Stereo image displaying based on both physiological and psychological stereoscopy from single image

Abstract

Image retrieval is a hot topic in the field of computer vision. Displaying query image and retrieved image as three-dimensional images can facilitate the users to evaluate the quality of the retrieved results. To show a single image in three-dimensional appearance, we propose to generate stereo image based on both physiological and psychological stereoscopy. We have found out how psychological stereoscopy can strengthen or weaken physiological stereoscopy. On the basis of this, we propose to introduce random binocular disparities into the single image. The net effect of this operation is the better stereo perception. When compared with other algorithms, the proposed algorithm is simple and the computational cost is relatively low. The effectiveness of our algorithm is demonstrated on different types of images. Since the 3D images are difficult to be captured, the algorithms we proposed aiming to generate stereo image from a single image are of great importance to the development of the field of the 3D vision.

A new methodology for evaluating various methods of 3D reconstruction from single image

“…c) Stereo image displaying based on both physiological and psychological stereoscopy from single image. ("Psych" for short) [11].…”

“…Binocular disparity is the most important element of physiological stereoscopy. It has been proved that after excluding all the psychological elements, it is possible that a set of visual stimulations can create the depth feeling with two eyes under the binocular disparity condition [11]. Due to the depth disparity of binocular stereoscopy the 3D content is perceived which also important in stereo imaging.…”

Depth map estimation based on linear regression using image focus

“…For 3D shape recovery (Hou,2008), we consider Shape From Focus (SFF), which is one of the optical passive 3D image reconstruction method. Techniques that retrieve spatial information, by looking at multiple images of the same scene, taken with different geometry or position of imaging devices, are classified as SFF (Rosenfeld,2000; Pradeep,2007).…”