TY  - JOUR
T1  - Completed Local Ternary Count for Rotation Invariant Texture Classification
AU - Sree, Ch. Sudha AU - Rao, M.V.P. Chandra Sekhara 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 10
SP  - 3633
EP  - 3641
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.3633.3641
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.3633.3641
KW  - Local binary pattern
KW  -local ternary pattern
KW  -completed local binary count
KW  -rotation invariant
KW  -texture classification
AB  - Rotation invariant texture classification is an important issue in image analysis. For more than a
decade Local Binary Pattern (LBP) variants have been proven to be successful methods in wide applications
of rotation invariant texture classification. However, these invariant patterns are not absolutely rotation
invariant and some of these are noise sensitive/insensitive. Till date, no ternary LBP variant is found as rotation
invariant and noise in sensitive. This study proposes a rotation invariant and noise insensitive texture
descriptors called, Local Ternary Count (LTC) and Completed Local Ternary Count (CLTC). The two descriptors
characterize the textures using local ternary gray scale difference by avoiding the micro-structure. The proposed
CLTC is a set of three new operators defined for sign, magnitude and central pixel components. Experiments
are conducted on three well known benchmark databases Outex, UIUC and CUReT. The performance of the
proposed method is analysed by comparing with the various existing LBP variants. It is observed that, CLTC
exhibits significant improvement in classification accuracy and is more robust to noise when compared with LBP
variants at different Signal-to-Noise Ratio (SNR) values.
ER  -