The problem of identifying three dimensional objects from their two dimensional perspective projections is an important one in computer vision. A segmentation procedure is described here to extract features from a simulated image, then a matching procedure which finds the three dimensional objects in the picture is discussed. An image: a photograph taken by a camera with fixed focal length, is given, and a three dimensional wire frame object model is also given. Each object in the model is composed of planar arcs. The arc is bounded by connected line segments or conics. The input image is a simulated photographs of some objects of our model, the problem is to identify what these objects are. The two dimensional data structure we derived from image are equations of each planar arc. The data stored in the database are equations of the boundary pieces of the three dimensional object. Our aim is to find the match between the given three dimensional data structure and the two dimensional data structure. The method used here is a numerical analysis nonlinear optimization. / Ph. D.