Computational estimation of scene structure through texture gradient cues

Tyler, C. W. & Gopi, A. (2017). Computational estimation of scene structure through texture gradient cues. Paper presented at the IS&T International Symposium on Electronic Imaging Science and Technology 2017, 29 Jan - 2 Feb 2017, California, USA.

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Abstract

Analyzing the depth structure implied in two-dimensional images is one of the most active research areas in computer vision. Here, we propose a method of utilizing texture within an image to derive its depth structure. Though most approaches for deriving depth from a single still image utilize luminance edges and shading to estimate scene structure, relatively little work has been done to utilize the abundant texture information in images. Our new approach begins by analyzing the two cues of local spatial frequency and orientation distributions of the textures within an image, which are used to compute the local slant information across the image. The slant and frequency information are merged to create a unified depth map, providing an important channel for image structure information that can be combined with other available cues. The capabilities of the algorithm are illustrated for a variety of images of planar and curved surfaces under perspective projection, in most of which the depth structure is effortlessly perceived by human observers. Since these operations are readily implementable in neural hardware in early visual cortex, they therefore represent a model of the human perception of the depth structure of images from texture gradient cues.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Reprinted with permission of IS&T: The Society for Imaging Science and Technology sole copyright owners of Electronic Imaging, Human Vision and Electronic Imaging 2017.
Divisions: School of Health Sciences > Department of Optometry & Visual Science
URI: http://openaccess.city.ac.uk/id/eprint/19332

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