Correcting perceived perspective distortions using object specific planar transformations

Abstract

Distortions due to perspective projection is often described under the umbrella term of foreshortening in computer graphics and are treated the same way. However, a large body of literature from artists, perceptual psychologists and perception scientists have shown that the perception of these distortions is different in different situations. While the distortions themselves depend on both the depth and the orientation of the object with respect to the camera image plane, the perception of these distortions depends on other depth cues present in the image. In the absence of any depth cue or prior knowledge about the objects in the scene, the visual system finds it hard to correct the foreshortening automatically and such images need user input and external algorithmic distortion correction.In this paper, we claim that the shape distortion is more perceptible than area distortion, and quantify such perceived foreshortening as the non-uniformity across the image, of the ratio e of the differential areas of an object in the scene and its projection. We also categorize foreshortening into uniform and non-uniform foreshortening. Uniform foreshortening is perceived by our visual system as a distortion, even if e is uniform across the image, only when comparative objects of known sizes are present in the image. Non-uniform foreshortening is perceived when there is no other depth cue in the scene that can help the brain to correct for the distortion. We present a unified solution to correct these distortions in one or more non-occluded foreground objects by applying object-specific segmentation and affine transformation of the segmented camera image plane. Our method also ensures that the background undergoes minimal distortion and preserves background features during this process. This is achieved efficiently by solving Laplace's equations with Dirichlet boundary conditions, assisted by a simple and intuitive user interface.