A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy

Application of Fractal and Wavelets in Microcalcification Detection

Breast cancer has been recognized as one or the most frequent, malignant tumors in women, clustered microcalcifications in mammogram images has been widely recognized as an early sign of breast cancer. This work is devote to review the application of Fractal and Wavelets in microcalcifications detection

A critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries

Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause for death, after heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the vital major imaging techniques used for diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a standard aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesions structures. This research basically aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely daubechies, symlet, coiflet and de-meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics obtained. Experiments indicate the feature statistics obtained from daubechies and de-meyer wavelets were able to clearly distinguish between the typical brain tissues and abnormal lesion structures

Simulation of Gegenbauer Processes using Wavelet Packets

In this paper, we study the synthesis of Gegenbauer processes using the wavelet packets transform. In order to simulate a 1-factor Gegenbauer process, we introduce an original algorithm, inspired by the one proposed by Coifman and Wickerhauser [1], to adaptively search for the best-ortho-basis in the wavelet packet library where the covariance matrix of the transformed process is nearly diagonal. Our method clearly outperforms the one recently proposed by [2], is very fast, does not depend on the wavelet choice, and is not very sensitive to the length of the time series. From these first results we propose an algorithm to build bases to simulate k-factor Gegenbauer processes. Given its practical simplicity, we feel the general practitioner will be attracted to our simulator. Finally we evaluate the approximation due to the fact that we consider the wavelet packet coefficients as uncorrelated. An empirical study is carried out which supports our results

Wavelet based approach for facial expression recognition

Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database

Coastal Hurricane Damage Assessment via Wavelet Transform of Remotely Sensed Imagery

This dissertation uses post storm imagery processed using wavelet transforms to investigate the capability of wavelet transform-based methods to classify post storm damage of residential areas. Five level Haar, Meyer, Symlets, and Coiflets wavelet transform decompositions of the post storm imagery are inputs to damage classification models of post hurricane and tornado damage. Hurricanes Ike, Rita, Katrina, and Ivan are examined as are the 2011 Joplin and Tuscaloosa tornadoes. Wavelet transform-based classification methods yielded varying classification accuracies for the four hurricanes examined, ranging from 67 percent to 89 percent classification accuracy for classification models informed by samples from the storms classified. Classification accuracies fall when the samples being classified are from a hurricane not informing the classification model, from 17 percent for Rita classified with an Ike-based model, 41 percent for Rita classified with an Ike-Katrina-based model, to 69 percent for Rita classified with an Ike-Katrina-Ivan-based model. The variability within and poor classification accuracy of these models can be attributed to the large variations in the four hurricane events studied and the significant differences in impacted land cover for each of these storms. Classification accuracies improved when these variations were limited via examination of residential areas impacted by 2011 Joplin and Tuscaloosa tornadoes. Damage classification models required as few as nineteen to as many as fifty nine wavelet coefficients to explain the variability in the hurricane storm data samples, and included all four wavelet functions studied. A similar analysis of the tornado damaged areas resulted in a damage classification model with only six wavelet coefficients, four Meyer-based, one Symlets-based and one Haar-based. Classification accuracies ranged from 96 percent for samples included in the model formation to 85 percent for samples not included in the model formation. The damage classification accuracies found for tornado storms suggests this model is suitable for operational implementation. The damage classification accuracies found for the hurricane storms suggests further investigation into methods that will reduce the variability attributable to land cover and storm variability