2.5D (visual perception)
Encyclopedia
2.5D describes effects in visual perception — especially stereoscopic vision — where the 3D environment of the observer is projected onto the 2D planes of the retinas. Thus, while the effect is still effectively 2D, it allows for depth perception. A specific aspect of stereoscopic vision in the perception of depth is that depth perception is easier when it involves evaluating the disparity between two items in the field of view compared to evaluating the exact depth of a lone, single item in the environment.

2.5D is the construction of a three dimensional environment from 2D retinal projections. 2.5D is inherently the ability to perceive the physical environment, which allows for the understanding of relationships between objects and ourselves within an environment. Perception of the physical environment is limited because of the visual and cognitive problem. The visual problem is the lack of objects in three dimensional space to be imaged with the same projection and the cognitive problem is that any object can be a different object depending on the perceiver. David Marr
David Marr
David Courtnay Marr was a British neuroscientist and psychologist. Marr integrated results from psychology, artificial intelligence, and neurophysiology into new models of visual processing...

’s work on the 2.5D Sketch has found that 2.5D has visual projection constraints. 2.5D projection constraints exist because "parts of images are always (deformed) discontinuities in luminance"; therefore, in reality we do not see all of our surroundings but construct the viewer-centered three dimensional view of our environment.

A primary aspect in regards to the human visual system is blur perception. It plays a vital aspect in ocular focusing in order for one to attain clarity central to retinal-imagery.
Visual perception is a complex system in which blur perception plays a key role in focusing on near or far objects. Retinal focus patterns are critical in blue perception. These patterns are comprised of distal and proximal retinal defocus. Depending on the object’s distance and motion from the individual viewing it, these patterns contain a balance or an imbalance of focus in both directions. (CiuVreda, 2006)

"An empirically based, conceptual model of human blur perception is shown. It incorporates the concepts of blur detection and blur discrimination in depth, and across the central and peripheral retina, in two-dimensional visual space. main things are its dynamic nature, predictability regarding the blur-based depth-ordering of objects, patterns of retinal de-focus with far and near viewing, and interactions related to retinal de-focus with the central and peripheral retina. Also, a two-dimensional schematic rshowing of the blur-free region during near viewing is depicted in dioptric space. This model has implications with respect to accommodation control, depth perception, and refractive error development and progression."(4) Summarizing that point up, the human blur perception involves ideas of blur detection and blur discrimination in detail. It also goes across the central and peripheral retina. The model has a very changing nature, its shown that a model of the blur perception is in dioptric space while in near viewing. The model can have suggestions according to depth perception and accommodating control.

"A 2.5D interframe motion model is proposed so that the stabilization system can perform in situations where significant depth changes are present and the camera has both rotation and translation. Inertial motion filtering is proposed in order to eliminate the vibration of the video sequences with enhanced perceptual properties. The implementation of this new approach integrates four modules: pyramid-based motion detection, motion identification and 2.5D motion parameter estimation, inertial motion filtering, and affine-based motion compensation. The stabilization system can smooth unwanted vibrations or shakes of video sequences and achieve real-time speed. We test the system on IBM PC compatible machines and the experimental results show that our algorithm outperforms many algorithms which require parallel pipeline image processing machines." (5)
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