hampered the extension to higher orders (more than the second order). In this paper we present a method which avoids the computation of the autocorrelation coefficients and which can be applied to a large set of toots commonly used in statistical pattern recognition. We will discuss different scenarios

hampered the extension to higher orders (more than the second order). In this paper we present an effective method for using higher order autocorrelation functions for pattern recognition. We will show that while the autocorrelation feature vectors (described below) are elements of a high dimensional space

In this paper, a multilinear approach based on image texture for face recognition is present. First, we extract the texture features of the facial images using the Local Binary Pattern (LBP) algorithm. Then, we apply the High-order Orthogonal Iteration (HOOI) algorithm, the algebra of higher-order

has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

double peak pattern is produced as the FIR filter passes over mine highlight and shadow regions. Although the location, size, and orientation of this pattern within a region of the image can vary, features derived from higher order spectra (HOS) are invariant to translation, rotation, and scaling, while

classification problems was com-pared with that achieved through other standard approaches. These include Haralick’s gray-level co-occurrence matrices, Gabor filters, and local binary patterns. An extensive texture classification study was carried out by selecting images with different contents from the Brodatz

trainable filter which we call Combination Of Shifted FIlter REsponses (COSFIRE) and use for keypoint detection and pattern recognition. It is automatically configured to be selective for a local contour pattern specified by an example. The configuration comprises selecting given channels of a bank of Gabor

of the differences between the gray values. Thus, it loses some important texture information. In this paper, we present a robust facial feature descriptor constructed with the Compound Local Binary Pattern (CLBP) for person-independent facial expression recognition, which overcomes the limitations of LBP

Perception of visual texture flows contributes to object segmentation, shape perception, and object recognition. To better understand the visual mechanisms underlying texture flow perception, we studied the factors limiting detection of simple forms of texture flows composed of local dot dipoles

predefined categories. The discriminative robust local binary pattern (DRLBP) and discriminative robust local ternary pattern (DRLTP) are used for different object texture and edge contour feature extraction process. It is robust to illumination and contrast variations as it only considers the signs