Doron Tal

This paper presents a database containing 'ground truth' segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties.

The leaky integrate-and-fire (LIF) model of neuronal spiking (Stein 1967) provides an analytically tractable formalism of neuronal firing rate in terms of a neuron's membrane time constant, threshold, and refractory period. LIF neurons have mainly been used to model physiologically realistic spike trains, but little application of the LIF model appears to have been made in explicitly computational contexts. In this article, we show that the transfer function of a LIF neuron provides, over a wide-parameter range, a compressive nonlinearity sufficiently close to that of the logarithm so that LIF neurons can be used to multiply neural signals by mere addition of their outputs yielding the logarithm of the product. A simulation of the LIF multiplier shows that under a wide choice of parameters, a LIF neuron can log-multiply its inputs to within a 5% relative error.

Walking in environments with stairs and curbs is potentially dangerous for people with low vision. We sought to understand what challenges low vision people face and what strategies and tools they use when navigating such surface level changes. Using contextual inquiry, we interviewed and observed 14 low vision participants as they completed navigation tasks in two buildings and through two city blocks. The tasks involved walking in- and outdoors, across four staircases and two city blocks. We found that surface level changes were a source of uncertainty and even fear for all participants. Besides the white cane that many participants did not want to use, participants did not use technology in the study. Participants mostly used their vision, which was exhausting and sometimes deceptive. Our findings highlight the need for systems that support surface level changes and other depth-perception tasks; they should consider low vision people's distinct experiences from blind people, their sensitivity to different lighting conditions, and leverage visual enhancements.

This paper presents a database containing ground truth segmentations produced by humans for images of a wide variety of natural scenes. We define an error measure which quantifies the consistency between segmentations of differing granularities and find that different human segmentations of the same image are highly consistent. Use of this dataset is demonstrated in two applications: (1) evaluating the performance of segmentation algorithms and (2) measuring probability distributions associated with Gestalt grouping factors as well as statistics of image region properties.