Refereed full papers (journals, book chapters, international conferences)

2004

@article{Pag2004,
	vgclass =	{refpap},
	author =	{David W. Paglieroni},
	title =	{Design considerations for image segmentation quality
	assessment measures},
	journal =	{Pattern Recognition},
	volume =	{37},
	number =	{8},
	pages =	{1607--1617},
	month =	{August},
	year =	{2004},
	url =	{http://dx.doi.org/10.1016/j.patcog.2004.01.017},
	abstract =	{Factors to consider when designing quality assessment
	measures for image segmentation are discussed. Quality assessment
	requires one manually generated segmentation (for reference) plus
	computer-generated segmentations corresponding to different image
	segmentation algorithms or algorithm parameter settings. Since true
	pixel class assignments are seldom available, one must typically rely
	on a trained human analyst to produce a reference by using a mouse to
	draw boundaries of perceived regions on a digital image background.
	Different algorithms and parameter settings can be compared by ranking
	computed disparities between maps of computer-generated region
	boundaries and region boundaries from a common reference.

	Proximity-based association between two boundary pixels is discussed in
	the context of association distance. Motivated by the concept of
	phase-modulated signals, a penalty factor on the degree of association
	is then introduced as some non-negative power (phase modulation order)
	of the cosine of disparity in phase (boundary direction) between two
	boundary pixels. Families of matching measures between maps of region
	boundaries are defined as functions of associations between many pairs
	of boundary pixels.  The measures are characterized as one-way
	(reflecting relationships in one direction between region boundaries
	from two segmentations) vs. two-way (reflecting relationships in both
	directions). Measures of inconsistency between perceived and computed
	matches of computer and manually generated region boundaries are
	developed and exercised so that effects of association distance, phase
	modulation, and choice of matching measure on image segmentation
	quality assessment can be quantified. It is quantitatively established
	that consistency can be significantly improved by using two-way
	measures in conjunction with high-order phase modulation and moderate
	association distances.},
}