Semantic feature extraction from signal and image using lifting wavelet filters.

提升小波被称为第二代小波，它是由朗讯科技贝尔实验室的维姆·斯韦尔登斯开发的。提升小波具有包含可控自由参数的提升项。提升格式是通过将提升项添加到初始双正交小波滤波器中而构造的一组下采样滤波器。所构造的滤波器也是双正交小波滤波器，并且保持了完美的重构。在本研究中，我们提出了一种新的确定提升项中自由参数的学习方法，并开发了一个基于学习提升方案的运动目标跟踪系统。然后，利用我们的提升二进小波格式给出了确定提升参数的各种学习方法，它是双正交提升格式的扩展版本。利用所学习的提升算法，我们开发了图像提取算法，并利用视频帧采集的人脸图像构建了身份识别系统。此外，利用二进提升格式，给出了用二进小波设计双正交小波的方法。此外，还研究了利用小波多分辨率分析生成三维物体。

Lifting wavelet is called the second generation wavelet, which is developed by Wim Sweldens of Lucent Technologies' Bell Labs. The lifting wavelet has a lifting term which incorporates controllable free parameters. Lifting scheme is a set of the down sampling filters constructed by adding the lifting term to initial biorthogonal wavelet filters. The constructed filters are also biorthogonal wavelet filters and keep the perfect reconstruction. These are very important properties for the lifting wavelet filters.In this research, using such properties, we proposed a new learning method for determining the free parameters in the lifting term and developed a tracking system of moving objects based on the learned lifting scheme. And then, the various learning methods to determine the lifting parameters were produced by our lifting dyadic wavelet scheme which is extended version of the biorthogonal lifting scheme. Using the learned lifting scheme, we developed the image extraction algorithm and constructed person identification system via facial images captured by video frames. Furthermore, using the dyadic lifting scheme, we presented the technique for designing biorthogonal wavelets by dyadic wavelet. In addition, we studied to generate 3D objects by utilizing the wavelet multi-resolution analysis.