ResearchSpace

Tracking image features with PCA-SURF descriptors

Show simple item record

dc.contributor.author Pancham, A
dc.contributor.author Withey, Daniel J
dc.contributor.author Bright, G
dc.date.accessioned 2015-10-05T07:23:09Z
dc.date.available 2015-10-05T07:23:09Z
dc.date.issued 2015-05
dc.identifier.citation Pancham, A, Withey, D.J. and Bright, G. 2015. Tracking image features with PCA-SURF descriptors. In: MVA2015 IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN en_US
dc.identifier.uri http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7153206&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7153206
dc.identifier.uri http://hdl.handle.net/10204/8149
dc.description MVA2015 IAPR International Conference on Machine Vision Applications, May 18-22, 2015, Tokyo, JAPAN. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. en_US
dc.description.abstract The tracking of moving points in image sequences requires unique features that can be easily distinguished. However, traditional feature descriptors are of high dimension, leading to larger storage requirement and slower computation. In this paper, Principal Component Analysis (PCA) is applied to the 64-Dimension (D) Speeded Up Robust Features (SURF) descriptor to reduce the descriptor dimensionality and computational time, and suggest the minimum number of dimensions needed for reliable tracking with the Kalman Filter (KF). Tests using image sequences, from an RGB-D camera, are used to validate the performance of the reduced PCA-SURF descriptors as compared to the standard SURF descriptor. en_US
dc.language.iso en en_US
dc.publisher IEEE Xplore en_US
dc.relation.ispartofseries Workflow;15307
dc.subject Image sequences en_US
dc.subject Traditional feature descriptors en_US
dc.subject PCA-SURF descriptors en_US
dc.subject Mobile robots en_US
dc.subject Principal Components Analysis en_US
dc.subject PCA en_US
dc.subject Scale-Invariant Feature Transform en_US
dc.subject SIFT en_US
dc.title Tracking image features with PCA-SURF descriptors en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Pancham, A., Withey, D. J., & Bright, G. (2015). Tracking image features with PCA-SURF descriptors. IEEE Xplore. http://hdl.handle.net/10204/8149 en_ZA
dc.identifier.chicagocitation Pancham, A, Daniel J Withey, and G Bright. "Tracking image features with PCA-SURF descriptors." (2015): http://hdl.handle.net/10204/8149 en_ZA
dc.identifier.vancouvercitation Pancham A, Withey DJ, Bright G, Tracking image features with PCA-SURF descriptors; IEEE Xplore; 2015. http://hdl.handle.net/10204/8149 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Pancham, A AU - Withey, Daniel J AU - Bright, G AB - The tracking of moving points in image sequences requires unique features that can be easily distinguished. However, traditional feature descriptors are of high dimension, leading to larger storage requirement and slower computation. In this paper, Principal Component Analysis (PCA) is applied to the 64-Dimension (D) Speeded Up Robust Features (SURF) descriptor to reduce the descriptor dimensionality and computational time, and suggest the minimum number of dimensions needed for reliable tracking with the Kalman Filter (KF). Tests using image sequences, from an RGB-D camera, are used to validate the performance of the reduced PCA-SURF descriptors as compared to the standard SURF descriptor. DA - 2015-05 DB - ResearchSpace DP - CSIR KW - Image sequences KW - Traditional feature descriptors KW - PCA-SURF descriptors KW - Mobile robots KW - Principal Components Analysis KW - PCA KW - Scale-Invariant Feature Transform KW - SIFT LK - https://researchspace.csir.co.za PY - 2015 T1 - Tracking image features with PCA-SURF descriptors TI - Tracking image features with PCA-SURF descriptors UR - http://hdl.handle.net/10204/8149 ER - en_ZA


Files in this item

Thumbnail
Name: Pancham_2015_ABST ...
Size: 6.347Kb
Format: PDF
View/Open

This item appears in the following Collection(s)

Show simple item record