rivalry. There is a hysteresis effect associated with stereopsis. Once fusion and stereopsis have stabilized, fusion and stereopsis can be maintained even if the two images are pulled apart slowly and symmetrically to a certain extent in the horizontal direction. In the vertical direction, there is a similar but smaller effect. This effect, first demonstrated on a random dot stereogram, was initially interpreted as an extension of Panum's fusional area. Later it was shown that the hysteresis effect reaches far beyond Panum's fusional area, and that stereoscopic depth can be perceived in random-line stereograms despite the presence of cyclodisparities of about 15 deg, and this has been interpreted as stereopsis with diplopia. Interaction of stereopsis with other depth cues Under normal circumstances, the depth specified by stereopsis agrees with other depth cues, such as motion parallax (when an observer moves while looking at one point in a scene, the fixation point, points nearer and farther than the fixation point appear to move against or with the movement, respectively, at velocities proportional to the distance from the fixation point), and pictorial cues such as superimposition (nearer objects cover up farther objects) and familiar size (nearer objects appear bigger than farther objects). However, by using a stereoscope, researchers have been able to oppose various depth cues including stereopsis. The most drastic version of this is pseudoscopy, in which the half-images of stereograms are swapped between the eyes, reversing the binocular disparity. Wheatstone (1838) found that observers could still appreciate the overall depth of a scene, consistent with the pictorial cues. The stereoscopic information went along with the overall depth. Computer stereo vision Computer stereo vision is a part of the field of computer vision. It is sometimes used in mobile robotics to detect obstacles. Example applications include the ExoMars Rover and surgical robotics. Two cameras take pictures of the same scene, but they are separated by a distance – exactly like our eyes. A computer compares the images while shifting the two images together over top of each other to find the parts that match. The shifted amount is called the disparity. The disparity at which objects in the image best match is used by the computer to calculate their distance. For a human, the eyes change their angle according to the distance to the observed object. To a computer this represents significant extra complexity in the geometrical calculations (epipolar geometry). In