Rotationally invariant transforms, namely, angular radial transform and polar harmonic transforms such as polar cosine transform, polar sine transform and polar complex exponential transforms, are used to characterize image features for a number of applications like logo recognition, face recognition etc. But the computation of features using these transforms is an expensive process due to thei...

The orientation invariance of visual pattern recognition in pigeons and humans was studied using a conditioned matching-to-sample procedure. A rotation effect, a lengthening of choice latencies with increasing angular disparities between sample and comparison stimuli, was replicated with humans. The choice speed and accuracy of pigeons was not affected by orientation disparities. Novel mirror-i...

Texture classification co-occurrence matrices rotation invariance digital circles discrete Fourier transform. Grey-level co-occurrence matrices (GLCM) have been on the scene for almost forty years and continue to be widely used today. In this paper we present a method to improve accuracy and robustness against rotation of GLCM features for image classification. In our approach co-occurrences ar...

We present the construction of combined blur and rotation moment invariants in arbitrary number of dimensions. Moment invariants to convolution with an arbitrary centrosymmetric filter are derived first, and then their rotationally invariant forms are found by means of group representation theory to achieve the desired combined invariance. Several examples of the invariants are calculated expli...

Visual object recognition occurs easily despite differences in position, size, and rotation of the object, but the neural mechanisms responsible for this invariance are not known. We have found a set of transforms that achieve invariance in a neurally plausible way. We find that a transform based on local spatial frequency analysis of oriented segments and on logarithmic mapping, when applied t...

The scale and rotation invariance properties of a recently proposed algorithm, using the fuzzy evidence accumulation principle, for finding lines (ridges) of non-parametric shapes is analysed. The proposed modifications consist in scaling the accumulated value with the inverse of the line width and further fuzzifying the accumulation process – along the line width. Good invariance properties re...

Under image scaling transform, two interaction models of image intensity gradient fields display unusual power scaling laws. These scalings are very universal in texture images and images of natural scenes, and robust against noise and image local deformation. An image similarity measure is developed in terms of these scaling laws. This similarity measure has graceful properties such as invaria...

Object recognition requires that templates with canonical views are stored in memory. Such templates must somehow be normalised. In this paper we present a novel method for obtaining 2D translation, rotation and size invariance. Cortical simple, complex and end-stopped cells provide multi-scale maps of lines, edges and keypoints. These maps are combined such that objects are characterised. Dyna...

This paper presents new textural features which are based on association rules. We give a texture representation, which is an appropriate formalism, that allows straightforward application of association rules algorithms. This representation has several good properties like invariance to global lightness and invariance to rotation. Association rules capture structural and statistical informatio...

Robust image hashing methods require the robustness to content preserving processing and geometric transform. Zernike moment is a local image feature descriptor whose magnitude components are rotationally invariant and most suitable for image hashing application. In this paper, we proposed Geometric invariant robust image hashing via zernike momment. Normalized zernike moments of an image are u...