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publications:rui-cvpr2013 [2013/10/21 12:46]
publications:rui-cvpr2013 [2016/10/20 01:41] (current)
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====== Sensing and Recognizing Surface Textures Using a GelSight Sensor ====== ====== Sensing and Recognizing Surface Textures Using a GelSight Sensor ======
-==== R. Li and E.H. Adelson ====+==== Rui Li and E.H. Adelson ====
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===== Abstract ===== ===== Abstract =====
-We describe a novel device that can be used as a 2.5D "scanner" for acquiring surface texture and shape. The device consists of a slab of clear elastomer covered with a reflective skin. When an object presses on the skin, the skin distorts to take on the shape of the object's surface. When viewed from behind (through the elastomer slab), the skin appears as a relief replica of the surface. A camera records an image of this relief, using illumination from red, green, and blue light sources at three different positions. A photometric stereo algorithm that is tailored to the device is then used to reconstruct the surface. There is no problem dealing with transparent or specular materials because the skin supplies its own BRDF.  Complete information is recorded in a single frame; therefore we can record video of the changing deformation of the skin, and then generate an animation of the changing surface. Our sensor has no moving parts (other than the elastomer slab), uses inexpensive materials, and can be made into a portable device that can be used "in the field" to record surface shape and texture.+Sensing surface textures by touch is a valuable capability for robots. Until recently it was difficult to build a compliant sensor with high sensitivity and high resolution. The GelSight sensor is compliant and offers sensitivity and resolution exceeding that of the human fingertips. This opens the possibility of measuring and recognizing highly detailed surface textures. The GelSight sensor, when pressed against a surface, delivers a height map. This can be treated as an image, and processed using the tools of visual texture analysis. We have devised a simple yet effective texture recognition system based on local binary patterns, and enhanced it by the use of a multi-scale pyramid and a Hellinger distance metric. We built a database with 40 classes of tactile textures using materials such as fabric, wood, and sandpaper. Our system can correctly categorize materials from this database with high accuracy. This suggests that the GelSight sensor can be useful for material recognition by robots.
===== Project page ===== ===== Project page =====
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title      : Sensing and Recognizing Surface Textures Using a GelSight Sensor title      : Sensing and Recognizing Surface Textures Using a GelSight Sensor
-authors    : R. Li and E.H. Adelson +authors    : Rui Li and E.H. Adelson 
-citation   : IEEE Conference on Computer Vision and Pattern Recognition+citation   : IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
shortcite  : CVPR shortcite  : CVPR
year       : 2013 year       : 2013
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summary_page : publications:rui-cvpr2013 summary_page : publications:rui-cvpr2013
keyword_tags : rui, adelson, gelsight keyword_tags : rui, adelson, gelsight
-pdf_url    : http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Li_Sensing_and_Recognizing_2013_CVPR_paper.pdf+pdf_url    : http://persci.mit.edu/_media/pub_pdfs/rui-cvpr13.pdf
type       : publication type       : publication
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publications/rui-cvpr2013.1382373994.txt.gz · Last modified: 2013/10/21 12:46 by rui