{"id":804,"date":"2017-11-12T19:04:55","date_gmt":"2017-11-12T19:04:55","guid":{"rendered":"https:\/\/ryma.cinvestav.mx\/ravg\/members\/mrico-2\/"},"modified":"2018-01-08T13:56:42","modified_gmt":"2018-01-08T13:56:42","slug":"amaldonado","status":"publish","type":"page","link":"https:\/\/ryma.cinvestav.mx\/ravg\/members\/amaldonado\/","title":{"rendered":"Alejandro Maldonado-Ram\u00edrez"},"content":{"rendered":"

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I am a PhD student working under supervision of Professor L. Abril Torres M\u00e9ndez. My research project<\/a> is focused on mapping underwater environments using visual information. The main objective of this project is to create maps that can be used for robots and human divers for navigating through the same place.<\/p>\n

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<\/a><\/form>

2016<\/h3>

Maldonado-Ramirez, Alejandro; Torres-Mendez, Luz Abril; Castelan, Mario<\/p>

A bag of relevant regions for visual place recognition in challenging environments<\/a> Conference<\/span> <\/p>

2016 23rd International Conference on Pattern Recognition (ICPR), <\/span>2016<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@conference{7899826,
\r\ntitle = {A bag of relevant regions for visual place recognition in challenging environments},
\r\nauthor = {Maldonado-Ramirez, Alejandro and Torres-Mendez, Luz Abril and Castelan, Mario},
\r\ndoi = {10.1109\/ICPR.2016.7899826},
\r\nyear  = {2016},
\r\ndate = {2016-12-01},
\r\nbooktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},
\r\npages = {1358-1363},
\r\nabstract = {In this paper, we present a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination. The high similarity of features makes difficult the disambiguation between two different places. The novelty of our 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 name 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. We have compared our results with those using state of the art descriptors that include the BoW and demonstrate that our approach performs better in most of the cases.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
\r\n<\/pre><\/div>
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In this paper, we present a method for vision-based place recognition in environments with a high content of similar features and that are prone to variations in illumination. The high similarity of features makes difficult the disambiguation between two different places. The novelty of our 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 name 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. We have compared our results with those using state of the art descriptors that include the BoW and demonstrate that our approach performs better in most of the cases.<\/div>
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