颜色特征对图像的各种变换具有很好的鲁棒性,使其在图像检索中发挥了重要作用,但传统颜色特征中缺乏空间分布信息,检索性能不佳.通过图像分块捕获颜色的空间分布信息,并引入高效匹配核,把局部颜色特征映射到低维空间,生成比传统颜色特征...颜色特征对图像的各种变换具有很好的鲁棒性,使其在图像检索中发挥了重要作用,但传统颜色特征中缺乏空间分布信息,检索性能不佳.通过图像分块捕获颜色的空间分布信息,并引入高效匹配核,把局部颜色特征映射到低维空间,生成比传统颜色特征RGB、HSV等判别性能更优的颜色高效匹配核特征CEMK(Color Efficient Match Kernels),基于CEMK完成商品图像检索.结果表明:颜色高效匹配核能有效地改善基于颜色的图像检索性能.展开更多
A face detection method using statistical skin-color model and facial feature matching is presented in this paper. According to skin-color distribution in YUV color space,we develope a statistical skin-color model thr...A face detection method using statistical skin-color model and facial feature matching is presented in this paper. According to skin-color distribution in YUV color space,we develope a statistical skin-color model through interactive sample training and learning. Using this method we convert the color image to binary image and then segment face-candidate regions in the video images. In order to improve the quality of binary image and remove unwanted noises ,filtering and mathematical morphology are empolied. After these two processing,we use facial feature matching for further detection. The presence or absence of a face in each region is verified by means of mouth detector based on a template matching method. The experimental results show the proposed method has the features of high speed and high efficiency,but also robust to face variation to some extent. So it is suitable to be applied to real-time face detection and tracking in video sequences.展开更多
文摘颜色特征对图像的各种变换具有很好的鲁棒性,使其在图像检索中发挥了重要作用,但传统颜色特征中缺乏空间分布信息,检索性能不佳.通过图像分块捕获颜色的空间分布信息,并引入高效匹配核,把局部颜色特征映射到低维空间,生成比传统颜色特征RGB、HSV等判别性能更优的颜色高效匹配核特征CEMK(Color Efficient Match Kernels),基于CEMK完成商品图像检索.结果表明:颜色高效匹配核能有效地改善基于颜色的图像检索性能.
文摘A face detection method using statistical skin-color model and facial feature matching is presented in this paper. According to skin-color distribution in YUV color space,we develope a statistical skin-color model through interactive sample training and learning. Using this method we convert the color image to binary image and then segment face-candidate regions in the video images. In order to improve the quality of binary image and remove unwanted noises ,filtering and mathematical morphology are empolied. After these two processing,we use facial feature matching for further detection. The presence or absence of a face in each region is verified by means of mouth detector based on a template matching method. The experimental results show the proposed method has the features of high speed and high efficiency,but also robust to face variation to some extent. So it is suitable to be applied to real-time face detection and tracking in video sequences.
