The Research And Implementation Of Two Image Segmentation Algorithm

Image engineering is a subject that has been developed in recent years, and it has many contents. According to the degree of abstract and the investigate methods, the research on it can be divided into three levels: image processing, image analysis and image comprehension.Image segmentation is a critical image analysis technique. It is necessary in the process of image object' s extraction and measurement. The result of the image segmentation is the basis of the feature extraction and pattern recognition. The research of the image segmentation is the hot spot and focus of the image technology. This paper make study of and implement the image segmentation.According to the character of the flyers, a one-dimension maximum entropy image binary conversion method based on edge features is proposed, which can implement adaptive threshold selection. Our experiments show that the new method overcomes the disadvantage of image segmentation with one-dimension maximum entropy and keeps the original edge features well. This method is simple. It is implementing. Especially, it is efficient for processing low quality and edge fuzzy images. We discuss the findings of the experiments in detail and draw good conclusions.Based on the idea of the information fusion, this paper proposes the image segmentation algorithm, which uses fusion of gray information and texture information. And it takes the pixel fusion technology. All this boosts up the image object. The validity of this algorithm is verified in the experiments.