ccthin.c PIX *pixThin() PIX *pixThinGeneral() PIX *pixThinExamples()
PIX * pixThin ( PIX *pixs, l_int32 type, l_int32 connectivity, l_int32 maxiters )
pixThin() Input: pixs (1 bpp) type (L_THIN_FG, L_THIN_BG) connectivity (4 or 8) maxiters (max number of iters allowed; use 0 to iterate until completion) Return: pixd, or null on error Notes: (1) See "Connectivity-preserving morphological image transformations," Dan S. Bloomberg, in SPIE Visual Communications and Image Processing, Conference 1606, pp. 320-334, November 1991, Boston, MA. A web version is available at http://www.leptonica.com/papers/conn.pdf (2) We implement here two of the best iterative morphological thinning algorithms, for 4 c.c and 8 c.c. Each iteration uses a mixture of parallel operations (using several different 3x3 Sels) and serial operations. Specifically, each thinning iteration consists of four sequential thinnings from each of four directions. Each of these thinnings is a parallel composite operation, where the union of a set of HMTs are set subtracted from the input. For 4-cc thinning, we use 3 HMTs in parallel, and for 8-cc thinning we use 4 HMTs. (3) A "good" thinning algorithm is one that generates a skeleton that is near the medial axis and has neither pruned real branches nor left extra dendritic branches. (4) To thin the foreground, which is the usual situation, use type == L_THIN_FG. Thickening the foreground is equivalent to thinning the background (type == L_THIN_BG), where the opposite connectivity gets preserved. For example, to thicken the fg using 4-connectivity, we thin the bg using Sels that preserve 8-connectivity.
PIX * pixThinExamples ( PIX *pixs, l_int32 type, l_int32 index, l_int32 maxiters, const char *selfile )
pixThinExamples() Input: pixs (1 bpp) type (L_THIN_FG, L_THIN_BG) index (into specific examples; valid 1-9; see notes) maxiters (max number of iters allowed; use 0 to iterate until completion) selfile (<optional> filename for output sel display) Return: pixd, or null on error Notes: (1) See notes in pixThin(). The examples are taken from the paper referenced there. (2) Here we allow specific sets of HMTs to be used in parallel for thinning from each of four directions. One iteration consists of four such parallel thins. (3) The examples are indexed as follows: Thinning (e.g., run to completion): index = 1 sel_4_1, sel_4_5, sel_4_6 index = 2 sel_4_1, sel_4_7, sel_4_7_rot index = 3 sel_48_1, sel_48_1_rot, sel_48_2 index = 4 sel_8_2, sel_8_3, sel_48_2 index = 5 sel_8_1, sel_8_5, sel_8_6 index = 6 sel_8_2, sel_8_3, sel_8_8, sel_8_9 index = 7 sel_8_5, sel_8_6, sel_8_7, sel_8_7_rot Thickening: index = 8 sel_4_2, sel_4_3 (e.g,, do just a few iterations) index = 9 sel_8_4 (e.g., do just a few iterations)
PIX * pixThinGeneral ( PIX *pixs, l_int32 type, SELA *sela, l_int32 maxiters )
pixThinGeneral() Input: pixs (1 bpp) type (L_THIN_FG, L_THIN_BG) sela (of Sels for parallel composite HMTs) maxiters (max number of iters allowed; use 0 to iterate until completion) Return: pixd, or null on error Notes: (1) See notes in pixThin(). That function chooses among the best of the Sels for thinning. (2) This is a general function that takes a Sela of HMTs that are used in parallel for thinning from each of four directions. One iteration consists of four such parallel thins.
Zakariyya Mughal <firstname.lastname@example.org>
This software is copyright (c) 2014 by Zakariyya Mughal.
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