A Novel Image Segmentation Algorithm via Neighborhood Principal Component Analysis and Laplace Operator

Image edge detection is sensitive to noise which is contained by natural images so that it affects the quality of the image segmentation. In order to remove noise and improve edge detection accuracy, then improving the quality of image segmentation, a novel image segmentation algorithm via neighborhood the principal component analysis and Laplace operator is proposed. The feature vectors of each pixel are extracted through the principal component analysis to obtain the main component, which effectively suppresses noise. Then the Laplace operator is used to detect the edge to realize the image segmentation. Compared to the traditional image segmentation of Sobel operator and LOG operator, the algorithm is proposed estimate the parameter values by principal component analysis instead of depending on experience. It can effectively reduce the noise on the image interference and simplify the computational complexity. Experimental results show that the algorithm can effectively improve the segmentation of the image with a strong advantage in the accuracy and robustness.