This paper explores the utility of visual acuity as a video quality metric for public safety applications. An
experiment has been conducted to track the relationship between visual acuity and the ability to perform a
forced-choice object recognition task with digital video of varying quality. Visual acuity is measured according
to the smallest letters reliably recognized on a reduced LogMAR chart.
Public safety practitioners increasingly use video for object recognition tasks. These end users need guidance regarding
how to identify the level of video quality necessary for their application. The quality of video used in public safety
applications must be evaluated in terms of its usability for specific tasks performed by the end user.
The Public Safety Communication Research (PSCR) project performed a subjective test as one of the first in a series to
explore visual intelligibility in video-a user's ability to recognize an object in a video stream given various conditions.
The test sought to measure the effects on visual intelligibility of three scene parameters (target size, scene motion, scene
lighting), several compression rates, and two resolutions (VGA (640x480) and CIF (352x288)). Seven similarly sized
objects were used as targets in nine sets of near-identical source scenes, where each set was created using a different
combination of the parameters under study. Viewers were asked to identify the objects via multiple choice questions.
Objective measurements were performed on each of the scenes, and the ability of the measurement to predict visual
intelligibility was studied.
This paper describes the application of the expectation-maximization/maximization of the posterior marginals
(EM/MPM) algorithm to serial section images, which inherently represent three dimensional (3D) data. The images of
interest are electron micrographs of cross sections of a titanium alloy. To improve the accuracy of the resulting
segmentation images, the images are pre-filtered before being used as input to the EM/MPM algorithm. The output of
the pre-filter at a particular pixel represents an estimate of the entropy at that pixel, based on the grayscale values of
neighboring pixels. This filter tends to be biased towards higher entropy values if an edge is present within the window
being used. This causes edges in the final segmentation to move out from higher entropy regions and into lower entropy
regions. In order to preserve the locations of these edges, a multiscale technique involving the use of an adaptive filter
window has been developed. We present experimental results demonstrating the application of this technique.
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