TY  - JOUR
T1  - An Efficient Leaf (Texture) Classification using Local Binary
Pattern with Noise Correction
AU - Uppu, Ravi Babu AU - Muthevi, Anil Kumar 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 21
SP  - 5478
EP  - 5484
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.5478.5484
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.5478.5484
KW  - Texture
KW  -noise
KW  -local binary patterns
KW  -uniform patterns
KW  -local ternary patterns
KW  -LBP
AB  - Leaf classification by using images based on their textures is the main objective of this study. Local
Binary Pattern (LBP) operator is eminent extracting method but it is not effective especially in the cases where
noise (noise occurs due to external sources and other reasons) in the images involved or corrupted the image
patterns. Local Ternary Pattern (LTP) is another famous feature extracting method gives solution to some extent
but not completely solves this problem. Towards achieving perfectness of classification by correcting noisy
bits, we propose a method for both error detection and correction called Corrected LBP (CLBP) based on the
analysis of uniform binary patterns which are appears more frequently in the natural images and almost all image
structures. We suggested in our proposed method modification of bits in the pattern based on the analysis of
neighbouring bits. It gives significant increase of accuracy and performance levels.
ER  -