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skin color model
相关语句
  肤色模型
    Considering the non-linearity among chroma (Cb, Cr component) and luminance (Y component) of skin color in the high and low luminance region, the skin color model is constructed by two lookup tables in Y-Cb and Y-Cr subspaces.
    考虑到在高亮度区和低亮度区肤色色度(Cb,Cr分量)与亮度(Y分量)非线性相关,采用Y-Cb和Y-Cr两个子空间的查询表来建立肤色模型
    Face Detection Under Rotation in Image Plane Using Skin Color Model Neural Network and Feature-based Face Model
    基于肤色模型、神经网络和人脸结构模型的平面旋转人脸检测
短句来源
    A Study of Building Skin Color Model of Lookup Table in YCbCr Color Space Based on Bayes Decision
    基于贝叶斯判决的关于YCbCr空间的肤色模型查询表建立的研究(英文)
短句来源
    Human face detection based on skin color model and BDF
    基于肤色模型和贝叶斯判别的人脸检测
短句来源
    Face Detection Based on Skin Color Model and FCM Dynamic Clustering
    基于肤色模型和FCM动态聚类的人脸检测
短句来源
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  皮肤颜色模型
    Hand area is segmented based on skin color model, then the hand contour is described by use of Fourier descriptor.
    基于皮肤颜色模型进行手势分割,并用傅里叶描述子描述轮廓。
短句来源
  “skin color model”译为未确定词的双语例句
    Then,we build a skin color model in YCbCr color space using the convergent property of skin color,and we present the Gaussian Model Skin Recognition Method and Positive-Negative Look-up Table Method in details.
    然后,利用皮肤颜色的聚合性,在YCbCr颜色空间建立了皮肤的颜色模型,并分别阐述了基于高斯分布模型的皮肤检测法和正反概率表方法;
短句来源
    Two face detection methods are proposed in our prototype system, they are base on skin color model in HSV chrominance space and Haar-like feature.
    在人脸检测环节,本文采用了两种方案:基于肤色特征的CAMSHIFT算法和基于局部Haar特征的算法;
短句来源
    Two face detection methods are proposed in our prototype system. One is using Adaboost learning method and the other is based on skin color model in YCbCr chrominance space.
    在人脸检测环节,本文采用了两种方案:基于Adaboost的人脸检测和基于肤色的人脸检测。
短句来源
    Three skin color model in the HSI and YCbCr color space are constructed and their advantages and disadvantages are analyzed, the strategies that combine the different images after color segmentation is given.
    实验结果表明,在肤色分割阶段,采取了在YCbCr和HSI色彩空间进行肤色和非肤色的分割,并对不同的分割图像进行融合,这样能较好地获取肤色区域;
短句来源
    The method involves locating human face like regions by substracting two adjacent pictures of asreies and using skin color model to segement face.
    然后根据肤色的特性,建立高斯概率模型,并利用投影法和模板匹配方法进行人脸的精确定位。
短句来源
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  skin color model
We build a skin color model with Gaussian distribution and segment face region for each frame of incoming image stream.
      
With this Gaussian skin color model, we segment face region for each frame of the video, so we can obtain face image stream.
      
We use the images from internet to learn the skin-color model.
      
We also reported a comprehensive analysis of the Bayesian skin color model together with several interesting results.
      
The skin color region is extracted based on the skin color model.
      
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For speech recognition systems under noisy environment, lip reading technique can effectively reduce the influence of noise and improve the accurate rate of speech recognition system by adding visual information to acoustic channel. In this paper, an effective and robust approach for lip and mouth locating and tracking is presented to enable the information extraction under abnormal illumination and without special marks. This approach first locates face region with skin color model,...

For speech recognition systems under noisy environment, lip reading technique can effectively reduce the influence of noise and improve the accurate rate of speech recognition system by adding visual information to acoustic channel. In this paper, an effective and robust approach for lip and mouth locating and tracking is presented to enable the information extraction under abnormal illumination and without special marks. This approach first locates face region with skin color model, then finds the eyes from the face region with iterative algorithm, modifies the position and size of face according to the position of eyes, transforms the lower part of face by specific color coordinators to clearly distinguish lip color from skin color, and finally describes the outline of upper lip and lower lip with deformable template.

在许多应用于有噪声环境下的语音识别系统中 ,唇读技术能有效地降低噪声的影响 ,通过视觉通道来补充仅取决于听觉通道的信息量 ,从而提高语音识别系统的识别率 .该文提出了一种有效和稳健的唇定位跟踪方法 ,以满足不用特殊标识物和规范性照明就能对信息进行有效提取的应用需求 .该方法首先用肤色模型查找脸 ;然后用迭代算法搜索脸部区域内的眼睛 ;再根据眼睛的位置来确定脸的大小和位置 ,并对脸的下半部分采用彩色坐标变换法将唇从肤色中明显地区分出来 ;最后 ,用可变模板将上下唇的内外轮廓描述出来 .

In this paper, a face detection algorithm for color images based on skin color and template, which is composed of skin color segmentation, template matching, and neural network verifying is presented. First, a skin color model in HSI chrominance space is used for segmenting regions in which may have faces, and then the average-face based template matching and neural network verifying methods are used for searching faces in those regions. This algorithm integrates skin color information in...

In this paper, a face detection algorithm for color images based on skin color and template, which is composed of skin color segmentation, template matching, and neural network verifying is presented. First, a skin color model in HSI chrominance space is used for segmenting regions in which may have faces, and then the average-face based template matching and neural network verifying methods are used for searching faces in those regions. This algorithm integrates skin color information in color images with template matching and neural network classification model in gray level images, which results in not only faster speed, but also higher robustness of the algorithm. Experimental results demonstrate the efficiency and feasibility of this algorithm.

针对彩色图像提出了一种基于肤色和模板的人脸检测方法 ,由肤色分割、模板匹配和人工神经网验证 3部分组成 .首先使用 HSI空间的肤色统计模型分割出可能包含人脸的区域 ,然后使用平均脸模板匹配和人工神经网验证的方法在这些区域中搜索人脸 .该方法将彩色图像的肤色信息和灰度图像的模板匹配及人工神经网分类模型综合起来 ,既极大地提高了速度 ,又具有较强的鲁棒性 .实验结果表明 ,该算法是快速而有效的 .

An approach to face detection which based on skin color model and eigenfaces is introduced. With the stability of human skin color's distribution in the chromatic space, the skin color areas are segmented. Further investigation is made based on the SNR of their reconstruction image in the eigenface subspace. The result of the experiment proved to be promising.

提出的人脸检测方法利用人类肤色在色度空间分布的稳定性 ,检测出图像中的皮肤区域 ,然后将其在特征脸空间中投影、重建 ,通过求重建图像的信噪比进行判断。实验结果证明了方法的有效性

 
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