TY - GEN
T1 - A robust feature descriptor
T2 - 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2016
AU - Yang, Chu Sing
AU - Yang, Yung Hsian
PY - 2016/1/1
Y1 - 2016/1/1
N2 - We improve a texture descriptor, Local Binary Feature (LBP), called Signed Local Binary Pattern (SLBP) which is more robust in rotation and scale. In this paper we will introduce how to obtain more information in LBP, which with signed bit, and make feature more stable with mean of local area instead of single pixel’s intensity. Finally, to reach more robust in scale by difference smooth factor and implement by Integral Images to reduce computation cost. A pixel can perform different texture information in each scale, thus we select meaningful edge type in smallest scale. And signed bit is adding by current center area is greater or less then whole neighbor area. Then we implement cell and block concept from Histogram of Gradient to test the character recognition. In result part we prove SLBP have more robust than LBP in rotation, scale by texture image and natural scene image. The last part is testing the performance of recognition rate in IIIT5K database.
AB - We improve a texture descriptor, Local Binary Feature (LBP), called Signed Local Binary Pattern (SLBP) which is more robust in rotation and scale. In this paper we will introduce how to obtain more information in LBP, which with signed bit, and make feature more stable with mean of local area instead of single pixel’s intensity. Finally, to reach more robust in scale by difference smooth factor and implement by Integral Images to reduce computation cost. A pixel can perform different texture information in each scale, thus we select meaningful edge type in smallest scale. And signed bit is adding by current center area is greater or less then whole neighbor area. Then we implement cell and block concept from Histogram of Gradient to test the character recognition. In result part we prove SLBP have more robust than LBP in rotation, scale by texture image and natural scene image. The last part is testing the performance of recognition rate in IIIT5K database.
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M3 - Conference contribution
T3 - Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2016
SP - 316
EP - 322
BT - Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2016
A2 - Arabnia, Hamid R.
A2 - Deligiannidis, Leonidas
A2 - Tinetti, Fernando G.
A2 - You, Jane
A2 - Jandieri, George
A2 - Jandieri, George
A2 - Korovin, Iakov
A2 - Schaefer, Gerald
A2 - Sim, Kok Swee
A2 - Solo, Ashu M. G.
PB - CSREA Press
Y2 - 25 July 2016 through 28 July 2016
ER -