natural Image Sequence ♦ Monocular View ♦ Great Extent ♦ Model Acquisition ♦ Monocular Visual Data
Description We extract and initialize kinematic models from monocular visual data from the ground up without any manual initialization, adaptation or prior model knowledge. Visual analysis, classification and tracking of articulated motion is challenging due to the difficulties involved in separating noise and spurious variability caused by appearance, size and view point fluctuations from the task-relevant variations. By incorporating powerful domain knowledge, model based approaches are able to overcome this problem to a great extent and are actively explored by many researchers. However, model acquisition, initialization and adaptation are still relatively underinvestigated problems. In this work we show how kinematic structure can be inferred from monocular views without making any a priori assumptions about the scene except that it consists of piecewise rigid segments constrained by jointed motion. The efficacy of the method is demonstrated on synthetic as well as natural image sequences.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2001-01-01
Publisher Institution In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’01), Technical Sketches, Kauai