{"_buckets": {"deposit": "78b8f2c4-a2b1-4e27-abd7-d0996944be54"}, "_deposit": {"id": "9853", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "9853"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00009853"}, "item_12_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2009-03-25", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}]}]}, "item_12_date_granted_64": {"attribute_name": "\u5b66\u4f4d\u6388\u4e0e\u5e74\u6708\u65e5", "attribute_value_mlt": [{"subitem_dategranted": "2009-03-25"}]}, "item_12_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "This thesis addresses the problem of recognizing 3D objects and human faces from still-images and video-sequences. Here, the recognition problem is formulated as an appearance matching process. In the appearance-based approach, an object is represented in the form of two-dimensional image sets. To gain efficiency, these images are then projected into a low dimensional space in which the images of each object are represented as Eigenpoints. However, since the appearance of an object in a two-dimensional image is influenced by several parameters, such as shape, pose, illumination, etc., it is important for the recognition system to capture these variations for gaining high recognition performance. A novel method which performs generalization of Eigenpoints through feature lines, called the Modified Nearest Feature Line (MNFL) method is proposed. The feature lines can be acquired by corresponding every pair of Eigenpoints in the same class and projecting every Eigenpoint to the constructed feature lines. Since the feature lines virtually provide an infinite number of Eigenpoints in each class, it expands the capacity of the available database and increases the system\u2019s ability to capture object variations. Moreover, focusing on the problem of pose variability, an appearance manifold with View-dependent Covariance matrix (VC) method is proposed. Being different from the feature line scheme which forms a generalization of Eigenpoints regardless of their pose, the appearance manifold scheme constructs a continuous curve (appearance manifold) by linking two Eigenpoints of consecutive poses. The appearance manifold is constructed along with view-dependent covariance matrices, so that it could capture pose variability and also learn the samples\u2019 distribution of each pose for gaining robustness to pose changes and also degradation effects. Finally, a new incremental unsupervised-learning framework of appearance manifolds is also proposed to present more realistic recognition applications. It is obvious that it is difficult to collect large amounts of training images which depict an object under all poses (from left sideview to right sideview). The incremental unsupervised-learning framework allows us to train the system with initial imagesequences, and later updates the existing categories incrementally every time an unlabeled image-sequence is input. The unlabeled images are first recognized based on the minimum distance of their projected Eigenpoints to the manifolds of objects and then the results are integrated to produce the final sequence\u2019s decision. The performance of the proposed methods were studied through experiments and the results showed that the proposed methods could accurately recognize 3D objects and human faces from still-images and video-sequences under a wide variety of poses, expressions, and degradation effects.", "subitem_description_type": "Abstract"}]}, "item_12_description_5": {"attribute_name": "\u5185\u5bb9\u8a18\u8ff0", "attribute_value_mlt": [{"subitem_description": "\u540d\u53e4\u5c4b\u5927\u5b66\u535a\u58eb\u5b66\u4f4d\u8ad6\u6587 \u5b66\u4f4d\u306e\u7a2e\u985e:\u535a\u58eb(\u60c5\u5831\u79d1\u5b66) (\u8ab2\u7a0b) \u5b66\u4f4d\u6388\u4e0e\u5e74\u6708\u65e5:\u5e73\u621021\u5e743\u670825\u65e5", "subitem_description_type": "Other"}]}, "item_12_dissertation_number_65": {"attribute_name": "\u5b66\u4f4d\u6388\u4e0e\u756a\u53f7", "attribute_value_mlt": [{"subitem_dissertationnumber": "13901\u7532\u7b2c8401\u53f7"}]}, "item_12_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/11662"}]}, "item_12_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_12_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_12_text_63": {"attribute_name": "\u5b66\u4f4d\u6388\u4e0e\u5e74\u5ea6", "attribute_value_mlt": [{"subitem_text_value": "2008"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Lina"}], "nameIdentifiers": [{"nameIdentifier": "29682", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-20"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "thesis_Lina2009.pdf", "filesize": [{"value": "3.1 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 3100000.0, "url": {"label": "thesis_Lina2009.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/9853/files/thesis_Lina2009.pdf"}, "version_id": "f568f8d3-391e-4a52-be78-e015588b9a19"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "3D Object and Human Face Recognition using Appearance Manifold with View-dependent Covariance Matrix", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "3D Object and Human Face Recognition using Appearance Manifold with View-dependent Covariance Matrix"}]}, "item_type_id": "12", "owner": "1", "path": ["312/651/734"], "permalink_uri": "http://hdl.handle.net/2237/11662", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2009-05-01"}, "publish_date": "2009-05-01", "publish_status": "0", "recid": "9853", "relation": {}, "relation_version_is_last": true, "title": ["3D Object and Human Face Recognition using Appearance Manifold with View-dependent Covariance Matrix"], "weko_shared_id": 3}