Wavelet Energy Signatures Research Articles

Wavelet energy signatures and robust Bayesian neural network for visual quality recognition of nonwovens

In this paper, the visual quality recognition of nonwovens is considered as a common problem of pattern recognition that will be solved by a joint approach by combining wavelet energy signatures, Bayesian neural network, and outlier detection. In this research, 625 nonwovens images of 5 different grades, 125 each grade, are decomposed at 4 levels with wavelet base sym6, then two energy signatures, norm-1 L 1 and norm-2 L 2 are calculated from wavelet coefficients of each high frequency subband to train and test Bayesian neural network. To detect the outlier of training set, scaled outlier probability of training set and outlier probability of each sample are introduced. The committees of networks and the evidence criterion are employed to select the ‘most suitable’ model, given a set of candidate networks which has different numbers of hidden neurons. However, in our research with the finite industrial data, we take both the evidence criterion and the actual performance into account to determine the structure of Bayesian neural network. When the nonwoven images are decomposed at level 4, with 500 samples to training the Bayesian neural network that has 3 hidden neurons, the average recognition accuracy of test set is 99.2%. Experimental results on the 625 nonwoven images indicate that the wavelet energy signatures are expressive and powerful in characterizing texture of nonwoven images and the robust Bayesian neural network has excellent recognition performance.

CO-OCCURRENCE PROBABILITIES AND WAVELET-BASED SPOOF FINGERPRINT DETECTION

Perspiration phenomenon is very significant to detect the liveness of a finger. However, it requires two consecutive fingerprints to notice perspiration, and therefore may not be suitable for real time authentications. Some other methods in the literature need extra hardware to detect liveness. To alleviate these problems, in this paper, to detect liveness a new texture-based method using only the first fingerprint is proposed. It is based on the observation that real and spoof fingerprints exhibit different texture characteristics. Textural measures based on gray level co-occurrence matrix (GLCM) are used to characterize fingerprint texture. This is based on structural, orientation, roughness, smoothness and regularity differences of diverse regions in a fingerprint image. Wavelet energy signature is also used to obtain texture details. Dimensionalities of feature sets are reduced by Sequential Forward Floating Selection (SFFS) method. GLCM texture features and wavelet energy signature are independently tested on three classifiers: neural network, support vector machine and K-nearest neighbor. Finally, two best classifiers are fused using the "Sum Rule''. Fingerprint database consisting of 185 real, 90 Fun-Doh and 150 Gummy fingerprints is created. Multiple combinations of materials are used to create casts and moulds of spoof fingerprints. Experimental results indicate that, the new liveness detection method is very promising, as it needs only one fingerprint and no extra hardware to detect vitality.

Log-polar wavelet energy signatures for rotation and scale invariant texture classification

Classification of texture images is important in image analysis and classification. This paper proposes an effective scheme for rotation and scale invariant texture classification using log-polar wavelet signatures. The rotation and scale invariant feature extraction for a given image involves applying a log-polar transform to eliminate the rotation and scale effects, but at same time produce a row shifted log-polar image, which is then passed to an adaptive row shift invariant wavelet packet transform to eliminate the row shift effects. So, the output wavelet coefficients are rotation and scale invariant. The adaptive row shift invariant wavelet packet transform is quite efficient with only O(n /spl middot/ log n) complexity. A feature vector of the most dominant log-polar wavelet energy signatures extracted from each subband of wavelet coefficients is constructed for rotation and scale invariant texture classification. In the experiments, we employed a Mahalanobis classifier to classify a set of 25 distinct natural textures selected from the Brodatz album. The experimental results, based on different testing data sets for images with different orientations and scales, show that the proposed classification scheme using log-polar wavelet signatures outperforms two other texture classification methods, its overall accuracy rate for joint rotation and scale invariance being 90.8 percent, demonstrating that the extracted energy signatures are effective rotation and scale invariant features. Concerning its robustness to noise, the classification scheme also performs better than the other methods.

Rotation and scale invariant wavelet feature for content‐based texture image retrieval

AbstractThis article introduces an effective rotation and scale invariant log‐polar wavelet texture feature for image retrieval. The proposed feature is an attempt to enhance the existing content‐based image retrieval systems that largely present difficulty in coping with images with changes in orientations and scales. The underlying feature extraction process involves a log‐polar transform followed by an adaptive row shift invariant wavelet packet transform. The log‐polar transform converts a given image into a rotation and scale invariant but row‐shifted image, which is then further processed through an adaptive row‐shift invariant wavelet packet transform operation to generate adaptively selected subbands of rotation and scale invariant wavelet coefficients, based on an information cost function. An energy signature is computed for each subband of these wavelet coefficients. To reduce feature dimensionality, only the most dominant log‐polar wavelet energy signatures are selected for the feature vector for image retrieval. The overall feature extraction process is quite efficient and involves only O(n · log n) complexity. Experimental results show that this rotation and scale invariant wavelet feature is quite effective for image retrieval and outperforms the traditional wavelet packet signatures.