2006 Fiscal Year Final Research Report Summary
Implementation of Efficient Computer Vision Based on a Robust Subspace Method
| Project/Area Number |
15300062
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Okayama University |
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Principal Investigator |
SHAKUNAGA Takeshi Okayama University, Graduate School of Natural Science and Technology, Computer Science, Professor (80284082)
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| Co-Investigator(Kenkyū-buntansha) |
MIGITA Tsuyoshi OKYAMA UNIVERSITY, Graduate School of Natural Science and Technology, Computer Science, Assistant Professor (90362954)
YAMANE Ryo OKYAMA UNIVERSITY, Faculty of Engineering, Information Technology, Research Assistant (00362955)
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| Project Period (FY) |
2003 – 2006
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| Keywords | Normalized Eigenspace / Homogeneous Eigenspace / Relative residual / Parallel Partial Projections / Sparse Template Matching / Face Recognition / Person Tracking / Sparse Template Condensation |
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| Research Abstract |
A novel framework of the subspace method is investigated from a viewpoint of computer vision. In this framework, an eigenspace is formulated by a homogeneous representation of a normalized eigenspace., where the normalization means 11-normalization. A partial projection of a given image onto a given normalized eigenspace is then efficiently made by a linear projection of the image onto a homogeneous eigenspace by taking all the effective pixels into account. This simple formulation can be extended to robust partial projection when an outlier detection module is combined with the simple partial projection.
The efficient partial projection can facilitate robust face recognition, real-time face/object tracking, and other applications. In the face recognition, a concept of parallel partial projections is introduced for implementation of robust mechanism for the face recognition in natural environments. In the real-time tracking, a concept of sparse template matching is introduced for accomplishing an efficient matching of a template and an input image. When the sparse template matching is utilized in a framework of particle filter, a very efficient tracker is implemented. The sparse template matching can also be generalized to sparse eigen-template matching when the eigen-template is given in prior, and the eigentemplate matching can also be utilized in the particle filter. Some other extensions are also investigated in the research.
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