An active contour model has been widely applied to image segmentation and analysis. A contour model is able to be deformed to fit an expected object based on energy minimization. However, the conditions of initializing a contour model and defining internal energy parameters are important factors to the desired deformation process. We propose a new method to lessen these conditions so that the contour can be attracted correctly to the boundary of an object. With the dynamic programming approach, the contour model resolves energy ambiguity occurring in the image input. Then, the deformation process focusses on energy optimization that allows the model to maintain a desirable shape of an expected object. Fuzzy logic is applied to measure energy ambiguity and to form explicit shape knowledge that will guide the contour formation. The experimental results show that the contour model is able to make better decision in deforming itself in spite of an improper contour initialization and parameter definitions.