Analysis of Hu\u27s Moment Invariants on Image Scaling and Rotation

Moment invariants have been widely applied to image pattern recognition in a variety of applications due to its invariant features on image translation, scaling and rotation. The moments are strictly invariant for the continuous function. However, in practical applications images are discrete. Consequently, the moment invariants may change over image geometric transformation. To address this research problem, an analysis with respect to the variation of moment invariants on image geometric transformation is presented, so as to analyze the effect of image\u27s scaling and rotation. Finally, the guidance is also provided for minimizing the fluctuation of moment invariants

Analysis of Geometric, Zernike and United Moment Invariants Techniques Based on Intra-class Evaluation

Abstract-In this paper, three moment invariants techniques have been used to extract the shape properties of the image. There are geometric moment, zernike moment and united moment invariants. These moment invariants have been used to analyze the image due to its invariant features of an image based on scaling factor and rotation. A set of equations known as intra-class analysis has been applied to measure the similarity of feature vector that represent the same object. The results obtained in this study have been analyzed and compared in terms of intra-class analysis in order to find the best technique among the three different types of moments. Based on the results that have been obtained by using the similar image, it is found that the geometric and united moment invariants techniques are better with small values of total percentage mean absolute error (TPMAE) as compared to zernike moment invariants

A new 2D static hand gesture colour image dataset for ASL gestures

It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset

A Compact and Complete AFMT Invariant with Application to Face Recognition

In this paper, we present a complete set of hybrid similarity invariants under the Analytical Fourier-Mellin Transform (AFMT) framework, and apply it to invariant face recognition. Because the magnitude and phase spectra are not processed separately, this invariant descriptor is complete. In order to simplify the invariant feature data for recognition and discrimination, a 2D-PCA approach is introduced into this complete invariant descriptor. The experimental results indicate that the presented invariant descriptor is complete and similarityinvariant. Its compact representation through the 2D-PCA preserves the essential structure of an object. Furthermore, we apply this compact form into ORL, Yale and BioID face databases for experimental verification, and achieve the desired results