caa a22 4500 605540705 CHVBK 20210128100912.0 cr unu---uuuuu 210128e20150401xx s 000 0 eng 10.1007/s00371-014-0934-5 doi (NATIONALLICENCE)springer-10.1007/s00371-014-0934-5 Multi-scale region perpendicular local binary pattern: an effective feature for interest region description [Elektronische Daten] [Thao-Ngoc Nguyen, Kazunori Miyata] This paper proposes the perpendicular local binary pattern (PLBP) for efficiently describing textures in an interest region. Its novelty is two-fold: (1) the candidate generation scheme provides a set of patterns for each pixel, instead of conventionally assigning one pattern per pixel, and (2) an adaptive threshold based on the image contrast of a region is used. These modifications successfully enhance the robustness of PLBP to Gaussian noise as well as in near-uniform regions. We introduce the novel multi-scale region PLBP descriptor, which adopts the PLBP as its core feature. It defines multiple support regions from an interest point, sequentially performs ring-shaped and intensity order-based segmentations on each region, and pools PLBPs to corresponding segments. These steps are controlled easily by a set of parameters, thus offering high flexibility. Experimental results on challenging benchmarks, including three datasets of image matching and two datasets of object recognition, demonstrate the effectiveness of the proposed descriptor in handling common photometric and geometric transformations. It significantly improves the robustness, compared with current state-of-the-art descriptors, while maintaining a reasonable operational cost. Springer-Verlag Berlin Heidelberg, 2014 Local binary pattern nationallicence Perpendicular nationallicence Intensity order nationallicence Multi-support regions nationallicence Interest regions nationallicence Image matching nationallicence Feature descriptor nationallicence Nguyen Thao-Ngoc School of Knowledge Science, Japan Advanced Institute of Science and Technology, 923-1292, Ishikawa, Japan aut Miyata Kazunori School of Knowledge Science, Japan Advanced Institute of Science and Technology, 923-1292, Ishikawa, Japan aut The Visual Computer Springer Berlin Heidelberg 31/4(2015-04-01), 391-406 0178-2789 31:4<391 2015 31 371 https://doi.org/10.1007/s00371-014-0934-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-0934-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Nguyen Thao-Ngoc School of Knowledge Science, Japan Advanced Institute of Science and Technology, 923-1292, Ishikawa, Japan aut NATIONALLICENCE 700 1- Miyata Kazunori School of Knowledge Science, Japan Advanced Institute of Science and Technology, 923-1292, Ishikawa, Japan aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/4(2015-04-01), 391-406 0178-2789 31:4<391 2015 31 371