Global Estimation of Signed 3D Surface Tilt from Natural Images

The ability of human visual systems to estimate 3D surface orientation from 2D retinal images is critical. But the computation to calculate 3D orientation in real-world scenes is not fully understood. A Bayes optimal model grounded in natural statistics has explained 3D surface tilt estimation of human observers in natural scenes (Kim and Burge, 2018). However, the model is limited because it estimates only unsigned tilt (tilt modulo 180deg). We extend the model to predict signed tilt estimates and compared with human signed estimates. The model takes image pixels as input and produces optimal estimates of tilt as output, using the joint statistics of tilt and image cues in natural scenes. The image cues to tilt are the directions of luminance, texture, and disparity gradients in a local area on the image. To estimate signed tilt, the disparity cue is used as a signed tilt cue, and the luminance and texture cues are used as unsigned tilt cues. Given a particular set of local image cues, the model computes the minimum mean squared error (MMSE) estimate, which is equal to the posterior mean over signed tilt. We found that the signed MMSE estimates were well aligned with human signed tilt estimates on the identical set of stimuli. Next, we pooled the local MMSE estimates across the space to obtain a global tilt estimate. Given that local MMSE estimates are unbiased predictor of groundtruth tilt with nearly equal reliability, the global pooled estimates are also near-optimal. The global estimates even better explained human tilt estimation. We conclude that this computational model provides a tool to understand how human visual systems make the best use of 2D image information to compute local estimates and integrate a global estimate of 3D surface tilt in complex natural scenes using the local estimates

Focus error estimation in images

Estimating focus error in an image involves a training phase and an application phase. In the training phase, an optical system is represented by a point-spread function. An image sensor array is represented by one or more wavelength sensitivity functions, one or more noise functions, and one or more spatial sampling functions. The point-spread function is applied to image patches for each of multiple defocus levels within a specified range to produce training data. Each of the images for each defocus level (i.e. focus error) is sampled using the wavelength sensitivity and spatial sampling functions. Noise is added using the noise functions. The responses from the sensor array to the training data are used to generate defocus filters for estimating focus error within the specified range. The defocus filters are then applied to the image patches of the training data and joint probability distributions of filter responses to each defocus level are characterized. In the application phase, the filter responses to arbitrary image patches are obtained and combined to derive continuous, signed estimates of the focus error of each arbitrary image patch.Board of Regents, University of Texas Syste

When, where and how osteoporosis-associated fractures occur: An analysis from the global longitudinal study of osteoporosis in women (GLOW)

Objective: To examine when, where and how fractures occur in postmenopausal women. Methods: We analyzed data from the Global Longitudinal Study of Osteoporosis in Women (GLOW), including women aged ≥55 years from the United States of America, Canada, Australia and seven European countries. Women completed questionnaires including fracture data at baseline and years 1, 2 and 3. Results: Among 60,393 postmenopausal women, 4122 incident fractures were reported (86% non-hip, non-vertebral [NHNV], 8% presumably clinical vertebral and 6% hip). Hip fractures were more likely to occur in spring, with little seasonal variation for NHNV or spine fractures. Hip fractures occurred equally inside or outside the home, whereas 65% of NHNV fractures occurred outside and 61% of vertebral fractures occurred inside the home. Falls preceded 68-86% of NHNV and 68-83% of hip fractures among women aged ≤64 to ≥85 years, increasing with age. About 45% of vertebral fractures were associated with falls in all age groups except those ≥85 years, when only 24% occurred after falling. Conclusion: In this multi-national cohort, fractures occurred throughout the year, with only hip fracture having a seasonal variation, with a higher proportion in spring. Hip fractures occurred equally within and outside the home, spine fractures more often in the home, and NHNV fractures outside the home. Falls were a proximate cause of most hip and NHNV fractures. Postmenopausal women at risk for fracture need counseling about reducing potentially modifiable fracture risk factors, particularly falls both inside and outside the home and during all seasons of the year. © 2013 Costa et al

Shape, perspective, and what is and is not perceived

Supplemental materials for Burge & Burge (in press). Shape, perspective, and what is and is not perceived. Psychological Review

The Reverse Pulfrich Effect: Misperception of Motion in Depth

CIP 2019, San Lorenzo de El Escorial, Madrid, 20th - 22nd, 2019Peer reviewe

Natural scene statistics predict how humans pool information across space in surface tilt estimation.

Visual systems estimate the three-dimensional (3D) structure of scenes from information in two-dimensional (2D) retinal images. Visual systems use multiple sources of information to improve the accuracy of these estimates, including statistical knowledge of the probable spatial arrangements of natural scenes. Here, we examine how 3D surface tilts are spatially related in real-world scenes, and show that humans pool information across space when estimating surface tilt in accordance with these spatial relationships. We develop a hierarchical model of surface tilt estimation that is grounded in the statistics of tilt in natural scenes and images. The model computes a global tilt estimate by pooling local tilt estimates within an adaptive spatial neighborhood. The spatial neighborhood in which local estimates are pooled changes according to the value of the local estimate at a target location. The hierarchical model provides more accurate estimates of groundtruth tilt in natural scenes and provides a better account of human performance than the local estimates. Taken together, the results imply that the human visual system pools information about surface tilt across space in accordance with natural scene statistics