DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Ju-Jang | - |
dc.contributor.advisor | 이주장 | - |
dc.contributor.author | Seo, Kap-Ho | - |
dc.contributor.author | 서갑호 | - |
dc.date.accessioned | 2011-12-14 | - |
dc.date.available | 2011-12-14 | - |
dc.date.issued | 2009 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=309310&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/35496 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전기및전자공학전공, 2009.2, [ vii, 55 p. ] | - |
dc.description.abstract | In recent years, computer vision research has witnessed a growing interest in subset analysis techniques. In particular, eigenvector decomposition has been shown to be a highly effective tool for problems which has high-dimensional signal formats (e.g., an image array) but, nevertheless, represent visual phenomena which are intrinsically low-dimensional. Subspace analysis is heavily used in appearance-based modelling and recognition where the principal modes or the characteristic degrees-of-freedom are extracted and used for description, detection, and recognition. The complex nonlinear appearance manifold expressed as a collection of subsets, and the connectivity among them. The connectivity encodes the transition probability between images in each manifold and is learned from a training video sequences. When we track and recognize the object, a single frame image is used for that tasks. In this case based on PCA, the undesired classification/recognition results often occur. In this thesis, Condensation PCA (CPCA) presentation is introduced, which can be used for spatio-temporal alignment in tracking and recognition tasks. | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | object tracking | - |
dc.subject | object recognition | - |
dc.subject | pca | - |
dc.subject | 물체추적 | - |
dc.subject | 물체인식 | - |
dc.subject | 주요요소분석법 | - |
dc.subject | object tracking | - |
dc.subject | object recognition | - |
dc.subject | pca | - |
dc.subject | 물체추적 | - |
dc.subject | 물체인식 | - |
dc.subject | 주요요소분석법 | - |
dc.title | Unified framework for object tracking and recognition based on condensation principal component analysis in a structured environment | - |
dc.title.alternative | 구조화된 환경 내에서의 조건 확률 확산형 주요 요소 분석법을 이용한 물체 추적 및 인식에 관한 통합 구조 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 309310/325007 | - |
dc.description.department | 한국과학기술원 : 전기및전자공학전공, | - |
dc.identifier.uid | 020015145 | - |
dc.contributor.localauthor | Lee, Ju-Jang | - |
dc.contributor.localauthor | 이주장 | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.