5,467 research outputs found

    A Compact and Complete AFMT Invariant with Application to Face Recognition

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    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

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

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    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

    Class-Based Feature Matching Across Unrestricted Transformations

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    We develop a novel method for class-based feature matching across large changes in viewing conditions. The method is based on the property that when objects share a similar part, the similarity is preserved across viewing conditions. Given a feature and a training set of object images, we first identify the subset of objects that share this feature. The transformation of the feature's appearance across viewing conditions is determined mainly by properties of the feature, rather than of the object in which it is embedded. Therefore, the transformed feature will be shared by approximately the same set of objects. Based on this consistency requirement, corresponding features can be reliably identified from a set of candidate matches. Unlike previous approaches, the proposed scheme compares feature appearances only in similar viewing conditions, rather than across different viewing conditions. As a result, the scheme is not restricted to locally planar objects or affine transformations. The approach also does not require examples of correct matches. We show that by using the proposed method, a dense set of accurate correspondences can be obtained. Experimental comparisons demonstrate that matching accuracy is significantly improved over previous schemes. Finally, we show that the scheme can be successfully used for invariant object recognition

    Efficient Invariant Features for Sensor Variability Compensation in Speaker Recognition

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    In this paper, we investigate the use of invariant features for speaker recognition. Owing to their characteristics, these features are introduced to cope with the difficult and challenging problem of sensor variability and the source of performance degradation inherent in speaker recognition systems. Our experiments show: (1) the effectiveness of these features in match cases; (2) the benefit of combining these features with the mel frequency cepstral coefficients to exploit their discrimination power under uncontrolled conditions (mismatch cases). Consequently, the proposed invariant features result in a performance improvement as demonstrated by a reduction in the equal error rate and the minimum decision cost function compared to the GMM-UBM speaker recognition systems based on MFCC features

    Revisiting Complex Moments For 2D Shape Representation and Image Normalization

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    When comparing 2D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem. In the paper, we show how to uniquely define the orientation of an arbitrary 2D shape, in terms of what we call its Principal Moments. We show that a small subset of these moments suffice to represent the underlying 2D shape and propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize grey-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating the method with real images.Comment: 69 pages, 20 figure
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