采用"Video Pro 32"彩色图像分析系统,对小白鼠心脏切片中的胶原纤维容积分数进行定量分析。作者根据专业研究需要,编写了程序,开发新的应用功能。结果测量出小白鼠左心室心内膜下、心外膜下心肌中胶原纤维的容积分数及右心...采用"Video Pro 32"彩色图像分析系统,对小白鼠心脏切片中的胶原纤维容积分数进行定量分析。作者根据专业研究需要,编写了程序,开发新的应用功能。结果测量出小白鼠左心室心内膜下、心外膜下心肌中胶原纤维的容积分数及右心室胶原纤维的容积分数的平均值。心肌动脉血管周围胶原纤维面积及其和该动脉面积之比。满足了研究者的要求。展开更多
A color based system using multiple templates was developed and implem ented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color stati...A color based system using multiple templates was developed and implem ented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the fronta l human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution o f human skin in chromatic color space in order to get a chroma chart showing lik elihoods of skin colors. This chroma chart is used to generate, from the origina l color image, a gray scale image whose gray value at a pixel shows its likelih ood of representing the skin. The algorithm uses an adaptive thresholding proces s to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates ma tching is used to determine if a given skin region represents a frontal human fa ce or not. Test of the system with more than 400 color images showed that the re sulting detection rate was 83%, which is better than most color-based face dete c tion systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.展开更多
文摘采用"Video Pro 32"彩色图像分析系统,对小白鼠心脏切片中的胶原纤维容积分数进行定量分析。作者根据专业研究需要,编写了程序,开发新的应用功能。结果测量出小白鼠左心室心内膜下、心外膜下心肌中胶原纤维的容积分数及右心室胶原纤维的容积分数的平均值。心肌动脉血管周围胶原纤维面积及其和该动脉面积之比。满足了研究者的要求。
文摘A color based system using multiple templates was developed and implem ented for detecting human faces in color images. The algorithm consists of three image processing steps. The first step is human skin color statistics. Then it separates skin regions from non-skin regions. After that, it locates the fronta l human face(s) within the skin regions. In the first step, 250 skin samples from persons of different ethnicities are used to determine the color distribution o f human skin in chromatic color space in order to get a chroma chart showing lik elihoods of skin colors. This chroma chart is used to generate, from the origina l color image, a gray scale image whose gray value at a pixel shows its likelih ood of representing the skin. The algorithm uses an adaptive thresholding proces s to achieve the optimal threshold value for dividing the gray scale image into separate skin regions from non skin regions. Finally, multiple face templates ma tching is used to determine if a given skin region represents a frontal human fa ce or not. Test of the system with more than 400 color images showed that the re sulting detection rate was 83%, which is better than most color-based face dete c tion systems. The average speed for face detection is 0.8 second/image (400×300 pixels) on a Pentium 3 (800MHz) PC.
