Fuzzy edge is the essential character of image and the main obstacle for image comprehension. That is why the fuzzy mathematics applied to edge detection widely. But most of fuzzy algorithms could not express the fuzziness and random of edge of image precisely. Edge information with lower gray-level was lost, and the result lacks precision. Cloud model is a flexible and useful model for treating uncertain issues. It considers fuzziness and random at the same time, overcomes the limitation of classical fuzzy algorithms. The paper proposes Object-Cloud Algorithm (OCA). In OCA, we consider that objects in image are fuzzy objects with uncertain edge. The degree membership of each pixel belonging to an object is inverse proportion with the distance between pixel and center of the object. So each object can be represented with a cloud, which can be called "object-cloud". Based on "object-cloud", OCA replaces microcosmic pixels with macroscopical objects. Pixel is just element of "object-cloud", and edge information with lower gray-level can be preserved precisely. OCA has two steps. First, creating a cloud for each object in image. Uncertain point and uncertain polyline in image can be treated as uncertain area. Area-cloud includes "core" and "half-cloud loop" outside the core. It can be represented by model A(Area,En, He) based on cloud theory. En is entropy. He is hyperentropy. Generating core (Area) is the precondition of creating an uncertain area-cloud. Second, achieving the fuzzy edge of object in image. After creating "object-cloud" for each object in image, adjacent area can be represented by intersecting clouds. We can gain a new "boundary-cloud" by Boolean calculation between two or more intersecting clouds which have public boundary. The fuzzy edge between objects in image can be detected by digital characteristics of "boundary-cloud" and cloud calculation.
