Performance characteristics of the masked target transform volume clutter metric

Abstract

Prior work has shown that the masked target transform volume (MTTV) clutter metric provides a measure of scene clutter that better correlates to measured probability of detection for human observers than several previously published clutter metrics. Several factors involved in using the MTTV to assess clutter in imagery are discussed here. A previously published modification to the MTTV metric to provide a normalized output value comparable across different image sets regardless of scene size is reviewed. Initial MTTV development required knowledge of a scene's target signature and produced an unbounded metric value. Metric behavior is discussed for the case in which an average of several target signatures is used in place of a specific target signature. This allows the MTTV to be calculated for images that do not contain a target. It is shown that the user may trade computational efficiency with metric accuracy to suit a particular application. The sensitivity of the metric to variations in image noise level, target segmentation error, and viewing distance are also presented.

Display Clutter

“…Furthermore, Camp et al (2013) outlined a number of criteria that can be used to identify reliable image-processing algorithms for clutter estimation. One of these desirable criteria is threshold or parameter independence from the researcher, as user-set parameters can lead to possibly unreliable measures of clutter.…”

“…Low local clutter can help mitigate the effects of high global clutter by quickly attracting attention to the search target, whereas high local clutter makes it more difficult to discriminate the target in a high-global-clutter display (Beck et al, 2010). Other reported effects of display clutter include distraction (Beck et al, 2012), uncertainty (Bravo & Farid, 2008;Lohrenz et al, 2006;Schons & Wickens, 1993), confusion (Alexander et al, 2008;Chu et al, 2012;Ewing et al, 2006;Kaber et al, 2008;Lohrenz et al, 2009), degraded object recognition and detection (Bravo & Farid, 2006;Camp, Moyer, & Moore, 2013), forward visual field interference (Yeh, Merlo, Wickens, & Brandenburg, 2003), degraded detection of unexpected events (e.g., Wickens & Long, 1995), and degraded information interpretation (Ewing et al, 2006;Peng et al, 2004;Yeh & Wickens, 2001). It is the study of attentional and performance costs such as these that ultimately turns clutter into a critical concern for human factors and ergonomics practitioners.…”