{"id":150036,"date":"1996-05-01T00:00:00","date_gmt":"1996-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/an-integrated-neural-and-algorithmic-system-for-optical-flow-computation\/"},"modified":"2018-10-16T21:57:04","modified_gmt":"2018-10-17T04:57:04","slug":"an-integrated-neural-and-algorithmic-system-for-optical-flow-computation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/an-integrated-neural-and-algorithmic-system-for-optical-flow-computation\/","title":{"rendered":"An Integrated Neural and Algorithmic System for Optical Flow Computation"},"content":{"rendered":"<div class=\"asset-content\">\n<p class=\"Para\">Motion detection plays a central role in several visual environments: knowledge of object velocities and trajectories is fundamental in scene interpretation and segmentation. This task appears a simple problem, but detecting moving objects is very difficult, in fact this is a problem that cannot be considered completely solved today [1] [2] [3].<\/p>\n<p class=\"Para\">In this paper we present a novel method that uses two different approaches: a \u201cneural\u201d one and an algorithmic one. In fact, a Multilayer Perceptron is used in the first stage, in order to detect some motion areas in the scene [5] [6]; a matching algorithm is then used to obtain a sparse optical flow and to compute the epipolar geometry of the moving camera [7] [8]; and, finally, a refinement algorithm is used to produce a denser optical flow field. Thus this method can extract features automatically from moving objects in a scene discarding stationary ones. This approach seems to be very useful for tracking and motion segmentation.<\/p>\n<p class=\"Para\">This work was developed in the context of JACOB project, to achieve the automatic retrieval of images based on motion [9].<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Motion detection plays a central role in several visual environments: knowledge of object velocities and trajectories is fundamental in scene interpretation and segmentation. This task appears a simple problem, but detecting moving objects is very difficult, in fact this is a problem that cannot be considered completely solved today [1] [2] [3]. In this paper [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. WIRN - Italian Workshop on Neural Nets","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. WIRN - Italian Workshop on Neural Nets","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Davide Molinelli, G.A. Marcello Gioiello, Filippo Sorbello","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"1996-05-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"http:\/\/rd.springer.com\/chapter\/10.1007%2F978-1-4471-0951-8_35%20","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":1996,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-150036","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proc. WIRN - Italian Workshop on Neural Nets","msr_affiliation":"","msr_published_date":"1996-05-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"http:\/\/rd.springer.com\/chapter\/10.1007%2F978-1-4471-0951-8_35%20","msr_doi":"","msr_publication_uploader":[{"type":"url","title":"http:\/\/rd.springer.com\/chapter\/10.1007%2F978-1-4471-0951-8_35%20","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":0,"url":"http:\/\/rd.springer.com\/chapter\/10.1007%2F978-1-4471-0951-8_35%20"}],"msr-author-ordering":[{"type":"text","value":"Davide Molinelli","user_id":0,"rest_url":false},{"type":"user_nicename","value":"antcrim","user_id":31055,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=antcrim"},{"type":"text","value":"G.A. 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