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Learning Based Image Analysis - Quality Assessment, Tracking and Classification
Learning Based Image Analysis - Quality Assessment, Tracking and Classification
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152945
- ISBN
- 9798342145169
- DDC
- 006
- 저자명
- Yang, Justin.
- 서명/저자
- Learning Based Image Analysis - Quality Assessment, Tracking and Classification
- 발행사항
- [Sl] : Purdue University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 138 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
- 주기사항
- Advisor: Zhu, Fengqing M.;Allebach, Jan P.;Lin, Qian;Comer, Mary L.
- 학위논문주기
- Thesis (Ph.D.)--Purdue University, 2024.
- 초록/해제
- 요약This dissertation presents four distinct studies in the fields of image processing and machine learning, focusing on applications ranging from quality assessment for raster images in scanned document and virtual reality facial expression tracking to compression for continual learning and food image classification. First, we shift the traditional focus of image quality assessment (IQA) from natural images to scanned documents, proposing a machine learning-based classification method to evaluate the visual quality of scanned raster images. We enhance the classifier's performance using augmented data generated through noise models simulating scanning degradation. Second, we address the challenges of virtual facial animation in immersive VR, developing a domain adversarial training model to generate domain invariant features and combined it with manifold learning methods for accurate facial action unit (AU) intensity estimation from partially occluded facial images. Third, we explore the use of image compression to increase buffer capacity in continual machine learning systems, thereby enhancing exemplar diversity and mitigating catastrophic forgetting. Our approach includes a new framework that selects compression rate and algorithm, showing significant improvements in image classification accuracy on the CIFAR-100 and Image: Net datasets. Finally, we combine class-activation maps with neural image compression in food image classification systems to adapt to continuously evolving data, extending buffer size and enhancing data diversity, which is validated on food-specific datasets and shows potential for broader applications in continual machine learning systems. Together, these studies demonstrate the versatility of image processing and machine learning techniques in addressing complex and varied challenges across different domains.
- 일반주제명
- Feature selection
- 일반주제명
- Food
- 기타저자
- Purdue University.
- 기본자료저록
- Dissertations Abstracts International. 86-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798342145169
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■035 ▼a(MiAaPQ)Purdue26342248
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a006
■1001 ▼aYang, Justin.
■24510▼aLearning Based Image Analysis - Quality Assessment, Tracking and Classification
■260 ▼a[Sl]▼bPurdue University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a138 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-04, Section: B.
■500 ▼aAdvisor: Zhu, Fengqing M.;Allebach, Jan P.;Lin, Qian;Comer, Mary L.
■5021 ▼aThesis (Ph.D.)--Purdue University, 2024.
■520 ▼aThis dissertation presents four distinct studies in the fields of image processing and machine learning, focusing on applications ranging from quality assessment for raster images in scanned document and virtual reality facial expression tracking to compression for continual learning and food image classification. First, we shift the traditional focus of image quality assessment (IQA) from natural images to scanned documents, proposing a machine learning-based classification method to evaluate the visual quality of scanned raster images. We enhance the classifier's performance using augmented data generated through noise models simulating scanning degradation. Second, we address the challenges of virtual facial animation in immersive VR, developing a domain adversarial training model to generate domain invariant features and combined it with manifold learning methods for accurate facial action unit (AU) intensity estimation from partially occluded facial images. Third, we explore the use of image compression to increase buffer capacity in continual machine learning systems, thereby enhancing exemplar diversity and mitigating catastrophic forgetting. Our approach includes a new framework that selects compression rate and algorithm, showing significant improvements in image classification accuracy on the CIFAR-100 and Image: Net datasets. Finally, we combine class-activation maps with neural image compression in food image classification systems to adapt to continuously evolving data, extending buffer size and enhancing data diversity, which is validated on food-specific datasets and shows potential for broader applications in continual machine learning systems. Together, these studies demonstrate the versatility of image processing and machine learning techniques in addressing complex and varied challenges across different domains.
■590 ▼aSchool code: 0183.
■650 4▼aFeature selection
■650 4▼aCoordinate transformations
■650 4▼aFood
■690 ▼a0800
■71020▼aPurdue University.
■7730 ▼tDissertations Abstracts International▼g86-04B.
■790 ▼a0183
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17164298▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
