Zhiling Long

Fast communication: Statistical image modeling in the contourlet domain using contextual hidden Markov models

In this paper, a contourlet contextual hidden Markov model (C-CHMM) is established for modeling contourlet images by adapting a previous CHMM for wavelet images (W-CHMM). A mutual information based context design procedure is presented, through which a new context has been constructed. The C-CHMM is tested in a denoising application with promising results, which verifies its effectiveness. This new model is demonstrated to be a better model for contourlet images than the state of the art contourlet hidden Markov tree model. As a general image model, it also shows more potential than the baseline W-CHMM.

Contourlet Spectral Histogram for Texture Classification

Texture classification is a very important image analysis application. To successfully distinguish among texture categories, common features that can effectively characterize texture images are in need. The contourlet transform is a recently proposed two-dimensional technique for image analysis. It has been proved very efficient for representing images with fine geometrical structures, of which texture images are typical examples. In this paper, contourlet based feature extraction for texture classification has been investigated. A new feature design based on the contourlet spectral histogram has been successfully developed. With this feature design, satisfactory classification accuracy has been achieved for some typical sets of Brodatz textures. It has also been demonstrated that the design outperformed several other comparable schemes

Multiscale texture segmentation via a contourlet contextual hidden Markov model

The contourlet transform is an emerging multiscale multidirection image processing technique. It effectively represents smooth curvature details typical of natural images, overcoming a major drawback of the 2-D wavelet transform. Previously, we developed a contourlet image model, that is, the contourlet contextual hidden Markov model (C-CHMM). In this paper, we further develop a multiscale texture segmentation technique based on the C-CHMM. The segmentation method combines a model comparison approach with a multiscale fusion and a neighbor combination process. It also features a neighborhood selection scheme based on smoothed context maps, for both model estimation and neighbor combination. Through a series of segmentation experiments, we examine the effectiveness of the C-CHMM in comparison with closely related models. We also investigate how different context designs affect the segmentation performance. Moreover, we show that the C-CHMM based technique provides improved accuracy in segmenting texture patterns of diversified nature, as compared with popular methods such as the HMTseg and the JMCMS. All these simulation experiments demonstrate the great potential of the C-CHMM for image analysis applications.

Contourlet Image Modeling with Contextual Hidden Markov Models

The contourlet transform is a recently developed two-dimensional transform technique. It is reported to be more effective than wavelets in representing smooth curvature details typical of natural images. To fully exploit the potential of contourlets in image processing and analysis applications, appropriate models are needed to describe statistical characteristics of images in the contourlet domain. In this paper, statistical contourlet image modeling techniques have been investigated. A contextual hidden Markov model, which was successfully applied to wavelet image denoising, has been adapted into the contourlet domain. The resulting contourlet contextual HMM has been tested in a denoising application with promising results, which verified its effectiveness in characterizing contourlet images

Underground power cable fault detection using complex wavelet analysis

Fault detection in underground power cables can be accomplished by examining voltage and current signals using signal processing techniques. In this paper, we explore the feasibility of applying complex wavelet analysis to fault detection. We combine complex wavelets with continuous wavelet transform (CWT), and calculate the impedance from the voltage and current data in the wavelet domain. We then examine the magnitude and phase distributions of the impedance under various conditions. We test our analysis approach with measurement data from different types of cables. The results show that the complex wavelet analysis based approach is able to provide unique signatures for distinguishing between the cables, thus very promising for fault detection.

USE OF OPTICAL AND IMAGING TECHNIQUES FOR INSPECTION OF OFF-LINE JOULE-HEATED MELTER AT THE WEST VALLEY DEMONSTRATION PROJECT

The West Valley melter has been taken out of service. Its design is the direct ancestor of the current melter design for the Hanford Waste Treatment Plant. Over its eight years of service, the West Valley melter has endured many of the same challenges that the Hanford melters will encounter with feeds that are similar to many of the Hanford double shell tank wastes. Thus, inspection of the West Valley melter prior to its disposal could provide valuable — even crucial — information to the designers of the melters to be used at the Hanford Site, particularly if quantitative information can be obtained. The objective of Mississippi State University’s Diagnostic Instrumentation and Analysis Laboratory’s (DIAL) efforts is to develop, fabricate, and deploy inspection tools for the West Valley melter that will (i.) be remotely operable in the West Valley process cell; (ii.) provide quantitative information on melter refractory wear and deposits on the refractory; and (iii.) indicate areas of heterogeneity of deposits, requiring more detailed characterization. A collaborative arrangement has been established with the West Valley Demonstration Project (WVDP) to inspect their melter.Copyright

Fusion of Radiation and Electromagnetic Induction Data for Buried Radioactive Target Detection and Characterization

In general, buried penetrators made of Depleted Uranium (DU) become hazardous waste. In addition to the detection of DU waste, it is also of interest to know their state of oxidation. However, radioactive target detection techniques usually do not differentiate between metal and oxide. In this study, data fusion techniques are applied to combine results from both the radiation detection and the electromagnetic induction (EMI) detection, so that further differentiation among DU metal, DU oxide, and non-DU metal debris may be achieved. A two-step approach is developed to accomplish decision level fusion. The approach is based on techniques such as majority voting (MV) and weighted majority voting (WMV), in combination with a set of decision rules. The fusion approach has been tested successfully with survey data collected on simulation targets.

SalSi: A new seismic attribute for salt dome detection

In this paper, we propose a saliency-based attribute, SalSi, to detect salt dome bodies within seismic volumes. SalSi is based on the saliency theory and modeling of the human vision system (HVS). In this work, we aim to highlight the parts of the seismic volume that receive highest attention from the human interpreter, and based on the salient features of a seismic image, we detect the salt domes. Experimental results show the effectiveness of SalSi on the real seismic dataset acquired from the North Sea, F3 block. Subjectively, we have used the ground truth and the output of different salt dome delineation algorithms to validate the results of SalSi. For the objective evaluation of results, we have used the receiver operating characteristics (ROC) curves and area under the curves (AUC) to demonstrate SalSi is a promising and an effective attribute for seismic interpretation.

Saliency detection for videos using 3D FFT local spectra

Bottom-up spatio-temporal saliency detection identifies perceptually important regions of interest in video sequences. The center-surround model proves to be useful for visual saliency detection. In this work, we explore using 3D FFT local spectra as features for saliency detection within the center-surround framework. We develop a spectral location based decomposition scheme to divide a 3D FFT cube into two components, one related to temporal changes and the other related to spatial changes. Temporal saliency and spatial saliency are detected separately using features derived from each spectral component through a simple center-surround comparison method. The two detection results are then combined to yield a saliency map. We apply the same detection algorithm to different color channels (YIQ) and incorporate the results into the final saliency determination. The proposed technique is tested with the public CRCNS database. Both visual and numerical evaluations verify the promising performance of our technique.

Evaluation of Fourier Transform Profilometry Performance: Quantitative Waste Volume Determination Under Simulated Hanford Waste Tank Conditions

The Hanford Site is currently in the process of an extensive effort to empty and close its radioactive single-shell and double-shell waste storage tanks. Before this can be accomplished, it is necessary to know how much residual material is left in a given waste tank and the chemical makeup of the residue. The objective of Mississippi State University’s Institute for Clean Energy Technology’s (ICET) efforts is to develop, fabricate, and deploy inspection tools for the Hanford waste tanks that will (1) be remotely operable; (2) provide quantitative information on the amount of wastes remaining; and (3) provide information on the spatial distribution of chemical and radioactive species of interest. A collaborative arrangement has been established with the Hanford Site to develop probe-based inspection systems for deployment in the waste tanks. ICET is currently developing an in-tank inspection system based on Fourier Transform Profilometry, FTP. FTP is a non-contact, 3-D shape measurement technique. By projecting a fringe pattern onto a target surface and observing its deformation due to surface irregularities from a different view angle, FTP is capable of determining the height (depth) distribution (and hence volume distribution) of the target surface, thus reproducing the profile of the target accurately under a wide variety of conditions. Hence FTP has the potential to be utilized for quantitative determination of residual wastes within Hanford waste tanks. We have completed a preliminary performance evaluation of FTP in order to document the accuracy, precision, and operator dependence (minimal) of FTP under conditions similar to those that can be expected to pertain within Hanford waste tanks. Based on a Hanford C-200 series tank with camera access through a riser with significant offset relative to the centerline, we devised a testing methodology that encompassed a range of obstacles likely to be encountered “in-tank.” These test objects were inspected by use of FTP and the volume of the test objects determined. The volumes of nondescript test objects were independently determined and were not known to the FTP operators. Several stages of testing are ongoing with successive stages imposing aspects that present increasing difficulty and increasingly more accurate approximations of in-tank environments. We report the Stage 1 results of this multi-stage evaluation of FTP performance.Copyright