Huseyin Ozkan

Comparison of infrared and visible imagery for object tracking: Toward trackers with superior IR performance

The subject of this paper is the visual object tracking in infrared (IR) videos. Our contribution is twofold. First, the performance behaviour of the state-of-the-art trackers is investigated via a comparative study using IR-visible band video conjugates, i.e., video pairs captured observing the same scene simultaneously, to identify the IR specific challenges. Second, we propose a novel ensemble based tracking method that is tuned to IR data. The proposed algorithm sequentially constructs and maintains a dynamical ensemble of simple correlators and produces tracking decisions by switching among the ensemble correlators depending on the target appearance in a computationally highly efficient manner. We empirically show that our algorithm significantly outperforms the state-of-the-art trackers in our extensive set of experiments with IR imagery.

Data Imputation Through the Identification of Local Anomalies

We introduce a comprehensive and statistical framework in a model free setting for a complete treatment of localized data corruptions due to severe noise sources, e.g., an occluder in the case of a visual recording. Within this framework, we propose: 1) a novel algorithm to efficiently separate, i.e., detect and localize, possible corruptions from a given suspicious data instance and 2) a maximum a posteriori estimator to impute the corrupted data. As a generalization to Euclidean distance, we also propose a novel distance measure, which is based on the ranked deviations among the data attributes and empirically shown to be superior in separating the corruptions. Our algorithm first splits the suspicious instance into parts through a binary partitioning tree in the space of data attributes and iteratively tests those parts to detect local anomalies using the nominal statistics extracted from an uncorrupted (clean) reference data set. Once each part is labeled as anomalous versus normal, the corresponding binary patterns over this tree that characterize corruptions are identified and the affected attributes are imputed. Under a certain conditional independency structure assumed for the binary patterns, we analytically show that the false alarm rate of the introduced algorithm in detecting the corruptions is independent of the data and can be directly set without any parameter tuning. The proposed framework is tested over several well-known machine learning data sets with synthetically generated corruptions and experimentally shown to produce remarkable improvements in terms of classification purposes with strong corruption separation capabilities. Our experiments also indicate that the proposed algorithms outperform the typical approaches and are robust to varying training phase conditions.

Online Anomaly Detection Under Markov Statistics With Controllable Type-I Error

We study anomaly detection for fast streaming temporal data with real time Type-I error, i.e., false alarm rate, controllability; and propose a computationally highly efficient online algorithm, which closely achieves a specified false alarm rate while maximizing the detection power. Regardless of whether the source is stationary or nonstationary, the proposed algorithm sequentially receives a time series and learns the nominal attributes-in the online setting-under possibly varying Markov statistics. Then, an anomaly is declared at a time instance, if the observations are statistically sufficiently deviant. Moreover, the proposed algorithm is remarkably versatile since it does not require parameter tuning to match the desired rates even in the case of strong nonstationarity. The presented study is the first to provide the online implementation of Neyman-Pearson (NP) characterization for the problem such that the NP optimality, i.e., maximum detection power at a specified false alarm rate, is nearly achieved in a truly online manner. In this regard, the proposed algorithm is highly novel and appropriate especially for the applications requiring sequential data processing at large scales/high rates due to its parameter-tuning free computational efficient design with the practical NP constraints under stationary or non-stationary source statistics.

A Deterministic Analysis of an Online Convex Mixture of Experts Algorithm

We analyze an online learning algorithm that adaptively combines outputs of two constituent algorithms (or the experts) running in parallel to estimate an unknown desired signal. This online learning algorithm is shown to achieve and in some cases outperform the mean-square error (MSE) performance of the best constituent algorithm in the steady state. However, the MSE analysis of this algorithm in the literature uses approximations and relies on statistical models on the underlying signals. Hence, such an analysis may not be useful or valid for signals generated by various real-life systems that show high degrees of nonstationarity, limit cycles and that are even chaotic in many cases. In this brief, we produce results in an individual sequence manner. In particular, we relate the time-accumulated squared estimation error of this online algorithm at any time over any interval to the one of the optimal convex mixture of the constituent algorithms directly tuned to the underlying signal in a deterministic sense without any statistical assumptions. In this sense, our analysis provides the transient, steady-state, and tracking behavior of this algorithm in a strong sense without any approximations in the derivations or statistical assumptions on the underlying signals such that our results are guaranteed to hold. We illustrate the introduced results through examples.

Data driven frequency mapping for computationally scalable object detection

Nonlinear kernel Support Vector Machines achieve better generalizations, yet their training and evaluation speeds are prohibitively slow for real-time object detection tasks where the number of data points in training and the number of hypotheses to be tested in evaluation are in the order of millions. To accelerate the training and particularly testing of such nonlinear kernel machines, we map the input data onto a low-dimensional spectral (Fourier) feature space using a cosine transform, design a kernel that approximates the classification objective in a supervised setting, and apply a fast linear classifier instead of the conventional radial basis functions. We present a data driven hypotheses generation technique and a LogistBoost feature selection. Our experimental results demonstrate the computational improvements 20∼100× while maintaining a high classification accuracy in comparison to SVM linear and radial kernel basis function classifiers.

Ensemble Of adaptive correlation filters for robust visual tracking

Correlation filters have recently been popular due to their success in short-term single-object tracking as well as their computational efficiency. Nevertheless, the appearance model of a single correlation filter based tracking algorithm quickly forgets the past poses of the target object due to the updates over time. To overcome this undesired forgetting, our approach is to run trackers with separate models simultaneously. Hence, we propose a novel tracker relying on an ensemble of correlation filters, where the ensemble is obtained via a decision tree partitioning in the object appearance space. Our technique efficiently searches among the ensemble trackers and activates the ones which are most specialized on the current object appearance. Our tracking method is capable of switching frequently in the ensemble. Thus, an inherently adaptive and non-linear learning rate is achieved. Moreover, we demonstrate the superior performance of our method in benchmark video sequences.

Acoustic direction finding in highly reverberant environment with single acoustic vector sensor

We propose a novel wideband acoustic direction finding method for highly reverberant environments using measurements from a single Acoustic Vector Sensor (AVS). Since an AVS is small in size and can be effectively used within the full acoustic frequency bands, the proposed solution is suitable for wideband acoustic source localization. In particular, we introduce a novel approach to extract the signal portions that are not distorted with multipath signals and noise. We do not make any stochastic and sparseness assumptions regarding the underlying signal source. Hence, our approach can be applied to a wide range of wideband acoustic signals. We present experiments with acoustic signals that are specially exposed to long reverberations, where the Signal-to-Noise Ratio is as low as 0 dB. In these experiments, the proposed method reliably estimates the source direction with less than 5 degrees of error even under the introduced significantly high reverberation conditions.

Efficient NP Tests for Anomaly Detection Over Birth-Death Type DTMCs

We propose computationally highly efficient Neyman-Pearson (NP) tests for anomaly detection over birth-death type discrete time Markov chains. Instead of relying on extensive Monte Carlo simulations (as in the case of the baseline NP), we directly approximate the log-likelihood density to match the desired false alarm rate; and therefore obtain our efficient implementations. The proposed algorithms are appropriate for processing large scale data in online applications with real time false alarm rate controllability. Since we do not require parameter tuning, our algorithms are also adaptive to non-stationarity in the data source. In our experiments, the proposed tests demonstrate superior detection power compared to the baseline NP while nearly achieving the desired rates with negligible computational resources.

Serum Trace Elements and Heavy Metal Levels in Patients Diagnosed with Chronic Otitis Media and Their Association with Surgical Treatment Outcomes

OBJECTIVE To determine the serum iron (Fe), zinc (Zn), manganese (Mn), copper (Cu), magnesium (Mg), cobalt (Co), and lead (Pb) levels in patients with chronic otitis media (COM) and to evaluate the association of the serum levels of these elements with treatment outcomes. MATERIALS AND METHODS Thirty-one healthy volunteers and 31 patients with COM were prospectively included in this study. Serum levels of Fe, Zn, Mn, Mg, Cu, Co, and Pb were determined by an atomic absorption UNICAM-929 spectrophotometer. RESULTS Serum Co, Pb, and Fe levels were significantly increased (p<0.001) and serum Cu, Zn, Mg, and Mn levels were significantly reduced in patients with COM compared with controls (p<0.001). Serum Co and Mn levels were significantly decreased (p<0.001 and p<0.005, respectively) and serum Cu levels were significantly increased after surgery (p<0.005). The other evaluated blood chemicals and heavy metals did not exhibit significant differences (p>0.05). CONCLUSION Significant alterations in the serum chemical composition of patients with COM were observed. Moreover, with surgical treatment, serum levels of some of these chemicals were significantly altered. Further prospective studies are warranted to elucidate the exact association of these alterations in the etiopathogenesis of COM.

Adaptive hierarchical space partitioning for online classification

We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.