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Bilateral filter (BF) performs sharpness enhancement and noise removal by using two Gaussian filters, the domain filter in spatial domain and the range filter in intensity domain. To apply the BF to infrared (IR) small target detection, the standard deviation of the two Gaussian filters need to be changed adaptively between the background region and the target region. This paper presents a new BF for small target detection with the adaptive standard deviation based on the analysis of the edge component, also having the variable filter size. At first, threshold of pixel edge components for four directions decides whether any pixel is potential small targets or not. For the proposed BF operation for the potential small target pixels, its edge component is mapped to two standard deviations of the domain filter and the range filter in the proposed BF by mapping function. When the BF comes to a target region, the two standard deviations increase in proportion to the edge component to blur the small targets. To further blur the small targets, the filter size of the BF also increases by its edge component. This enables the BF to perform better and become more suitable in the field of small target detection Experimental results demonstrate that the proposed method is more robust and efficient than the conventional methods.

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In this paper, we introduce an efficient TDLMS filter, using the new weight structure and nonlinear step size for small target detection within infra-red (IR) imagery. A new TDLMS filter that can efficiently detect a small target in IR imagery is proposed. The concept of the proposed filter is to utilize the new weight matrix having the structure reducing effects of the target pixels in order to predict exactly the background. The nonlinear step size utilizing the block statistics is used and background estimation is calculated finally by using the Gaussian distance map. Experimental results show that the proposed method exhibits higher detection rates and lower false alarm rates in comparison to the conventional TDLMS filter.

The characteristics or aspects of important fiducial points (FPs) in the electrocardiogram (ECG) signal are complicated because of various factors, such as non-stationary effects and low signal-to-noise ratio. Due to the various noises caused by the ECG signal measurement environment and by typical ECG signal deformation due to heart diseases, detecting such FPs becomes a challenging task. In this study, we introduce a novel PQRST complex detector using a one-dimensional bilateral filter (1DBF) and the temporal characteristics of FPs. The 1DBF with noise suppression and edge preservation preserves the P- or T-wave whereas it suppresses the QRS-interval. The 1DBF acts as a background predictor for predicting the background corresponding to the P- and T-waves and the remaining flat interval excluding the QRS-interval. The R-peak and QRS-interval are founded by the difference of the original ECG signal and the predicted background signal. Then, the Q- and S-points and the FPs related to the P- and T-wave are sequentially detected using the determined searching range and detection order based on the detected R-peak. The detection performance of the proposed method is analyzed through the MIT-BIH database (MIT-DB) and the QT database (QT-DB).

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