Fingerprint Recognition Using Translation Invariant Scattering Network

Fingerprint recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture higher frequency content of the signal. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). At the end, multi-class SVM is used to perform template matching for the recognition task. The proposed scheme is tested on a well-known fingerprint database and has shown promising results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201

Data-Driven Modeling and Prediction for Reservoir Characterization and Simulation Using Seismic and Petrophysical Data Analyses

This study explores the application of data-driven modeling and prediction in reservoir characterization and simulation using seismic and petrophysical data analyses. Different aspects of the application of data-driven modeling methods are studied, which include rock facies classification, seismic attribute analyses, petrophysical properties prediction, seismic facies segmentation, and reservoir dimension reduction. The application of using petrophysical well logs to predict rock facies is explored using different data analytics methods including decision tree, random forest, support vector machine and neural network. Different models are trained from a set of well logs and pre-interpreted rock facies data. Among the compared methods, the random forest method has the best performance in classifying rock facies in the dataset. Seismic attribute values from a 3D seismic survey and petrophysical properties from well logs are collected to explore the relationships between seismic data and well logs. In this study, deep learning neural network models are created to establish the relationships. The results show that a deep learning neural network model with multi-hidden layers is capable to predict porosity values using extracted seismic attribute values. The utilization of a set of seismic attributes improves the model performance in predicting porosity values from seismic data. This study also presents a novel deep learning approach to automatically identify salt bodies directly from seismic images. A wavelet convolutional neural network (Wavelet CNN) model, which combines wavelet transformation analyses with a traditional convolutional neural network (CNN), is developed and demonstrated to increase the accuracy in predicting salt boundaries from seismic images. The Wavelet CNN model outperforms the conventional image recognition techniques, providing higher accuracy, to identify salt bodies from seismic images. Besides, this study evaluates the effect of singular value decomposition (SVD) in dimension reduction of permeability fields during reservoir modeling. Reservoir simulation results show that SVD is valid in the parameterization of the permeability field. The reconstructed permeability fields after SVD processing are good approximations of the original permeability values. This study also evaluates the application of SVD on upscaling for reservoir modeling. Different upscaling schemes are applied on the permeability field, and their performance are evaluated using reservoir simulation

Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms

CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization

This paper proposes a spatial-Radon domain CT image reconstruction model based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model combines the idea of joint image and Radon domain inpainting model of \cite{Dong2013X} and that of the data-driven tight frames for image denoising \cite{cai2014data}. It is different from existing models in that both CT image and its corresponding high quality projection image are reconstructed simultaneously using sparsity priors by tight frames that are adaptively learned from the data to provide optimal sparse approximations. An alternative minimization algorithm is designed to solve the proposed model which is nonsmooth and nonconvex. Convergence analysis of the algorithm is provided. Numerical experiments showed that the SRD-DDTF model is superior to the model by \cite{Dong2013X} especially in recovering some subtle structures in the images

Computer-Assisted Algorithms for Ultrasound Imaging Systems

Ultrasound imaging works on the principle of transmitting ultrasound waves into the body and reconstructs the images of internal organs based on the strength of the echoes. Ultrasound imaging is considered to be safer, economical and can image the organs in real-time, which makes it widely used diagnostic imaging modality in health-care. Ultrasound imaging covers the broad spectrum of medical diagnostics; these include diagnosis of kidney, liver, pancreas, fetal monitoring, etc. Currently, the diagnosis through ultrasound scanning is clinic-centered, and the patients who are in need of ultrasound scanning has to visit the hospitals for getting the diagnosis. The services of an ultrasound system are constrained to hospitals and did not translate to its potential in remote health-care and point-of-care diagnostics due to its high form factor, shortage of sonographers, low signal to noise ratio, high diagnostic subjectivity, etc. In this thesis, we address these issues with an objective of making ultrasound imaging more reliable to use in point-of-care and remote health-care applications. To achieve the goal, we propose (i) computer-assisted algorithms to improve diagnostic accuracy and assist semi-skilled persons in scanning, (ii) speckle suppression algorithms to improve the diagnostic quality of ultrasound image, (iii) a reliable telesonography framework to address the shortage of sonographers, and (iv) a programmable portable ultrasound scanner to operate in point-of-care and remote health-care applications