Video-rate Image Segmentation by means of Region Splitting and Merging
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1 Video-rte Imge Segmenttion y mens of Region Splitting nd Merging Knur Anej, Florence Lguzet, Lionel Lcssgne, Alin Merigot Institute for Fundmentl Electronics, University of Pris South Orsy, Frnce knur.nej@gmil.com, florence.lguzet@u-psud.fr lionel.lcssgne@u-psud.fr, lin.merigot@u-psud.fr Astrct This pper proposes fst method for imge segmenttion. After n optiml split of the imge into rectngulr regions, this pper focuses on the fst merging of these regions. Since the computtion time is very smll, hence it is suitle for rel time pplictions, while producing good segmenttion for trcking purposes. I. INTRODUCTION Imge segmenttion ims to group piel in n imge into regions, sed on their similrity in terms of grey level, color or teture. It is n importnt step in prcticl ppliction of imge nlysis s it is frequently preliminry pss for oject locliztion, recognition or trcking, etc. While mny sophisticted methods hve een proposed, most re presently unle to perform this segmenttion on the fly, s it is required in mny time-criticl pplictions like vehicle guidnce, nd so on. We present in the pper very fst segmenttion scheme sed on imge splitting nd merging. A clssicl Split nd Merge segmenttion method ws proposed in [1]. It splits the imge recursively in squres until ll the squres re homogenous enough. It hs some drwcks inspite of the fct tht it is very time effective. It is incple of dpting itself to the imge chrcteristics i.e. it produces lrge numer of regions for oundries other thn the horizontl nd verticl ones. Also these region oundries re highly dependent even on simple trnformtions like trnsltion nd scling. Hence we choose insted the Optiml Split method[2]. Here if given rectngle is not homogenous enough, we select n optiml split position, i.e. position tht will led to the most homogeneous suregions. The computtion of this position cn e very efficiently done with the help of the scns or prefi sum tht cn drmticlly ccelrte the computtion of glol sums over squre regions, in order to clculte, for instnce, the verge intensity nd vrince of these regions. A inry tree dt structure is used to mintin the list of these homogenous regions otined during the split phse. The min interest of this pproch is tht the splitting is dpted to the imge chrteristics which lrgely reduces the initil numer of regions without ny significnt chnge in the computtionl cost. This pper presents new method for merging the regions otined from the optiml split method. A region is merged with its neighours till it cn merge no further so s to hve the vrince of ll the regions elow given threshold. II. IMPLEMENTATION Our segmenttion method proceeds in the following mnner. Firstly, in the split phse, the initil split is performed in order to get the representtion of the imge s set of homogenous regions. Ech homogenous region is ssigned unique lel. Then region djcency grph (RAG) is constructed corresponding to this region set so tht ech region gets references to its neighours. In the merging phse, every region is merged with its surrounding regions to get lrger regions which re still homogenous enough. This RAG is updted until no more regions cn e merged further. Merge synoptic (Fig. 1) is the flow digrm representing the vrious steps of our method. Here the oundry nd rg imges hve een used to help in understnding the lgorithm mechnism. The net section presents the region merging lgorithm nd ll the ove mentioned spects will e descried in detil in the following sections. imge of lels Fig. 1. initil imge Split initil split Itertive Merge finl merge imge djcency tle Merge synoptic oundries imge rg imge oundries imge rg imge A. Algorithm B. Imge of lels During splitting, inry tree dt structure is used to mintin the list of the regions. So y scnning the inry tree, ll the regions cn e ccessed. But only the lef nodes of this tree will represent the homogenous regions otined upon the termintion of the splitting process. Ech lef node represents its corresponding homogenous region nd hence while trversing the tree, the lels re ssigned to these nodes nd new imge of lels is then formed where ech region is filled y its corresponding lel.
2 Algorithm 1: forech vlid region e k do select its est neighour f k if neighour f k is vlid then if ((gry level(e k ) = gry level(f k )) then Region is merged with its est neighour if the homogeneity criterion is followed Updte informtion for this new region in the following-: Imge of lels Adjcency tle (representing RAG so formed) Properties of the region fter merging (verge intensity, vrince,size, etc) Continue merging the new region so formed (updted e k ) until it cnnot merge ny further else select nother neighour fk for e k end else invlidte the region ek (remove it from the list) end end C. Adjcency tle This tle is sed on the imge of lels so s to mintin the list of neighours for ech region in the originl imge fter the split phse is over. Becuse of the optiml splitting process, the imge is divided into severl rectngulr regions. So while scnning the imge of lels, there re four possiilities which cn e encountered y the scnning element (Fig. 2 top right) verticl order horizontl order etween two djcent regions hving different lels corner formed y three djoining regions nd the cse where the element remins inside the sme region Fig. 2. #6 verticl order =<> #3 horizontl order (<>=) #7 #2 #1 inside (==) #4 #5 corner (<><>) #8 Automton nd the scnning element In order to ccelerte the uilding of the djcency tle, finite stte utomton (Fig. 2) hs een creted to represent ll the possile trnsitions. Hence, the lels re dded to ech other s list of neighours if nd only if, verticl order or corner is encountered y the scnning element (cses (5),(6),(7),(8) of Fig. 3). Becuse when the element moves inside (i.e. within) the sme region, it does not encounter trnsition. The trnsition in cse of the horizontl order is insignificnt ecuse it ultimtely leds to verticl order or corner. While dding the lel to the neighours list of prticulr region (i.e. represented y nother lel) it is lso ensured eforehnd tht tht lel is not present in the list eing considered. As the merging progresses, this djcency tle is modified to contin the comined list of neighours of the regions eing merged. #1: corner -> horizontl #2: horizontl -> horizontl #3: verticl -> inside #4: inside -> inside Fig. 3. #6: horizontl -> verticl #7: inside -> verticl #8: inside -> corner Trnsitions #5: horizontl -> corner D. Selection of the net region to merge It cn e done using severl methods: the region is selected rndomly from the list of ville vlid regions the region hving minimum vrince(i.e. the most homogenous region) is selected every time from the updted list of vlid regions the region with the lrgest size cn e selected to commence the merging process It ppers tht these methods led to similr results in terms of the qulity of the segmenttion. As simple rndom selection is much fster, this is wht will e used in this pper. E. Best neighour selection Every region in the imge otined fter splitting hs its own list of neighours (i.e connected regions) s contined in the djcency tle. If the difference of verge gry level intensities of the selected region nd its neighour is less thn the verge threshold (0.4 vrince threshold gives good results), then the neighour is considered to e eligile. This is done to check the similrity of the grey levels of the regions eing merged ecuse otherwise, y only considering vrince sed criteri, smll region could e sored y much lrger one, whtever its gry level my e. Then the comined glol vrince of the min region (under considertion) nd one of its eligile neighours is clculted. The neighour giving smllest vlue of vrince fter merging is selected s the est neighour from the list of eligile neighours of the min region. If region does not hve n eligile est neighour then it cnnot merge further, nd it is removed from the list of vlid regions. But it is possile tht lter this invlid region cn qulify s est neighour of some other newly formed vlid region. F. Merging rows When the region nd its corresponding est neighour re selected considering the ove mentioned criteri in mind, it must e ensured tht the vrince of the selected region is less thn the mimum vrince vlue. The threshold vlue of the vrince for the split nd merge phse is the sme. The regions re then merged nd their list of neighours is comined. The
3 new neighours list when merging region B to region A is the initil neighours list of A {B}+ the neighours of B tht re not neighours of A. Hence the list of neighours is updted for the new region( region R i.e. updted region A) so formed in the djcency tle. The size of the region R is the sum of the size of region A nd B. A common lel i.e. the lel of the min region eing merged (region A) is ssigned to region R. Also the verge grey level intensity of this region R is modified nd updted. The merging for region is done in this mnner till it cn merge no further. The region ecomes invlid when it cnnot merge ny more. Then nother region is selected nd is merged with its neighours in the similir wy. This merging process continues till no more vlid regions (tht cn merge further) eist. III. DIFFERENT APPROACHES A. Originl method Simple merging consists of selecting region, merging it with its est neighour, then selecting nother region, nd so on till no more vlid regions eist. Good results were chieved using this pproch ut the time tken to perform the merging ws much more thn epected. Thus, we eperimented severl vrints of this seline method nd compred them with respect to their computtion time nd qulity of the segmenttion (numer of regions, finl vrince, etc). B. One region t time In this method, one region is selected for merging nd it is merged until it cnnot merge ny further. Then nother region is selected nd so on. This method gives lmost the sme segmenttion qulity ut it with much etter eecution time (tht is improved up to 30 times). C. Other considered modifictions To increse the ccurcy further,the modifiction mde during est neighour selection ws to check tht if the min region (under considertion) is lso the est neighour of its selected est neighour. So the merging ws performed only if oth the regions eing merged were est neighours of ech other respectively. But this leds to highly selective merging nd very few regions cn e merged effectively. For instnce, on the cmermn imge with 1730 initil regions, the numer of regions fter merging were 13 (insted of 531 regions otherwise). An epensive spect in the merging, is the updting of the neighour list. To ese neighour serch nd selection, this list needs to sorted. Then we cn use merge sort when performing the ctul merging. An initilly considered solution ws to initilly sort ll the neighor lists. The ssocited eecution time ws importnt, nd we chose insted to perform the sorting on demnd, only for the cse where the list needs to e merged. This leds to drmtic reduction in the eecution time (54 ms vs 1849 ms for the merge time for the cmermn imge) A trick which hs een used to decrese the computtionl merging time further is to modify the rndom selection of the min region in order to improve the eecution time. In our method, the list of regions is written in the djcency tle nd then rndom selection is performed y picking ny region from this tle. This tle cn e updted t ny time during the merging process when region is removed. But the prolem is tht it will require mny useless copies nd moves in the tle. So insted we choose lzy scheme, where the tle is not modified ut new prllel list of ll the regions is mintined in the eginning. Ech time region is picked rndomly from this list, its vlidity is checked. If it is vlid, it gets selected. But if it is invlid, then it is swpped with the lst element of this list nd the size of the list is decresed y one. In this wy, this new list gets modified ech time n invlid region is picked from the list so s to decrese the numer of invlid regions in it nd grdully increse the proility of picking out vlid region eing picked out during rndom selection. This optimiztion mkes the selection process fster. The sme mount of merging is done in 36 ms insted of 54 ms for cmermn imge. D. Test imge segmenttion results Three clssicl imges hve een used for enchmrking: cmermn, imge nd peppers. The computer used is PowerPC G5 running t 2.5 GHz using gcc 4.0. Figure 4 shows some segmenttion results for severl imges. We disply the originl imge, the segmented imge (otined y replcing piel vlue in every region y the region verge gry level) nd the oundry imge fter merge for different thresholds of vrince. The visully good results were otined for vrince threshold of 30 for the given emples. The ssocited numers represent the numer of regions, efore nd fter merging. We cn see tht, despite some rtifcts tht re inherent to the method, mostly when deling with regions with olique frontiers, the overll segmenttion is quite correct nd cn produce in rel time good strting point for locliztion or recognition tsk. A. Vrince vs totl Time IV. GRAPHS Figure 5 presents the totl computtion time for the enchmrked imges for vrince threshold rnge of (-70). We cn remrk tht the computtion time lies in the rnge 45 ms leding to possile implementtion in rel video frmerte. B. Detiled nlysis of the eecution time Figure 7 presents the detiled contriutions of the different psses of the lgorithm for the imge imge for different vrinces. We cn notice tht the split time is in generl negligile (round 5 ms), nd the most importnt contriution is the initil djcency tle construction, nd the ctul merging step. This lter time increses with the vrince, s the the deeper merging performed supersedes the smller numer of regions produces t the initil imge slitting. C. Vrince vs Numer of regions For ll imges (Fig. 6) the numer of regions decrese with increse the vlue of vrince threshold ecuse greter the vlue of the threshold, more the numer of regions will e merged.
4 () Cmermn () After optiml split(vr -30) (c) Boundry imge fter merge, vr- 15,n(2233,1528) (d) Boundry imge fter merge, vr- 30,n(1730,643) (e) Boundry imge fter merge, vr- 50,n(1441,230) (f) Finl segmented imge(vr 30) (g) Peppers (h) Boundry imge fter merge, vr-,n(39,571) (i) Boundry imge fter merge, vr- 65,n(1741,168) (j) Imge (k) Boundry imge fter merge, vr- 35,n(2631,775) (l) Boundry imge fter merge, vr-,n(78,380) Fig. 4. Emples of segmenttion results
5 D. Tle V. COMPARISON WITH HOROWITZ PAVLIDIS ALGORITHMS For vrince threshold vrying etween to 70, the verge time nd stndrd devition is computed for ech imge in this tle. cmermn imge peppers Averge time (ms) Stndrd devition time time cmermn imge vrince Fig. 9. computtion time for HP2 lgorithm 25 0 time cmermn imge 15 vrince cmermn imge Fig computtion time for the enchmrked imges regions cmermn imge n regions vrince Fig. 10. computtion time for HP4 lgorithm vrince Fig numer of regions for the enchmrked imges imge!time totl merge split djcency lels 0 vrince Fig. 7. computtion time for imge emple As we cn see (Fig. 9), HP2 is the only rel-time lgorithm (< ms). HP4 provides etter split (Fig. 8) ut is twice slower (Fig. 10) thn HP2. finlly the Optiml Split & Merge is s fst s HP2, ut with etter split & merge stges. VI. CONCLUSION This pper hs presented rel-time merging phse of rel-time segmenttion scheme suitle for time criticl pplictions. As it relies on n initil optiml split of the imge, the qulity of segmenttion is much higher tht most others methods sed on clssicl split nd merge, nd it cn e used for fst pre-segmenttion purposes for locliztion or trcking. The qulity of the segmenttion cn e further improved y postprocessing steps (for instnce the segmenttion done using successive steps[3]). Presently considered etensions re lso to etend the method to color imges. REFERENCES [1] S. Horowitz nd T. Pvlidis, Picture segmenttion y tree trversl lgorithm, Journl of the ACM, 23:368388, [2] Alin Merigot, Revisiting imge splitting, 12th IEEE Interntionl Conference on Imge Anlysis nd Processing (ICIAP 03), Septemer 03, Mntov, Itly, pp [3] Andre Gglowicz et Olivier Mong, A new pproch for imge segmenttion. In Proceedings, Eighth Interntionl Conference on Pttern Recognition, IEEE Pul. 86CH2342-4, pp , Pris, Frnce,1986.
6 () Cmermn HP2 () Cmermn HP2 oundries (c) Cmermn HP4 (d) Cmermn HP4 oundries (e) HP2 (f) peper HP2 oundries (g) peper HP4 (h) peper HP4 oundries (i) imge HP2 (j) imge HP2 oundries (k) imge HP4 (l) imge HP4 oundries Fig. 8. Emples of Horowitz Pvlidis (HP2 & HP4) results
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