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Template matching is a classical and essential step in many pattern recognition, object detection or video tracking systems. This paper aims at integrating and evaluating different template matching methods in the context of pattern spotting in historical document images — i.e. the search for occurrences of a given visual pattern in document images. Given a query image, our pattern spotting system...
Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the co-existence of multiple types of heterogeneity. In this paper, we propose a HEterogeneous REpresentation learning model with structured Sparsity regularization (HERES) to learn from multiple types of heterogeneity. HERES aims to leverage two kinds of information to build a robust...
Multi-label learning is widely applied in many tasks, where an object possesses multiple concepts with each represented by a class label. Previous studies on multi-label learning have focused on a fixed set of class labels, i.e., the class label set of test data is the same as that in the training set. In many applications, however, the environment is open and new concepts may emerge with previously...
Identifying influential spreaders in online social networks (OSNs) has long been an important but difficult problem to be addressed. Distinguished from previous works that mainly focused on the stationary features of users' influence, we systematically study the variations of users' spreading capability given the fact that influential spreaders are more likely to be the targets of various cyber attacks...
Functional gene network analysis, such as gene co-expression network analysis, is useful for detecting disease-associated gene modules. Compared with many gene interaction networks in pathway databases, co-expression networks constructed directly from RNA-seq experiment are context-specific and thus more helpful for detecting differential gene modules under defined conditions. However, existing co-expression...
Image matching is an area of intensive research. Among others, correlation methods have been used widely for this purpose. In literature several correlation filters have been proposed for image matching. Traditionally linear correlation is applied among the images, however the operation is not robust when images are corrupted with non-Gaussian noise. On the other hand, many recognition systems are...
The xDAWN algorithm is well-known as a method for designing spatial filters to improve signal-to-noise ratio and to reduce the dimension of observed EEG signals. This paper proposes a method for spatially smoothing xDAWN spatial filters to give a robustness against small sample problem. The proposed method gives a subspace constraint to the parameter space of the spatial filters. This subspace is...
In most direct adaptive control techniques the dynamic order of systems are assumed to be known. However, this assumption in most real-world problems is not met. Fostered by addressing this problem, this paper presents a methodology to design an operational controller for a specific class of deterministic input-affine systems. Meaning, in the first phase the dynamic order of the unknown system is...
Response surface methodology (RSM) has been widely used in practice, which can optimize single response versus several factors. Naturally people are not only interested in single response optimization, but also multiple responses optimization. In this paper we propose a general framework for multiple responses optimization using Bayesian posterior predictive method. This method can account for the...
In this paper we present our submission to the AAIA'16 Data Mining Challenge, where the objective was to predict dangerous seismic events based on hourly aggregated readings from different sensor and recent mining expert assessment of the conditions in the mine. During the course of the competition we have exploited a framework for automatic feature extraction from time series data that did not require...
A new method to extract fetal electrocardiogram (FECG) signal from the mixed ECG signals is presented by introducing time-correlation to traditional independent component analysis (ICA). FECG signal extraction is a hot research topic because the FECG signal reflects the heart situation of the fetus and provides the basis of early diagnosis. In this paper, an objective function based on time-correlation...
Adaptive detection of range-spread target is discussed in the extremely training-deficient scenarios. The performance analysis of the detector without training data is conducted by Monte Carlo simulation. It implies that, the detector performs robustly for different correlations of clutter and for different target scatterer models. Furthermore, the detection performance improves as the number of channels...
Person re-identification aims to match people across non-overlapping camera views. One of the challenges in re-identification is cross view matching, where the gallery and query data belong to different views. This problem is difficult because the person's appearance varies greatly due to significant viewpoint and poses changes. In this paper, we perform Kernel Canonical Correlation Analysis (KCCA)...
This paper presents a new embedding strategy to extend the performance bound of spread spectrum (SS) based watermarking by introducing more imperceptible distortions measured in the mean square errors (MSE). The potential of the host is sufficiently exploited and utilized to maximize the watermark robustness. This strategy is then realized in audio watermarking by adaptively inverting the host according...
The simultaneous localization and mapping (SLAM) problem has been a research focus for many years and have reached a mature state. However, more robust solutions to the SLAM problem are still required, especially in large noise level scenarios. Because of the strong non-linearity of the SLAM problem, it is vital to start from a good initial value to avoid being trapped in local minima. In this paper,...
One of the most challenging problems faced by tracking algorithms is the issue of template drift. For robust object tracking, template should be adaptive enough to incorporate maximum changes of target appearance, and at same time it should be restrictive enough to reject any background information entering into its model so that drifting of template may be avoided. The existing template updating...
Correlation filter based tracking method has been widely used for its high efficiency and robustness. However, reducing model drifting while achieving both high robustness and fast scale estimation is still an open problem. In this paper, we represent the target in kernel feature space and train a classifier on a scale pyramid to achieve adaptive scale estimation. We then integrate three complementary...
Change detection in aerial images is the core of many remote sensing applications to analyze the dynamics of a wide area on the ground. In this paper, a remote sensing method is proposed based on viewpoint transformation and a modified Kendall rank correlation measure to detect changes in oblique aerial images. First, the different viewpoints of the aerial images are compromised and then, a local...
This paper proposes a Doppler resilient modulation approach that can be used in acoustic local positioning systems with Binary Phase-Shift Keying (BPSK) modulated signals for Time-of-Flight (ToF) estimation. The proposed approach is implemented in the receiver and can be performed in parallel with other classic techniques to improve the position estimation process. The technique is based on Differential...
An auto-correlation matrix memory (ACMM) system continuously computes the degree to which a presented input is novel or anomalous relative to past examples. Here we demonstrate that such a filter can be efficiently implemented with memristive nanodevices and accompanying CMOS circuitry. Complete (a full crossbar) and incomplete (an array of memristive devices) variants of the proposed nanofabric are...
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