-Texture classification is a major issue in image analysis and pattern recognition. A number of methods are proposed in the literature including Local Binary Pattern (LBP). The LBP variant (s) plays an active role to extract texture features for texture classification. These are rotation invariant, noise sensitive or noise insensitive mehods. Each method has its own advantages and disadvantages...

This paper presents a scale and rotation invariant face detection system. The system employs a hierarchical neural network, called SICoNNet, whose processing elements are governed by the nonlinear mechanism of shunting inhibition. The neural network is used as a face/nonface classifier that can handle in-plane rotated patterns. To train the network as a rotation invariant face classifier, an en...

In the framework of elastic shape analysis, a shape is invariant to scaling, translation, rotation and reparameterization. Since this framework does not yield a closed form of geodesic between two shapes, iterative methods have been proposed. In particular, path straightening methods have been proposed and used for computing a geodesic that is invariant to curve scaling and translation. Path st...

This paper proposes a novel and effective geometric invariant shape representation based on Radon and adaptive stationary wavelet transforms. The proposed representation is invariant to general geometrical transformations. Instead of analyzing shapes directly in the spatial domain, the proposed method extracts shape translation and scale invariant features in radon transform domain by statistic...

In the application of content-based image retrieval, the ideal characteristics should be invariance to geometrical transformations. That is, once the image undergoes geometrical transformations, we expect the features extracted from the image are invariant. Thus, in this paper, rotation, scaling and translation (RST) invariant features for image retrieval are investigated, and a new method is p...

This paper presents a novel algorithm, called Radial Sector Coding (RSC), for Translation, Rotation and Scale invariant character recognition. Translation invariance is obtained using Center of Mass (CoM). Scaling invariance is achieved by normalizing the features of characters. To obtain most challenging rotation invariance, RSC searches a rotation invariant Line of Reference (LoR) by exploiti...

This paper presents a novel rotation-invariant texture image retrieval using particle swarm optimization (PSO) and support vector regression (SVR), which is called the RTIRPS method. It respectively employs log-polar mapping (LPM) combined with fast Fourier transformation (FFT), Gabor filter, and Zernike moment to extract three kinds of rotation-invariant features from gray-level images. Subseq...

An efficient texture modeling framework based on Topological Attribute Patterns (TAP) is presented considering topology related attributes calculated from Local Binary Patterns (LBP). Our main contribution is to introduce new efficient mapping mechanisms that improve some typical mappings for LBP-based operators in texture classification such as rotation invariant patterns ( ri ), rotation inva...

The Coordinated Clusters Representation (CCR) is a texture descriptor based on the probability of occurrence of elementary binary patterns (texels) defined over a square window. The CCR was originally proposed for binary textures, and it was later extended to grayscale texture images through global image thresholding. The required global binarization is a critical point of the method, since thi...