A modification to our previous simulation of breast anatomy is proposed to improve the quality of simulated x-ray projections images. The image quality is affected by the voxel size of the simulation. Large voxels can cause notable spatial quantization artifacts; small voxels extend the generation time and increase the memory requirements. An improvement in image quality is achievable without reducing voxel size by the simulation of partial volume averaging in which voxels containing more than one simulated tissue type are allowed. The linear x-ray attenuation coefficient of voxels is, thus, the sum of the linear attenuation coefficients weighted by the voxel subvolume occupied by each tissue type. A local planar approximation of the boundary surface is employed. In the two-material case, the partial volume in each voxel is computed by decomposition into up to four simple geometric shapes. In the three-material case, by application of the Gauss-Ostrogradsky theorem, the 3D partial volume problem is converted into one of a few simpler 2D surface area problems. We illustrate the benefits of the proposed methodology on simulated x-ray projections. An efficient encoding scheme is proposed for the type and proportion of simulated tissues in each voxel. Monte Carlo simulation was used to evaluate the quantitative error of our approximation algorithms.

In recent years, the spectroscopy community has increasingly been using various techniques for automatic computer assisted quantitative and qualitative evaluation of specimen based on spectroscopy data. In this paper, an automated method for classification of four types of proteins (Bovine Serum Albumin (the most abundant protein in blood plasma), and Osteopontin, Leptin and Insulin-like Growth Factor II) (identified as potential biomarkers for ovarian cancer) from laser-induced breakdown spectroscopy (LIBS) data is proposed. Automatic classification of these complex proteins can lead to the identification of chemical components that are vital in the detection of certain diseases (i.e. ovarian cancer). The LIBS method consists of the laser ablation of the sample by using ultrashort and high energetic laser pulses that break the molecular bonds of the compound. The high intensity electromagnetic field induces multiphoton absorption and ionization, resulting in the formation of short living plasma in the vicinity of the sample. During cooling, this plasma reemits light collected by spectrometers. In this paper, the high dimensional spectroscopy data is preprocessed using linear dimensionality reduction technique of principal component analysis and then classification is performed using support vector machines (SVM) and adaptive local hyperplane (ALH). The influence of the number of extracted features as well as the parameters of the classification algorithms to the classification accuracy is also investigated. Our experiments performed on real life data suggest that both classification methods are quite efficient in distinguishing among four types of proteins and they have a fairly robust detection performance for a range of the numbers of extracted features as well as the algorithms' parameters.

Outlier detection has recently become an important problem in many data mining applications. In this paper, a novel unsupervised algorithm for outlier detection is proposed. First we apply a provably globally optimal Expectation Maximization (EM) algorithm to fit a Gaussian Mixture Model (GMM) to a given data set. In our approach, a Gaussian is centered at each data point, and hence, the estimated mixture proportions can be interpreted as probabilities of being a cluster center for all data points. The outlier factor at each data point is then defined as a weighted sum of the mixture proportions with weights representing the similarities to other data points. The proposed outlier factor is thus based on global properties of the data set. This is in contrast to most existing approaches to outlier detection, which are strictly local. Our experiments performed on several simulated and real life data sets demonstrate superior performance of the proposed approach. Moreover, we also demonstrate the ability to detect unusual shapes.

In this paper we present a framework for detecting, recognizing, and localizing objects in overlapping multi-camera network. The three main components of the framework include background change detection, object recognition, and object localization. The background change detection is based on analyzing wavelet transform coefficients of small patches of non-overlapping 3D texture maps. Detected changed background becomes the region of interest which is scanned to recognize various objects of interest. The object recognition is based on model histogram ratios of gradient magnitude patches. The supervised learning of objects is performed by a support vector machine. A homographic spatial transformation brings multiple cameras into alignment with the ground plane to localize objects in 2D space. Experimental results are demonstrated using various benchmark video sequences and object category datasets.

A problem of detecting changes in a region viewed by multiple overlapping cameras is addressed. We are exploring a background change detection method of multilayered orthoimages using spatiotemporal texture blocks. Each camera view is projected to create a spatially aligned multilayered background orthoimages. The multilayered orthoimages are subdivided into non-overlapping blocks, and each block is represented by a 3D texture map. Texture maps are dimensionally reduced with principal component analysis. Motion detection is performed on each block and non-moving texture sections of the block are clustered into N-dimensional hyperspheres to discover changing patterns. The method evaluation is performed on publicly available surveillance video datasets.

Outlier detection has recently become an important problem in many industrial and financial applications. This problem is further complicated by the fact that in many cases, outliers have to be detected from data streams that arrive at an enormous pace. In this paper, an incremental LOF (local outlier factor) algorithm, appropriate for detecting outliers in data streams, is proposed. The proposed incremental LOF algorithm provides equivalent detection performance as the iterated static LOF algorithm (applied after insertion of each data record), while requiring significantly less computational time. In addition, the incremental LOF algorithm also dynamically updates the profiles of data points. This is a very important property, since data profiles may change over time. The paper provides theoretical evidence that insertion of a new data point as well as deletion of an old data point influence only limited number of their closest neighbors and thus the number of updates per such insertion/deletion does not depend on the total number of points TV in the data set. Our experiments performed on several simulated and real life data sets have demonstrated that the proposed incremental LOF algorithm is computationally efficient, while at the same time very successful in detecting outliers and changes of distributional behavior in various data stream applications

We propose motion detection and object tracking method that is particularly suitable for infrared videos. Detection of moving objects in infrared videos is based on changing texture in parts of the view field. We estimate the speed of texture change by measuring the spread of texture vectors in the texture space. This method allows us to robustly detect very fast and very slow moving object. Our theoretical and experimental results show that the proposed method significantly outperforms the Stauffer-Grimson approach based on Gaussian mixture model. We observe that the proposed method does not require any post-processing, which is a necessary step for the Stauffer-Grimson approach. Moreover, the object tracking is improved when based on the spatiotemporal texture blocks.

We investigate the application of localized neural network-based distributional learning techniques for characterizing interesting groups and potentially new types of disorder proteins. Instead of employing a single autoassociator model for learning global distributions of ordered and disordered classes, clustering-based partitioning techniques are first applied independently to both ordered and disordered labeled data set to identify regions of similar characteristics. Subsequently, local autoassociators are employed on labeled data to learn distribution of each cluster. These local autoassociators are used in testing phase to assign each tuple from the unlabeled data set to the cluster closest in distributional sense. Obtained partitions are analyzed for the presence and frequency of the expert-annotated keywords. Frequency comparison is applied to provide insight of keywords sensitive to the distribution heterogeneity and disorder/order labeling. Experimental results on a labeled database of confirmed order and disorder proteins and unlabeled data extracted from SWISS/spl I.bar/PROT database are consistent with related literature and can provide further insight into relationship between protein similarity, keyword labeling and the disorder property.

A labeled sequence data set related to a certain biological property is often biased and, therefore, does not completely capture its diversity in nature. To reduce this sampling bias problem a data mining procedure is proposed for detecting underrepresented relevant sequences. The procedure is aimed at helping domain experts achieve a cost-effective qualitative enlargement of knowledge through an in-depth study of a small number of statistically underrepresented and functionally interesting sequences. Our procedure consists of: (i) learning a class-conditional distribution model on each class of labeled data; (ii) applying the models to select statistically underrepresented unlabeled sequences; and (iii) automatically evaluating their interestingness. An application of the proposed approach is illustrated on an important problem of increasing the data set of confirmed disordered proteins. The obtained results demonstrate the promise of the proposed approach for an efficient reduction of sampling bias in biological databases.

1 Introduction Prediction of a continuous response variable in spatial-temporal domains has recently drawn attention in a data analysis community [4],[11]. Spatial-temporal regression models, learned on systematically collected values of driving attributes, can contribute to better understanding of complex phenomena studied in meteorology, oceanography, environmental science, precision agriculture and other domains. However, spatial-temporal modelling is often difficult due to various factors. For example, due to a prohibitive cost of data collection or other constraints, it is often not possible to systematically measure values of all attributes that have an influence to the observed response [15]. In such applications, models estimated on available attributes often have unsatisfactory explanatory power.

A boosting-based method for centers placement in radial basis function networks (RBFNs) is proposed. Also, the influence of several methods for drawing random samples on the accuracy of RBFNs is examined. The new method is compared to trivial, linear and non-linear regressors including the multilayer perceptron and alternative RBFN learning algorithms and its advantages are demonstrated for learning heterogeneous functions from sparse and non-uniform samples.

A novel method for problem decomposition and for local model selection in a multimodel prediction system is proposed. The proposed method partitions the data into disjoint subsets obtained by the local regression modeling and then it learns the distributions on these sets in order to identify the most appropriate regression model for each test point. The system is applied to a site specific agriculture domain and is shown to provide a substantial improvement in the prediction quality as compared to a global model. Also, some aspects of local learner choice and setting of their parameters are discussed and an overall ability of the proposed model to accurately perform regression is assessed.

We analyze a new interference suppression structure in direct-sequence spread-spectrum (DSSS) systems that is based on two dimensional (frequency- and time-domain) notch filtering. In the first step, the filter is realized in the form of variable-step (function of estimation error-power /spl nu/) least mean square algorithm. This modification will improve the system performance in the transient period by making this period shorter. Unfortunately, in the steady-state regime, the performance will be slightly degraded. This suggests a modification that will include an algorithm with /spl nu/=2 in the transient state and /spl nu/=1 in the steady-state operation. Further modification of the algorithm will use time notching of the code division multiple-access (CDMA) receiver in the period when estimation error is larger than a given threshold. A comprehensive analysis and comparison of these modifications are presented and discussed. These results can be used either for performance enhancement of DSSS systems in the presence of jamming or for CDMA overlay-type mobile communication networks. In the latter case, the interfering signal comes from another user employing narrow-band modulation. The analysis does not include fading so that the results can be used in the case when the narrow-band modulated signal is not significantly distorted by fading. An example is line-of-sight microwave communications, which happens to be one of the most feasible applications.

Using an operator approach this letter shows that Metzger's Algorithm for pdf computation (see ibid., vol. 35, pp. 396-402, Apr 1987) has an error in formula (15), which defines the procedure of generating the successive series of pdf approximation. A correction is given.