Perception of Relative Depth Interval: Systematic Biases in Perceived Depth

Given an estimate of the binocular disparity between a pair of points and an estimate of the viewing distance, or knowledge of eye position, it should be possible to obtain an estimate of their depth separation. Here we show that, when points are arranged in different vertical geometric configurations across two intervals, many observers find this task difficult. Those who can do the task tend to perceive the depth interval in one configuration as very different from depth in the other configuration. We explore two plausible explanations for this effect. The first is the tilt of the empirical vertical horopter: Points perceived along an apparently vertical line correspond to a physical line of points tilted backwards in space. Second, the eyes can rotate in response to a particular stimulus. Without compensation for this rotation, biases in depth perception would result. We measured cyclovergence indirectly, using a standard psychophysical task, while observers viewed our depth configuration. Biases predicted from error due either to cyclovergence or to the tilted vertical horopter were not consistent with the depth configuration results. Our data suggest that, even for the simplest scenes, we do not have ready access to metric depth from binocular disparity.</jats:p

Image Retrieval with Semantic Sketches

With increasingly large image databases, searching in them becomes an ever more difficult endeavor. Consequently, there is a need for advanced tools for image retrieval in a webscale context. Searching by tags becomes intractable in such scenarios as large numbers of images will correspond to queries such as “car and house and street”. We present a novel approach that allows a user to search for images based on semantic sketches that describe the desired composition of the image. Our system operates on images with labels for a few high-level object categories, allowing us to search very fast with a minimal memory footprint. We employ a structure similar to random decision forests which avails a data-driven partitioning of the image space providing a search in logarithmic time with respect to the number of images. This makes our system applicable for large scale image search problems. We performed a user study that demonstrates the validity and usability of our approach

Designing co-located tabletop interaction for rehabilitation of brain injury

This paper surveys emerging design research on co-located group interaction with tabletop displays as an approach toward developing an upper-limb movement rehabilitation system for acquired brain injury (ABI). Traditional approaches and newer virtual reality interventions for physical therapy tend to focus on individuals interacting one-on-one with a therapist in a clinical space – this is both labor intensive and costly. Co-located tabletop environments have been shown to enhance the engagement of users, translating to skill acquisition. We describe the principles of group interaction that inform our understanding of motor rehabilitation using interactive media; explore four constructs from interactive tabletop research that may influence the design of co-located systems for rehabilitation: 1) physical space, 2) group awareness, 3) territoriality, and 4) interaction simultaneity; and consider how each construct can be expressed in particular design solutions for rehabilitation of ABI