Long binary patterns are Abelian 2-avoidable

We show that every long binary pattern is Abelian 2-avoidable.     

A cyclic binary morphism avoiding Abelian fourth powers

We exhibit a cyclic binary morphism avoiding Abelian fourth powers.     

On a class of Abelian codes

We prove that the projective kernel of a special family of Abelian codes, introduced by P. Camion called H-codes, is trivial except in one case, when it is determined here explicitly. We then introduce a generalization of H-codes and show that these codes also have similar properties.     

Avoidable binary patterns in partial words

The problem of classifying all the avoidable binary patterns in (full) words has been completely solved (see Chap. 3 of M. Lothaire, Algebraic Combinatorics on Words, Cambridge University Press, 2002). In this paper, we classify all the avoidable binary patterns in partial words, or sequences that may have some undefined positions called holes. In particular we show that, if we do not substitute any variable of the pattern by a partial word consisting of only one hole, the avoidability index of the pattern remains the same as in the full word case.     

基于二维多尺度局部Gabor二进制模式特征的表情识别

为了在独立于个体身份的面部表情识别中取得更加理想的效果,提出了一种基于二维多尺度局部Gabor二进制模式（MB-LGBP）特征的识别方法。对于表情识别而言,MB-LGBP已被证明了是一种局部和整体上都具有很强表征能力的描绘子。将MB-LGBP与灰度共现矩阵(GLCM)结合起来得到了可以更好地描述局部纹理空间结构特性的二维MB-LGBP特征。在识别中,分别选择了支持向量机（SVM）和基于卡方距离的K-最近邻（KNN）分类器,并对结果进行了比较。实验结果证明了二维MB-LGBP特征相比于MB-LGBP以及其他一些主要的表情识别特征的优越性。     

On representations of 2-increasing binary aggregation functions

In this paper, we provide two different representations of 2-increasing binary aggregation functions by means of their lower and upper margins and a suitable copula.     

A parallel shrinking algorithm for binary patterns

A shrinking algorithm for parallel processing of binary patterns is proposed. It shrinks any pattern symmetrically from all sides to an isolated point at the center irrespective of the complexity of the pattern. It uses a window of 3 × 3 elements. This algorithm does not alter the connectivity properties of the patterns and shrinks each pattern to a single isolated element.     

Multi-structure local binary patterns for texture classification

Recently, the local binary patterns (LBP) have been widely used in the texture classification. The LBP methods obtain the binary pattern by comparing the gray scales of pixels on a small circular region with the gray scale of their central pixel. The conventional LBP methods only describe microstructures of texture images, such as edges, corners, spots and so on, although many of them show good performances on the texture classification. This situation still could not be changed, even though the multi-resolution analysis technique is adopted by LBP methods. Moreover, the circular sampling region limits the ability of the conventional LBP methods in describing anisotropic features. In this paper, we change the shape of sampling region and get an extended LBP operator. And a multi-structure local binary pattern (Ms-LBP) operator is achieved by executing the extended LBP operator on different layers of an image pyramid. Thus, the proposed method is simple yet efficient to describe four types of structures: isotropic microstructure, isotropic macrostructure, anisotropic microstructure and anisotropic macrostructure. We demonstrate the performance of our method on two public texture databases: the Outex and the CUReT. The experimental results show the advantages of the proposed method.     

A simple method for improving local binary patterns by considering non-uniform patterns

The basic idea behind LBP is that an image is composed of micropatterns. A histogram of these micropatterns contains information about the local features in an image. These micropatterns can be divided into two types: uniform and non-uniform. In standard applications using LBP, only the uniform patterns are used. The non-uniform patterns are considered in only a single bin of the histogram that is used to extract features in the classification stage. Non-uniform patterns have undesirable characteristics: they are of a high dimension, partially correlated, and introduce unwanted noise. To offset these disadvantages, we explore using random subspace, well-known to work well with noise and correlated features, to train features based also on non-uniform patterns. We find that a stand-alone support vector machine performs best with the uniform patterns and random subspace with histograms of 50 bins performs best with the non-uniform patterns. Superior results are obtained when the two are combined. Based on extensive experiments conducted in several domains using several benchmark databases, it is our conclusion that non-uniform patterns improve classifier performance.     

Local binary patterns for a hybrid fingerprint matcher

In this work, we present a novel hybrid fingerprint matcher system based on local binary patterns. The two fingerprints to be matched are first aligned using their minutiae, then the images are decomposed in several overlapping sub-windows, each sub-window is convolved with a bank of Gabor filters and, finally, the invariant local binary patterns histograms are extracted from the convolved images.Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed hybrid approach with respect to the well-known Tico's minutiae matcher and other image-based approaches. Moreover, a BioHashing approach have been designed using the proposed fixed-length feature vector and very interesting performance has been obtained by combining it with the Tico's minutiae matcher.     

Facial expression recognition based on local binary patterns

In this paper, a novel approach to automatic facial expression recognition from static images is proposed. The face area is first divided automatically into small regions, from which the local binary pattern (LBP) histograms are extracted and concatenated into a single feature histogram, efficiently representing facial expressions—anger, disgust, fear, happiness, sadness, surprise, and neutral. Then, a linear programming (LP) technique is used to classify the seven facial expressions. Experimental results demonstrate an average expression recognition accuracy of 93.8% on the JAFFE database, which outperforms the rate of all other reported methods on the same database.     

On the structure of binary feedforward inverses with delay 2

Let M‘=S(Mα,f)be a semi-input-memory finite automaton with input alphabet Y and output alphabet X.If X=Y={0,1},then M‘ is a feedforware inverse with delay 2 if and only if there exists a cycle c of state diagram of Mαsuch that f(y0,…,yc,λα（t)0 can be expressed in the form of f ^(1)(y0,…,yc-1,λα（t)) yc for any state t in C and y0,y1,…,yc in Y;or of f^(2)(y0,…,yc-2,λα（t)) yc-c for any state t in Cand y0,y1,…,yc in Y;or for any state t in Cand y0,y1,…yc,in Y,y0,y1…yc satisfies the D[t] condition.The socalled y0,y1…yc satisfying the D[t] condition is that:for some i,j,(i,j)∈{(1,2),(1,3),(2,1),(2,2),(3,1),(3,2)},there exists a (c 2-k)-ary function f^(k),k=1,2,3,such that the Equation(1)and Equation (2)hokl simultaneously for all y‘c-2,…,y‘c 1∈Y. Equation (1);f(y0,…,yc-i,y‘c-i 1,…y‘c,λα（t))=f^(j)(y0,…yc-i,λα（t）） y‘c-i 1 Equation (2):f(y1,…,yc-j 1,y‘c-j 2,…,y‘c 1,λα（t））=f^(j)(y1,…,yc-j 1,λα（t）） y‘c-j s where t=δα（t）and if (i,j)=(1,2)then one and only one of the following conditions C1 and C2 holds for all y‘c-1,y‘c,y‘c 1∈Y.Condition C1:there exists a c-ary function g^(1),such that f(y0,…,yc-2,y‘c-1,y‘c,λα（t））=g^(1),(y0,…,yc-2,λα（t）） y‘c-1( )y‘c;Condition C2:there exists a (c-1)-ary functiong g^(2)such that f(y1,…,yc-2,y‘c-1,y‘c,y‘c 1,λα（t））=g^(2)(y1,…,yc-2,λα（t）） y‘c-1 y‘c,where t=δα(t).