18569940
Segmentation of bright targets using wavelets and adaptive thresholding
A general systematic method for the detection and segmentation of bright targets is developed. We use the term "bright target" to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.
40769679
Content-based image retrieval using a Gaussian mixture model in the wavelet domain
The research on Content-based Image Retrieval (CBIR) has been very active in recent years. The performance of a CBIR system can be significantly improved by selecting a good indexing feature space to represent image characteristics. In this paper, we introduce a statistical-model based technique for analyzing and extracting image features in the wavelet domain. The images are decomposed into a set of wavelet subspaces in the wavelet domain and for each wavelet subspace, a two component Gaussian mixture model is developed to describe the statistical characteristics of the wavelet coefficients. The model parameters, which are a good reflection of image features in the wavelet subspaces, are obtained by an EM (Expectation-Maximization) algorithm and employed to construct the indexing feature space for a CBIR system. We apply the new method on the Brodatz image database to demonstrate its performance. The experimental results indicate that our indexing feature space is very effective in representing image characteristics and provides a high retrieval rate in the CBIR system. When compared with some other conventional feature extraction methods, the new method achieves comparable retrieval performance with less number of features in the feature space, which means it is more computationally efficient.
13183312
Wavelet based automatic thresholding for image segmentation
In this paper, a new systematic method to segment possible target areas based on wavelet transforms is presented. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayesian classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). We present examples which demonstrate the efficiency of the technique on a variety of targets.
33784783
A New LUT Watermarking Scheme with Near Minimum Distortion Based on the Statistical Modeling in the Wavelet Domain
This paper presents a new wavelet domain look-up table (LUT) watermarking algorithm that leads to the sub-optimal embedding of watermarks in the sense of minimizing distortion. The algorithm provides a joint distortion-robustness design of the LUT based watermark. There are two key features in the algorithm: (1) a near minimum-distortion LUT with the maximum run of 2 is designed based on the statistical properties of the wavelet coefficients; (2) an expectation-maximization (EM) algorithm based method is employed to model the statistical distribution of wavelet coefficients and select significant coefficients (coefficients with large magnitude) for watermark embedding. The experimental results show that images watermarked by the proposed algorithm have about 1.5-2.5dB peak-signal-to-noise-ratio (PSNR) gain over the conventional odd-even embedding method, while the system presents great robustness. In the case of 0.2bpp (1/40) JPEG2000 compression, the proposed scheme can ensure a reasonably low bit error rate (BER).
26512495
Multiscale tumor detection and segmentation in mammograms
Breast cancer is a leading cause of death among women. Automatic computer-aided analysis method can help radiologists to make more reliable and efficient decisions. Tumors and other abnormalities are of basic interests that need to be segmented and extracted in mammograms. In this paper, a new wavelet-packet multiscale image segmentation method is presented first. By combining this new method with a multiscale region-based segmentation method, we develop a new generic systematic scheme to segment suspicious tumor areas with exact boundaries in mammograms based on multiscale analysis in both grayscale and space. Examples are presented to illustrate the effectiveness of the technique. The new segmentation scheme can also be used in analysis of other abnormal tissues in mammograms.
1464666
Feedforward neural networks with multilevel hidden neurons for remotely sensed image classification
Artificial neural network has been, used as a powerful tool for pattern classification. However, it is difficult to train when the data exhibit non-sparse or overlapping pattern classes which is often the case in practical applications. In this paper, we introduce the feedforward neural network with the hidden layer consisting of multilevel neurons. The convergence property of one-layer neural network with multilevel neurons is proved. The new feedforward model is inherently capable of fuzzy pattern classification of non-sparse or overlapping pattern classes. As an application, we apply the network for the classification of LANDSAT TM data. The results show that this approach produces better results compared with conventional neural networks.
8448018
Efficient Frequent Pattern Mining on Web Logs
Mining frequent patterns from Web logs is an important data mining task. Candidate-generation-and-test and pattern-growth are two representative frequent pattern mining approaches. We have conducted extensive experiments on real world Web log data to analyse the characteristics of Web logs and the behaviours of these two approaches on Web logs. To improve the performance of current algorithms on mining Web logs, we propose a new algorithm – Combined Frequent Pattern Mining (CFPM) to cater for Web log data specifically. We use heuristics to prune search space and reduce costs in mining so that better efficiency is achieved. Experimental results show that CFPM significantly improves the performance of the pattern-growth approach by 1.2–7.8 times on mining frequent patterns from Web logs.
17933107
Safety analysis of usage control authorization models
The usage control (UCON) model was introduced as a unified approach to capture a number of extensions for traditional access control models. While the policy specification flexibility and expressive power of this model have been studied in previous work, as a related and fundamental problem, the safety analysis of UCON has not been explored. This paper presents two fundamental safety results for UCONA, a sub-model of UCON only considering authorizations. In UCONA, an access control decision is based on the subject and/or the object attributes, which can be changed as the side-effects of using the access right, resulting in possible changes to future access control decisions. Hence the safety question in UCONA is all the more pressing since every access can potentially enable additional permissions due to the mutability of attributes in UCON. In this paper, first we show that the safety problem is in general undecidable. Then, we show that a restricted form of UCONA with finite attribute value domains and acyclic attribute creation relation has a decidable safety property. The decidable model maintains good expressive power as shown by specifying an RBAC system with a specific user-role assignment scheme and a DRM application with consumable rights.
620811
Using data mining techniques to learn layouts of flat-file biological datasets
One of the major problems in biological data integration is that many data sources are stored as atlasses, with a variety of different layouts. Integrating data from such sources can be an extremely time-consuming task. We have been developing data mining techniques to help learn the layout of a dataset in a semi-automatic way. In this paper, we focus on the problem of identifying delimiters for optional fields. Since these fields do not occur in every record, frequency based methods are not able to identify the corresponding delimiters. We present a method which uses contrast analysis on the frequency of sequences to identify such delimiters and help complete the layout descriptions. We demonstrate the effectiveness of this technique using three atlasses biological datasets.
7135142
Precise dynamic slicing algorithms
Dynamic slicing algorithms can greatly reduce the debugging effort by focusing the attention of the user on a relevant subset of program statements. In this paper we present the design and evaluation of three precise dynamic slicing algorithms called the full preprocessing (FP), no preprocessing (NP) and limited preprocessing (LP) algorithms. The algorithms differ in the relative timing of constructing the dynamic data dependence graph and its traversal for computing requested dynamic slices. Our experiments show that the LP algorithm is a fast and practical precise slicing algorithm. In fact we show that while precise slices can be orders of magnitude smaller than imprecise dynamic slices, for small number of slicing requests, the LP algorithm is faster than an imprecise dynamic slicing algorithm proposed by Agrawal and Horgan.
7613242
Bluetooth simulations for wireless sensor networks using GTNetS
We introduce a simulation environment for wireless sensor networks using the Bluetooth wireless protocol in our Georgia Tech Network Simulator (GTNetS). Our goal is to explore the applicability of the Bluetooth protocol for wireless sensor networks. Our Bluetooth simulator implements detailed behavior of lower layers of the Bluetooth protocol stack, including baseband, LMP, L2CAP, and BNEP, with the emphasis on interference resilient and power efficient characteristics. The implementation is based on the design of GTNetS and will allow our Bluetooth simulator to be used for large-scale network simulations once an effective scatternet protocol implementation is completed. Further, our Bluetooth simulator is designed by using a strict layered model, which makes it easy to extend in order to accommodate modifications to the Bluetooth protocol or new MAC protocols for sensor networks. We present some simulation results with a simple network configuration to measure the performance of Bluetooth networks in terms of power consumption.