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A comparison of image features for registering LWIR and visual images

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dc.contributor.author Cronje, J
dc.contributor.author De Villiers, J
dc.date.accessioned 2013-01-31T06:53:22Z
dc.date.available 2013-01-31T06:53:22Z
dc.date.issued 2012-11
dc.identifier.citation Cronje, J and De Villiers, J. 2012. A comparison of image features for registering LWIR and visual images. 23rd Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Pretoria, South Africa, 29-30 November 2012 en_US
dc.identifier.isbn 978-0-620-54601-0
dc.identifier.uri http://www.prasa.org/proceedings/2012/prasa2012-37.pdf
dc.identifier.uri http://hdl.handle.net/10204/6502
dc.description 23rd Annual Symposium of the Pattern Recognition Association of South Africa (PRASA), Pretoria, South Africa, 29-30 November 2012 en_US
dc.description.abstract This paper presents a comparison of several established and recent image feature-descriptors to register long wave infra-red images in the 8–14 m band to visual band images. The feature descriptors were chosen to include robust algorithms, SURF and SIFT — and fast algorithms, BRISK and BFROST. To evaluate the feature-descriptors a ground truth was created by determining the intrinsic and extrinsic camera calibration parameters for the cameras and using this to photogrammetrically relate pixel positions between the images. The inlier results of each feature descriptor for the top 20%, 50% and 100% of the matches (based on match strength) were used to create a homography. The average pixel error between the homography reprojected feature points and the photogrammetric reprojection was used as the error. The results show that none of the descriptors perform well in standard form, with BFROST faring slightly better than the other algorithms. This suggests a need to modify the algorithms to detect physical/structural features and de-emphasise textural features. en_US
dc.language.iso en en_US
dc.publisher PRASA en_US
dc.relation.ispartofseries Workflow;9973
dc.subject Long Wave Infra Red en_US
dc.subject LWIR en_US
dc.subject LWIR images en_US
dc.subject Algorithms en_US
dc.subject Visual images en_US
dc.title A comparison of image features for registering LWIR and visual images en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Cronje, J., & De Villiers, J. (2012). A comparison of image features for registering LWIR and visual images. PRASA. http://hdl.handle.net/10204/6502 en_ZA
dc.identifier.chicagocitation Cronje, J, and J De Villiers. "A comparison of image features for registering LWIR and visual images." (2012): http://hdl.handle.net/10204/6502 en_ZA
dc.identifier.vancouvercitation Cronje J, De Villiers J, A comparison of image features for registering LWIR and visual images; PRASA; 2012. http://hdl.handle.net/10204/6502 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Cronje, J AU - De Villiers, J AB - This paper presents a comparison of several established and recent image feature-descriptors to register long wave infra-red images in the 8–14 m band to visual band images. The feature descriptors were chosen to include robust algorithms, SURF and SIFT — and fast algorithms, BRISK and BFROST. To evaluate the feature-descriptors a ground truth was created by determining the intrinsic and extrinsic camera calibration parameters for the cameras and using this to photogrammetrically relate pixel positions between the images. The inlier results of each feature descriptor for the top 20%, 50% and 100% of the matches (based on match strength) were used to create a homography. The average pixel error between the homography reprojected feature points and the photogrammetric reprojection was used as the error. The results show that none of the descriptors perform well in standard form, with BFROST faring slightly better than the other algorithms. This suggests a need to modify the algorithms to detect physical/structural features and de-emphasise textural features. DA - 2012-11 DB - ResearchSpace DP - CSIR KW - Long Wave Infra Red KW - LWIR KW - LWIR images KW - Algorithms KW - Visual images LK - https://researchspace.csir.co.za PY - 2012 SM - 978-0-620-54601-0 T1 - A comparison of image features for registering LWIR and visual images TI - A comparison of image features for registering LWIR and visual images UR - http://hdl.handle.net/10204/6502 ER - en_ZA


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