which method commonly used evaluate remaining 'boundary' pixels after initial segmentation (based on thresholds)?
i thought classification based on standard deviation threshold values don't know if common practice in image analysis. region growing method based on answer on question ( http://www.mathworks.com/matlabcentral/answers/53351-how-can-i-segment-a-color-image-with-region-growing ) not sensible use region growing algorithm. suggested imdilate. method seems arbitrary, useful when enhancing images aesthetic purpose or enhance visibility. problem assigning of pixels has correct because have measurements on these extracted objects/features , few pixels make huge difference.
what looking :
- to collect boundary pixels of bw image first segmentation (which found : http://nl.mathworks.com/help/images/ref/bwboundaries.html)
- a decision rule (nearest neighbor ?) classify boundary pixels. helpful if there multiple methods this, because makes relative accuracy check of classification possible.
i appreciate input/advice more experience in area point me right direction (functions, tutorials etc…)
thank !
what work depends on images have. no one-size-fits-all algorithm.
first, need answer question: given pixel close segmented feature, make believe pixel belongs feature? also: "close"?
the answer second question determines search area. here, imdilate useful identify candidate pixels (i.e. dilate feature, subtract feature, , left ring of candidate pixels around each feature). if test on pixels, risk not take forever, images, region growing mechanism expands entire image.
the answer first question determines algorithm you'll use. gradient, i.e. "if pixel p closer in intensity adjacent feature of neighbors, take it"? texture? local threshold (hysteresis thresholding)? answer, again, depends on images segmenting. make sure test on large set of images, because may on 1 image may totally fail on different one.
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