An Affine Invariant Local Ternary Patterns Method
نویسندگان
چکیده
Local Binary Patterns and various derivatives of the method have been widely used in the field of texture recognition over the past 15 years. A restriction of these methods is their variance with respect to affine transformations of an image. This is caused by the fixed circular neighborhood and the fixed support area of sampling points. The main approach to deal with affine transformations such as rotations is based on modifying or enhancing the encoding scheme of the patterns. In this work we present an extension to Local Ternary Patterns which is based on adaptive elliptic shaped neighborhoods with adaptive support areas of sampling points. We use scale normalized Laplacian maxima in a scale-space to identify interest points within an image. Based on the scale information the multi-scale second moment matrix is computed to estimate the affine transformation at the location of a Laplacian scalespace maximum. Utilizing this information, a scale mask is computed to improve the reliability of scale estimation. Finally Local Ternary Patterns are computed along equidistant points in terms of arc length along the estimated ellipse.
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