Detrended fluctuation thresholding for empirical mode decomposition based denoising

Signal decompositions such as wavelet and Gabor transforms have successfully been applied in denoising problems. Empirical mode decomposition (EMD) is a recently proposed method to analyze non-linear and non-stationary time series and may be used for noise elimination. Similar to other decomposition based denoising approaches, EMD based denoising requires a reliable threshold to determine which oscillations called intrinsic mode functions (IMFs) are noise components or noise free signal components. Here, we propose a metric based on detrended fluctuation analysis (DFA) to define a robust threshold. The scaling exponent of DFA is an indicator of statistical self-affinity. In our study, it is used to determine a threshold region to eliminate the noisy IMFs. The proposed DFA threshold and denoising by DFA–EMD are tested on different synthetic and real signals at various signal to noise ratios (SNR). The results are promising especially at 0 dB when signal is corrupted by white Gaussian noise (WGN). The proposed method outperforms soft and hard wavelet threshold method.     

A novel context sensitive multilevel thresholding for image segmentation

Most of the traditional histogram-based thresholding techniques are effective for bi-level thresholding and unable to consider spatial contextual information of the image for selecting optimal threshold. In this article a novel thresholding technique is presented by proposing an energy function to generate the energy curve of an image by taking into an account the spatial contextual information of the image. The behavior of this energy curve is very much similar to the histogram of the image. To incorporate spatial contextual information of the image for threshold selection process, this energy curve is used as an input of our technique instead of histogram. Moreover, to mitigate multilevel thresholding problem the properties of genetic algorithm are exploited. The proposed algorithm is evaluated on the number of different types of images using a validity measure. The results of the proposed technique are compared with those obtained by using histogram of the image and also with an existing genetic algorithm based context sensitive technique. The comparisons confirmed the effectiveness of the proposed technique.     

基于平稳小波变换的心电信号去噪研究

讨论了经典的离散小波去噪原理和平稳小波变换的原理，分析了阈值以及阈值函数的选取方法。并将经典的离散小波和平稳小波用于心电信号去噪处理。通过在不同阈值和不同阈值函数条件下对心电信号处理的去噪效果的对比研究，说明了平稳小波相对于离散小波不仅提高了信噪比，还较好的抑制了Gibbs现象，取得了更好的去噪效果。     

Local entropy-based transition region extraction and thresholding

Transition region based thresholding is a newly developed approach for image segmentation in recent years. Gradient-based transition region extraction methods (G-TREM) are greatly affected by noise. Local entropy in information theory represents the variance of local region and catches the natural properties of transition regions. In this paper, we present a novel local entropy-based transition region extraction method (LE-TREM), which effectively reduces the affects of noise. Experimental results demonstrate that LE-TREM significantly outperforms the conventional G-TREM.     

Evaluation of neural network performance and generalisation using thresholding functions

The application of a simple thresholding technique to help assess the satisfactory performance of classification networks formed from Multi-Layer Perceptron (MLP) artificial neural networks (ANNs) is discussed. Both conventional Maximum Likelihood and Bayesian Evidence based training paradigms were implemented. Firstly a simulated data set drawn from a two-dimensional Gaussian distribution was investigated to illustrate the physical significance of the threshold plots compared to the classifier output probability contours. Secondly a real world application data set comprising of low-frequency vibration measurements on an aircraft wing (a GNAT trainer) is considered. It is demonstrated that simple threshold based plots applied to classifier network outputs may provide a simple yet powerful technique to aid in the rejection of poorly regularised network structures.     

Novel automatic video cut detection technique using Gabor filtering

Video shot transition identification constitutes an important computer vision research field, being applied, as an essential step, in many other digital video analysis domains: video scene detection, video compression, video indexing, video content retrieval and video object tracking. This paper approaches the video cut transition detection domain, providing a novel feature-based automatic identification method. We propose a feature extraction technique that uses 2D Gabor filtering, computing tridimensional image feature vectors for the video frames. Most shot cut detection techniques use a thresholding operation to discriminate between the inter-frame difference metric values and thus identify the video break points. Our identification approach is not threshold-based, using an automatic unsupervised distance classification procedure instead of a threshold. Thus, we provide a region-growing based classification approach, that proves to be very efficient in clustering the distances between feature vectors of consecutive frames. The two resulted distance classes determine a satisfactory video shot detection.     

Algorithms for Fuzzy Segmentation

Fuzzy segmentation is an effective way of segmenting out objects in pictures containing both random noise and shading. This is illustrated both on mathematically created pictures and on some obtained from medical imaging. A theory of fuzzy segmentation is presented. To perform fuzzy segmentation, a ‘connectedness map’ needs to be produced. It is demonstrated that greedy algorithms for creating such a connectedness map are faster than the previously used dynamic programming technique. Once the connectedness map is created, segmentation is completed by a simple thresholding of the connectedness map. This approach is efficacious in instances where simple thresholding of the original picture fails. Received: 22 October 1998?Received in revised form: 22 November 1998?Accepted: 2 December 1998     

Segmenting object with ambiguous boundary using information theoretic rough sets

In image segmentation, image is divided into regions of similar pixels that satisfy a defined notion of similarity. The complexity of image segmentation is further increased when the separation between neighboring regions is ambiguous. In this paper, we propose an approach that uses the information theoretic rough sets concept (ITRS) to model the ambiguous boundary of the object for further segmentation. The advantage of this approach is incorporating the prior knowledge of the object for effective extraction despite its ambiguous boundary. This approach starts with an assumption that seed points of the regions are available. It then computes the probability of association of the pixels with the seed points. Rough sets theory is invoked on this probability or likelihood map to identify positive, negative, and boundary states of the object. Optimal threshold for the boundary region is determined using histogram based segmentation algorithm for final object extraction. The main contribution relies on the application of ITRS in categorizing the object by combining both the prior and image information. The proposed approach, ITRS segmentation, is compared with different image segmentation methods on simulated brain images, and the result is encouraging with its state-of-the-art performance.     

一种用于截幅音频修复中的自适应一致迭代硬阈值算法

一致迭代硬阈值(CIHT)算法在处理音频截幅失真中具有较好的性能。但是,在截幅程度较大时音频截幅修复的性能会下降。因此,该文提出一种基于自适应门限的改进算法。该算法自动估计音频信号截幅程度,根据估计的截幅程度信息,自适应调整算法中的截幅程度因子。与近年来提出的CIHT算法和一致字典学习算法(CDL)相比,该文所提算法能更好地重建音频信号,特别在音频信号截幅失真严重的情况。该算法的运算复杂度与CIHT相近,与CDL相比,拥有更快的运行速度,有利于实时实现。

     

倒数粗糙熵图像阈值化分割算法

基于粗糙集理论的粗糙熵阈值法不需要图像之外的先验信息。粗糙熵阈值法需要解决两个问题,一是图像信息不完整性的度量,二是图像的粒化。该文基于倒数信息熵,提出一种倒数粗糙熵用来度量图像中信息的不完整性。为了更好地对图像进行粒化,采用一种基于均匀性直方图的粒子选取方式。该文提出的倒数粗糙熵表述简洁,计算简单。实验验证了该文方法的有效性。