چکیده (انگلیسی):

Texture analysis is one of the important as well as useful tasks in image processing applications. Many texture
models have been developed over the past few years and Local Binary Patterns (LBP) is one of the simple and
efficient approach among them. A number of extensions to the LBP method have been also presented but the
problem remains challenging in feature vector generation and comparison. As textures are oriented and scaled
differently, a texture model should effectively handle grey-scale variation, rotation variation, illumination
variation and noise. The length of the feature vector in a texture model also plays an important role in deciding
the time complexity of the texture analysis. This study proposes a new texture model, called Optimized Local
Ternary Patterns (OLTP) in the spatial methods of texture analysis. The proposed texture model is based on Local
Ternary Patterns (LTP), which in turn is based on LBP. A new concept called “Level of Optimality” to select the
optimal set of patterns is discussed in this study. This proposed texture model uses only optimal patterns to extract
the textural information from the digital images and thereby reducing the length of the feature vector. This
proposed model is robust to image rotation, grey-scale transformation, histogram equalization and noise. The
results are compared with other widely used texture models by applying classification tests to variety of texture
images from the standard Brodatz texture database. Experimental results prove that the proposed texture model is
robust to grey-scale variation, image rotation, histogram equalization and noise. Experimental results also show
that the proposed texture model improves the classification accuracy and the speed of the classification process.
In all tested tasks, the proposed method outperforms the earlier methods.