{"id":823,"date":"2017-11-12T20:55:47","date_gmt":"2017-11-12T20:55:47","guid":{"rendered":"https:\/\/ryma.cinvestav.mx\/ravg\/?post_type=project&#038;p=823"},"modified":"2018-01-08T15:39:52","modified_gmt":"2018-01-08T15:39:52","slug":"bag-relevant-regions-vision-based-place-recognition-challenging-environments","status":"publish","type":"project","link":"https:\/\/ryma.cinvestav.mx\/ravg\/project\/bag-relevant-regions-vision-based-place-recognition-challenging-environments\/","title":{"rendered":"A Bag of Relevant Regions for Vision-based Place Recognition in Challenging Environments"},"content":{"rendered":"<p>In this project we have developed\u00a0a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination[1]. Particularly, we have focused on underwater environments[2].\u00a0The high similarity of features makes difficult the disambiguation between two different places. This method relies on using the Bag-of-Words (BoW) approach to derive an image descriptor from a set of relevant regions, which are extracted using a visual attention algorithm. We have named our approach Bag of Relevant Regions (BoRR). The descriptor of each relevant region is built by using a 2D histogram of the chromatic channels of the CIE-Lab color space.<br \/>\n<img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-826 alignleft\" src=\"https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/training-130x300.png\" alt=\"\" width=\"130\" height=\"300\" srcset=\"https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/training-130x300.png 130w, https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/training.png 304w\" sizes=\"auto, (max-width: 130px) 100vw, 130px\" \/><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-medium wp-image-825 alignleft\" src=\"https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/description-164x300.png\" alt=\"\" width=\"164\" height=\"300\" srcset=\"https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/description-164x300.png 164w, https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/description.png 385w\" sizes=\"auto, (max-width: 164px) 100vw, 164px\" \/><\/p>\n<p>The proposed approach has two phases:<\/p>\n<ol>\n<li><strong>Training phase:<\/strong> a visual attention algorithm is used to identify the most relevant region in a set of images. \u00a0The features are clustered by similarity in terms of theirs 2D color histograms. For each cluster a representative feature (<em>visual word<\/em>) is taken to build a vocabulary of visual features.<\/li>\n<li><strong>Description phase:<\/strong> the relevant features of a given image are obtained by using the visual attention algorithm. Then, each feature is associated to the corresponding feature in the vocabulary. Finally, an histogram of occurrence of each <em>visual word\u00a0<\/em>is calculated and used as the image descriptor.<\/li>\n<\/ol>\n<p>&nbsp;<\/p>\n<p>We have compared our method against a Bag-of-Words with SURF descriptors and FAB-MAP (a state-of-the-art method) on images taken from underwater environments. Our proposed approach has obtained better results in most of the cases.<\/p>\n<p><strong>References<\/strong><\/p>\n<p>[1]\u00a0A. Maldonado-Ram\u00edrez and L. A. Torres-M\u00e9ndez, \u201cA Bag of Relevant Regions for Visual Place Recognition in Challenging Environments,\u201d <i>International Conference on Pattern Recognition<\/i>, Canc\u00fan, 2016.<\/p>\n<p>[2]\u00a0A. Maldonado-Ram\u00edrez and L. A. Torres-M\u00e9ndez, \u201cA Bag of Relevant Regions Model for Visual Place Recognition in Coral Reefs,\u201d <i>2016 MTS\/IEEE OCEANS<\/i>, Monterey, 2016.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this project we have developed\u00a0a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination[1]. Particularly, we have focused on underwater environments[2].\u00a0The high similarity of features makes difficult the disambiguation between two different places. This method relies on using the Bag-of-Words (BoW) [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":824,"comment_status":"open","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"project_category":[16],"project_tag":[],"class_list":["post-823","project","type-project","status-publish","has-post-thumbnail","hentry","project_category-vision-based-underwater-place-recognition"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>A Bag of Relevant Regions for Vision-based Place Recognition in Challenging Environments - Robotics Active Vision Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ryma.cinvestav.mx\/ravg\/project\/bag-relevant-regions-vision-based-place-recognition-challenging-environments\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Bag of Relevant Regions for Vision-based Place Recognition in Challenging Environments - Robotics Active Vision Group\" \/>\n<meta property=\"og:description\" content=\"In this project we have developed\u00a0a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination[1]. Particularly, we have focused on underwater environments[2].\u00a0The high similarity of features makes difficult the disambiguation between two different places. This method relies on using the Bag-of-Words (BoW) [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ryma.cinvestav.mx\/ravg\/project\/bag-relevant-regions-vision-based-place-recognition-challenging-environments\/\" \/>\n<meta property=\"og:site_name\" content=\"Robotics Active Vision Group\" \/>\n<meta property=\"article:modified_time\" content=\"2018-01-08T15:39:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ryma.cinvestav.mx\/ravg\/wp-content\/uploads\/sites\/19\/2017\/11\/Mexibot_exploring_2014_Triptico.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"834\" \/>\n\t<meta property=\"og:image:height\" content=\"438\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/ryma.cinvestav.mx\\\/ravg\\\/project\\\/bag-relevant-regions-vision-based-place-recognition-challenging-environments\\\/\",\"url\":\"https:\\\/\\\/ryma.cinvestav.mx\\\/ravg\\\/project\\\/bag-relevant-regions-vision-based-place-recognition-challenging-environments\\\/\",\"name\":\"A Bag of Relevant Regions for Vision-based Place Recognition in Challenging Environments - 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