Estimation of probabilities of label imperfections and correction of mislabels

Schemes that use both the imperfectly labeled and unlabeled pattern sets for the estimation of probabilities of label imperfections and correction of mislabels are presented in this paper. Using relationships between the class conditional densities, and a priori probabilities with and without imperfections in the labels, the problem of estimating probabilities of label imperfections is formulated as that of minimizing the Bayes probability of error. Experimental results are presented from the processing of remotely sensed multi-spectral scanner imagery data. A thresholding scheme is proposed for the correction of pattern mislabels. For a symmetric mislabeling case, a relationship is developed between the probability that such a scheme gives a bad label to a pattern and the probability that the scheme accepts the original label of the pattern. This relationship could be used for computing the threshold from unlabeled samples for a specified probability of bad labeling. An example is presented to illustrate the behavior of the scheme. Furthermore, bounds are presented between the Bayes errors with and without imperfections in the labels and are shown to become equalities when the imperfections in the labels become symmetric.     

基于交叉熵阈值法的快速迭代算法

针对交叉熵阈值法的时间复杂性过大的不足,提出了基于目标函数最优化原理的交叉熵分割准则的快速迭代算法.大量的实验结果表明,提出的快速迭代算法是有效的.     

小波变换在手持心电图机中的心电信号去噪应用

本文以小波变换为基础,介绍了小波去噪的方法和步骤,并通过具体的实例分析,说明小波去噪的效果明显优于传统的滤波效果。然后利用软硬阈值小波去噪算法对ECG信号进行消噪处理。结果表明,软阈值去噪的效果明显的优于硬阈值去噪。     

基于遗传算法的图像多阈值自动分割方案

图像多阈值分割是图像处理的基本技术之一。遗传算法则是一种借鉴生物界自然选择和自然遗传机制的高度并行、随机、自适应的搜索算法。本文将遗传算法引人图像分割,提出一种新的图像多阈值分割方案,并加以改进。该方案能够快速正确地实现分割,且不需事先认为的确定分割类数。实验结果令人满意。     

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。     

用自定义图案对彩色图象二值化处理

研究了Photoshop抖动处理的原理与过程,巧妙定义二值图案,然后对此二值图案进行模糊化处理,使彩色图象变成了一幅由自定义图案所组成的二值化图象。     

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.