MODFAT: Moving Object Detection by Removing Shadow Based on Fuzzy Technique with an Adaptive Thresholding Method

Nowadays, in this technology centric world, gadgets have become handy due to miniaturization. Especially cameras are widely used device for many aspects, one of the common applications is human behavior identification and intelligent video surveillance. In a such application moving object detection in complex dynamic scene is a tedious task due to various challenges such as occlusion, background illumination variation and shadow. Shadows are created in light occlusion in the object it has major impact in accurate object detection. In this paper, object detection with elimination of shadow is addressed. Many existing methods have failed in discriminating the actual moving object from shadow object very accurately. In order to overcome the limitations of existing methods, an improved fuzzy technique rule is used for shadow removal and an adaptive fuzzy thresholding is used for segmenting a foreground object in background. The proposed techniques are experimented with standard and our own datasets and also, it is compared with other existing approaches. Results of proposed method shows improved reliability.

A hybrid GPFA-EEMD_Fuzzy threshold method for ECG signal de-noising

The Electrocardiogram (ECG) signal records the electrical activity of the heart. It is very difficult for physicians to analyze the ECG signal if noise is embedded during acquisition to inspect the heart’s condition. The denoising of electrocardiogram signals based on the genetic particle filter algorithm(GPFA) using fuzzy thresholding and ensemble empirical mode decomposition (EEMD) is proposed in this paper, which efficiently removes noise from the ECG signal. This paper proposes a two-phase scheme for eliminating noise from the ECG signal. In the first phase, the noisy signal is decomposed into a true intrinsic mode function (IMFs) with the help of EEMD. EEMD is better than EMD because it removes the mode-mixing effect. In the second phase, IMFs which are corrupted by noise is obtained by using spectral flatness of each IMF and fuzzy thresholding. The corrupted IMFs are filtered using a GPF method to remove the noise. Then, the signal is reconstructed with the processed IMFs to get the de-noised ECG. The proposed algorithm is analyzed for a different local hospital database, and it gives better root mean square error and signal to noise ratio than other existing techniques (Wavelet transform (WT), EMD, Particle filter(PF) based method, extreme-point symmetric mode decomposition with Nonlocal Means(ESMD-NLM), and discrete wavelet with Savitzky-Golay(DW-SG) filter).