{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/32289"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/32289","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Image Segmentation and Range Estimation Using a Moving-aperture Lens","abstract":"Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation.","abstract_html":"Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation.","abstract_has_math":false,"creators":["Subramanian, Anbumani"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Electrical and Computer Engineering","degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Abbott, A. Lynn"],"committee_members":["Bell, Amy E.","Ferrari, Leonard A."],"year":2001,"date_issued":"2001-04-24","date_published":"2001-04-24","updated_at":"2026-07-22T22:20:05Z","subjects":["range estimation","motion parallax","optical flow","Image segmentation","motion segmentation"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05072001-130335"],"render_values":[{"text":"etd-05072001-130335","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/32289","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Abbott, A. Lynn"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Bell, Amy E.","Ferrari, Leonard A."]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Subramanian, Anbumani"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:35:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:35:23Z","2002-05-07"]},{"key":"dc:date.issued","label":"Date","values":["2001-04-24"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical and Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["range estimation","motion parallax","optical flow","Image segmentation","motion segmentation"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05072001-130335"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/32289"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:title","label":"Title","values":["Image Segmentation and Range Estimation Using a Moving-aperture Lens"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Abbott, A. Lynn"],"dc:contributor.committeemember":["Bell, Amy E.","Ferrari, Leonard A."],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Subramanian, Anbumani"],"dc:date.accessioned":["2014-03-14T20:35:23Z"],"dc:date.available":["2014-03-14T20:35:23Z","2002-05-07"],"dc:date.issued":["2001-04-24"],"dc:description.abstract":["Given 2D images, it still remains a big challenge in the field of computer vision to group the image points into logical objects (segmentation) and to determine the locations in the scene (range estimation). Despite the decades of research, a single solution is yet to be found. Through our research we have demonstrated that a possible solution is to use moving aperture lens. This lens has the effect of introducing small, repeating movements of the camera center so that objects appear to translate in the image, by an amount that depends on distance from the plane of focus. Our novel method employs optical flow techniques to an image sequence, captured using a video camera with a moving aperture lens. For a stationary scene, optical flow magnitudes and direction are directly related to the three-dimensional object distance and location from the observer. Exploiting this information, we have successfully extracted objects at different depths and estimated the locations of objects in the scene, with respect to the plane of focus. Our work therefore demonstrates an ability for passive range estimation, without emitting any energy in an environment. Other potential applications include video compression, 3D video broadcast, teleconferencing and autonomous vehicle navigation."],"dc:description.degree":["Master of Science"],"dc:identifier.other":["etd-05072001-130335"],"dc:identifier.uri":["http://hdl.handle.net/10919/32289"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["range estimation","motion parallax","optical flow","Image segmentation","motion segmentation"],"dc:title":["Image Segmentation and Range Estimation Using a Moving-aperture Lens"],"dc:type":["Thesis"],"thesis:degree_discipline":["Electrical and Computer Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:05Z"}