{"id":157144,"date":"2009-06-01T00:00:00","date_gmt":"2009-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/picking-the-best-daisy\/"},"modified":"2018-10-16T21:39:36","modified_gmt":"2018-10-17T04:39:36","slug":"picking-the-best-daisy","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/picking-the-best-daisy\/","title":{"rendered":"Picking the Best Daisy"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Local image descriptors that are highly discriminative, computational efficient, and with low storage footprint have long been a dream goal of computer vision research. In this paper, we focus on learning such descriptors, which make use of the DAISY configuration and are simple to compute both sparsely and densely. We develop a new training set of match\/non-match image patches which improves on previous work. We test a wide variety of gradient and steerable filter based configurations and optimize over all parameters to obtain low matching errors for the descriptors. We further explore robust normalization, dimension reduction and dynamic range reduction to increase the discriminative power and yet reduce the storage requirement of the learned descriptors. All these enable us to obtain highly efficient local descriptors: e.g, 13:2% error at 13 bytes storage per descriptor, compared with 26:1% error at 128 bytes for SIFT.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Local image descriptors that are highly discriminative, computational efficient, and with low storage footprint have long been a dream goal of computer vision research. In this paper, we focus on learning such descriptors, which make use of the DAISY configuration and are simple to compute both sparsely and densely. We develop a new training set [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-157144","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"IEEE Computer Society","msr_edition":"Computer Vision and Pattern Recognition","msr_affiliation":"","msr_published_date":"2009-06-01","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":"223966","msr_publicationurl":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"winder_hua_brown_cvpr09.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/06\/winder_hua_brown_cvpr09.pdf","id":223966,"label_id":0},{"type":"url","title":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/www.cs.ubc.ca\/~mbrown\/patchdata\/patchdata.html"},{"id":223966,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2009\/06\/winder_hua_brown_cvpr09.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"swinder","user_id":33778,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=swinder"},{"type":"text","value":"Gang Hua","user_id":0,"rest_url":false},{"type":"text","value":"Matthew Brown","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[430839,170255],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":430839,"post_title":"On-device ML for Object and Activity Detection","post_name":"device-ml-ambient-aware-applications","post_type":"msr-project","post_date":"2017-10-05 11:03:40","post_modified":"2020-03-13 17:08:00","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/device-ml-ambient-aware-applications\/","post_excerpt":"To process data locally, we have accelerated ML computations via ASICs that incorporate efficient pipelining and parallelism techniques. We have also compressed ML models by scaling their bit-precision values.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/430839"}]}},{"ID":170255,"post_title":"Core Tools for Augmented Reality","post_name":"core-tools-for-augmented-reality","post_type":"msr-project","post_date":"2009-04-28 15:15:15","post_modified":"2019-08-19 15:30:01","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/core-tools-for-augmented-reality\/","post_excerpt":"We aim to enable people with mobile devices to receive continuously updated information about their surroundings by pointing a camera. The system is able to use image recognition to augment what a person sees on the screen with 2D or 3D graphics that track their environment in real time. 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