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Robotic vision systems make use of image shapes to identify and locate objects in the workspace of the robot. For objects observed by a normal lens system, and centered in the image, shape parameters can be reliably extracted and compared to reference models by a number of well-known techniques. In many applications, it appears desirable to mount the camera very close to the workspace, and to use a wide angle lens. This overcomes problems caused by congested manufacturing workstations, and allows a wide field of vision. Unfortunately, the shapes of objects in the peripheral area of camera images have a distortion, inherent to planar projective mapping, which increases with increasing angles measured from the optical axis. A significant portion of the image area from a wide angle lens falls in this category. When the objects contain depth, this distortion alters angles and sizes of solid objects. This paper presents a transformation that corrects the image shape of any selected object or region in a wide angle digital image. It performs the transformation by defining a virtual camera to "take a picture" of the image with the desired orientation. A new, simulated, image is formed with correct shapes and angles making shape recognition easier. Examples of the implementation of this algorithm are presented.