Traditional Fuzzy C-Means (FCM) clustering algorithm only considers the characteristics of a single pixel when applied to liver CT image segmentation, and it can not overcome the influence of uneven gray scale and the problem of boundary leakage caused by blurred liver boundary. In order to solve the problems, a Spatial Fuzzy C-Means (SFCM) clustering segmentation algorithm combined with spatial constraints was proposed. Firstly, the convolution kernel was constructed by using two-dimensional Gauss distribution function, and the feature matrix could be obtained by using the convolution kernel to extract the spatial information of the source image. Then, the penalty term of spatial constraint was introduced to update and optimize the objective function to obtain a new iteration equation. Finally, the liver CT image was segmented by using the new algorithm. As shown in results, the shape of liver contour splited by SFCM is more regular when segmenting liver CT images with gray unevenness and boundary leakage. The accuracy of SFCM reaches 92.8%, which is 2.3 and 4.3 percentage points higher than that of FCM and Intuitionistic Fuzzy C-Means (IFCM). Also, over-segmentation rate of SFCM is 4.9 and 5.3 percentage points lower than that of FCM and IFCM.

A method of object detection with few samples based on two-stage voting was proposed to detect objects using template matching method while there are only a few samples. Firstly, the voting space was constructed off-line by using probability model through several samples. Then, a method of two-stage voting was used to detect objects in testing images. In the first stage, the components of object from testing image were detected, and the positions of components in query image were saved. In the second stage, the similarity of the object was computed integrally based on the components. According to the theory analysis and experimental results, the proposed method obtains lower computation complexity and higher precisions than previous works.