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

使用提升小波滤波器从信号和图像中提取语义特征。

• 批准号: 15300048
• 负责人: NIIJIMA Koichi
• 金额: $ 10.11万
• 依托单位: KYUSHU UNIVERCITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2003
• 资助国家: 日本
• 起止时间: 2003 至 2006
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15300048/
• 关键词: biorthogonal wavelet; dyadic wavelet; lifting scheme; free parameters; learning; multi-resolution analysis; person identification; リフティングウェーブレット; 方向余弦最大化学習; 分散最大化学習; カートシス最小化学習; 対象物検出; 表情認識; 顔画像認識; リフティング双直交ウェーブレットフィルタ; 顔部位; 逐次学習; 追跡; リフ

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.

提升小波称为第二代小波，由朗讯科技贝尔实验室的 Wim Sweldens 开发。提升小波具有包含可控自由参数的提升项。提升方案是通过在初始双正交小波滤波器上添加提升项而构造的一组下采样滤波器。构造的滤波器也是双正交小波滤波器并保持完美的重构。这些对于提升小波滤波器来说是非常重要的属性。在本研究中，利用这些属性，我们提出了一种新的学习方法来确定提升项中的自由参数，并基于学习的提升方案开发了运动物体的跟踪系统。然后，通过我们的提升二进小波方案（双正交提升方案的扩展版本）产生了确定提升参数的各种学习方法。利用学习到的提升方案，我们开发了图像提取算法，并通过视频帧捕获的面部图像构建了人员识别系统。此外，利用二进提升格式，我们提出了二进小波设计双正交小波的技术。此外，我们还研究了利用小波多分辨率分析来生成 3D 对象。

项目成果

Fast face detection by lifting dyadic wavelet filters

• DOI: 10.1109/icip.2003.1247389
• 发表时间: 2003-11
• 期刊: Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429)
• 作者: Turghunjan Abdukirim Türüki;K. Niijima;S. Takano

Ryuichi Ikeura, Koichi Niijima, Shigeru Takano: "Fast object tracking by lifting wavelet filters"Proc.IEEE International Symposium on Signal Processing and Information Technology(ISSPIT2003). (CDROM). (2003)

Ryuichi Ikeura、Koichi Niijima、Shigeru Takano：“通过提升小波滤波器进行快速对象跟踪”Proc.IEEE 国际信号处理和信息技术研讨会(ISSPIT2003)。

Lifting Wavelet Based Cognitive Vision System

基于提升小波的认知视觉系统

• 发表时间: 2007
• 期刊: Proceedings of the 5th International Conference on Computer Vision Systems (ICVS 2007) (CD-ROM)
• 作者: Y.Higashijima;S.Takano;K.Niijima
• 通讯作者: K.Niijima

Video scene retrieval based on the Layerization of images and the matching of layer-trees,

基于图像分层和层树匹配的视频场景检索，

• 发表时间: 2005
• 期刊: Proc. of the Image and Vision Computing New Zealand 2005 (IVCNZ2005)
• 作者: N.Nakamura;S.Takano;K.Niijima
• 通讯作者: K.Niijima

Time-tunnel : Visual analysis tool for Time-series numerical data and its extension toward parallel coordinates

时间隧道：时间序列数值数据的可视化分析工具及其向平行坐标的扩展

• 发表时间: 2005
• 期刊: Computer Graphics, Imaging and Visualization as Proc.of CGIV 05 (IEEE CS Press)
• 作者: H.Notsu;Y.Okada;Y.Akaishi;K.Niijima
• 通讯作者: K.Niijima