The research team is led by Masakazu Ohara, a graduate student of the Department of Computer Science and Engineering at Toyohashi University of Technology (a student in the Doctoral Program of the Leadership Program); Associate Professor Kowa Koida from the Electronics-Inspired Interdisciplinary Research Institute; and Professional Professor Juno Kim of the University of New South Wales (Australia) found that when people judge the thickness of an object, objects with obvious optical properties resemble flatter glass than they actually are.
It was previously known that objects made of metabolic or glossy materials are thought to be thicker than they are, but now current research has indicated that visible properties have a pronounced effect.
The results of the analysis of image locks that contribute to thickness judgments showed that thickness could be sensed by people based on image properties in the form of regional variations of local light contrast.
With this model of computing, it is now possible to predict what types of images in which the shape of 3D objects may be misinterpreted by humans, which may be useful for everyday applications, such as tools to help walking in visually impaired people or autonomous driving.
The surface texture of an object is also extremely important for product development because it is attractive to the human senses (e.g., sight, and touch).
We can draw a lot from the appearance of textures. For example, the sense that the slippery ground is associated with glory and understanding is a combination of surface material, but if this visual estimate is erroneous, an accident can occur while walking or driving.
In addition, when surface gloss or transparency is misdiagnosed, the bumps on the ground may also be misinterpreted.
Humans identify the texture and optical properties of a surface with just a glance, even if the necessary neural processing is complex. Accordingly, understanding the way in which the eyes and brain communicate our knowledge of texture is still of great interest.
When the human brain monitors an object, it tries to illuminate and process the illumination that strikes the object, the 3D shape of the object, and the optical properties of the surface. Certainly, images are created by physically and precisely combining these three elements.
However, it is still a difficult fundamental problem to study how the visual system deals with these three elements. There have been many reports where comments are not usually correct. For example, if a surface is glossy and in contrast to a matte surface without gloss, it has been reported that the bumps on the object should appear as exfoliating (Mooney & Anderson, 2014; Published in Conventional Biology).
A device is an optical phenomenon associated with the reflection of light from the surface of an object. However, the property of apparent refraction is also an optical phenomenon of objects. Whether transparency is a fundamental property, visual properties and their understanding of 3D objects have not yet been studied.
Therefore, this research group compared transparent properties and a normal matte and glossy surface to understand how different materials affect the understanding of 3D shape.
Participants in the experiment looked at computer-generated spherical objects displayed on a computer screen, and were asked to estimate the thickness guided in depth. Several items with different thickness and surface materials were prepared for comparison.
As a result, objects made of transparent material were seen to be flatter than the materials with similar shape but with a mixture of different materials. This effect occurred regularly even when: i) the object had a lump in its shape, ii) the significant lighting environment was altered, iii) the object was resized, iv) the object was moved left and right, and v) whether an object was viewed with one eye only.
The above results suggest that obvious features influence people’s understanding of the shape of 3D material in different ways to diffuse and speculative reflection.
The research group also analyzed the images to find out what kind of stuff in the image added something to a thick sense. The factor that produced the most accurate estimates of the understanding of thickness was the degree of regional variation of local variance in the image.
Local diffraction is the root-square difference (RMS) of the pixels within a small region of the image, and this correlation is similar to those produced by neurons found in the early visual field of the image. retina and the brain. The regional variance is measured as the change from the sum of the above RMS contrast values over a wide range of view.
The research team found that obvious material properties can cause a 3D shape of a material to be underestimated. However, it is not known whether these misinterpretations are a simple mistake or an entirely indirect effect on individual equipment. The cause of the misconceptions needs to be examined in detail in later research.
Also, not only is the computational model for predicting a thick understanding useful for understanding the cloud devices of the human visual system, but it is also linked to prediction of situations. in which people could misinterpret the shape or texture of objects.
Errors in defining even a surface or whether a frozen surface can cause accidents when walking or driving a car.
In situations where gloss, transparency, or lumps of 3D material can be misinterpreted, it can be expected that the computer model could be used in devices to aid movement when walking, such as smart glasses, or semi-independent driving activities to warn drivers before an accident.
Source:
Toyohashi University of Technology