TARGET RECOGNITION ALGORITHM BASED ON SALIENT CONTOUR FEATURE SEGMENTS

Transcription

1 U.P.B. Sc. Bull., Seres C, Vol. 81, Iss. 1, 2019 ISSN TARGET RECOGNITION ALGORITHM BASED ON SALIENT CONTOUR FEATURE SEGMENTS Janhu SONG 1, Yungong LI 2, Yanju LIU 3 *, Yang YU 4, Zhe YIN 5 A target recognton algorthm based on salent contour feature segments s presented. The algorthm segments the contour accordng to the contour dvson scheme and evaluates the value of the contour segment from three aspects: curvature dfference, fluctuaton ampltude, and bendng rato. The segments of unreasonable dvson are merged. The mportance of the segment s evaluated from the aspect of the length of the contour segment relatve to the overall contour length. On ths bass, the reasonable segmentaton result of the contour segments s obtaned. The smlarty measures are performed between the reasonably dvded contour segment and the contour segment to obtan the best matchng result of the target. Expermental results show that the proposed algorthm determnes the parameters of sgnfcance evaluaton of contour segmentaton, whch ensures that the segmented contour segment has sgnfcant features and mproves the target recognton rate. Keywords: Contour dvson, Contour feature segments, Sgnfcant features, Object Recognton 1. Introducton Contour feature s a knd of advanced vsual nformaton. Even when an mage object loses ts color or texture features, the object s category can stll be dentfed accordng to the target contour. The contour segment descrpton method s one of the most wdely used target recognton methods n natural mage target recognton n recent years [1-3]. Ma and Lateck et al. [4] proposed a shape descrptor, whch s partcularly sutable for the shape matchng of mage edge fragments to smulate the outlne of the target object. However, ths method s greatly affected by nose. Flp Krolupper et al. [5] acheved the purpose of dvdng the contour by performng polygon approxmaton of the contour through the nflecton pont on the contour. El Saber et al. [6] selected ponts wth local maxmum curvature along contours as feature ponts and computed dstance matrces for each canddate object 1 Shenyang Lgong Unversty, Chna 2 Shenyang Lgong Unversty, Chna 3 * Shenyang Lgong Unversty, Chna, contact author, e-mal: @qq.com 4 Shenyang Lgong Unversty, Chna 5 Shenyang Lgong Unversty, Chna

2 26 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn regon and example template. Xang Ba et al. [7] dvded the contour nto several parts by usng the DCE (dscrete curve evoluton) algorthm on the complete contour of a known object. Chunjng Xu et al. [8] proposed the contour flexblty based on the topologcal relatonshp of contour shape and convexty. Ths method can accurately obtan the global and local features of the target contour. The target has strong robustness but hgh computatonal complexty. Sh Sq et al. [9] proposed to select the concave ponts wth the largest absolute curvature n contnuous non-characterstc ponts to segment the contour. The segmentaton of nonsense contour s processed. And the localty of the contour segment s evaluated by the rato of feature ponts and non-feature ponts n the contour segment. Huang Weguo et al. [10] proposed to segment the contour accordng to the corner pont. The feature pont and the non-characterstc pont are judged usng parameters whch are dfferent from the ones proposed by Sh et al. [9] to evaluate the value of the contour fragment. Amng at the problem of unreasonable contour segmentaton or lack of evaluaton of contour segments, ths paper proposes a target recognton algorthm based on salent contour feature segments and determnes the sgnfcance evaluaton parameters of contour segment. 2. Target recognton algorthm based on the salent contour feature segments The target recognton algorthm based on salent contour feature segments proposed n ths paper frstly obtans the target contour, calculates the curvature and segments the target contour. Secondly, the evaluaton value of the dvded contour fragments s carred out. The fragments whch don t satsfy wth the value are merged accordng to the segmentaton rules. The mportance of the contour fragment s calculated, and the contour fragment wth sgnfcant features s selected accordng to the mportance of the outlne segment. Fnally, the smlarty measure s performed on the contour segment to obtan the target matchng result. Compared wth the exstng technology, the algorthm proposed n ths paper determnes the evaluaton parameters of the contour segmentaton and evaluates the contour segment by value and mportance to ensure that the contour segment has sgnfcant features. The overall flow chart of the algorthm s shown n Fg. 1.

3 Target recognton algorthm based on salent contour feature segments 27 Contour acquston Curvature calculaton Contour dvson Value evaluaton Contour mergng No Meet the value? Yes Importance evaluaton Select the contour fragment wth sgnfcant features 2.1 Contour acquston Recognton results Smlarty measure Fg. 1. Target recognton flow chart Accordng to the vsual psychology, when the human vson observes an mage, t frstly perceves a sgnfcant area of the mage, that s, a promnent edge part, and then the detals n the mage. Therefore, t can be seen that the edge feature of the mage has more mportant sgnfcance for the target recognton. Contour acquston s an mportant step n the contourbased target detecton method. The qualty of the contour detecton method drectly affects the target detecton result. The Canny edge detecton method s one of the most used methods at present. The Gaussan flterng part n the Canny operator has a poor suppresson effect on the mpact nose, so that part of the mpact nose s easly detected as an edge. Medan flterng can suppress the random nose

4 28 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn wthout blurrng the edge. Therefore, the medan flterng s selected to smooth the mpact nose, and then the Canny operator edge detecton s performed on the smoothed mage to extract the contour. The contour of the smoothed motorcycle graph extracted by the Canny operator s shown n Fg. 2. It can be seen from Fg. 2 that the contour extracted by the Canny operator has many small segments that we do not care about or have no nfluence on the recognton. In order to elmnate the small segments n the contour, the contours extracted by the Canny operator are processed by expanson, eroson, regon fllng, openng operaton and closng operaton to obtan the target shape map. The target shape map s shown n Fg.3. Fnally, the edge pxels are connected nto boundary edge segments. The fnal motorcycle profle s shown n Fg. 4. Fg. 2. Motorcycle contour extracted by Canny operator Fg.3. The target shape map Fg. 4. Fnal motorcycle contour 2.2 Curvature calculaton of the contour When the overall contour of the target s extracted, how to dvde the contour nto peces wth obvous local features becomes a key pont. Dfferent contour segments have dfferent effects on the accuracy of target recognton. A reasonable contour segmentaton can descrbe the characterstcs of the target effcently and completely. When the segment s too short, the characterstcs are not obvous, and the msmatch rate s ncreased. When the

5 Target recognton algorthm based on salent contour feature segments 29 contour segment s too long, t s not conducve to descrbng smlar objects. Therefore, the segmented contour segments must not only be able to descrbe the object tself, but also be able to dstngush t from other objects. In ths paper, the curvature of the target contour s calculated, and the contour s dvded accordng to the curvature calculaton result. The curvature of a curve s defned as the rotaton rate of the tangent drecton angle to the arc length at a pont on the curve, whch s defned by dfferental, ndcatng the degree of devaton of the curve from the straght lne. The curvature has the nvarance of rotaton, scalng and translaton. The contour curve s set to be C ( t) = ( x( t), y( t)), the curvature of the contour curve can be expressed as formula (1): x ( t) y ( t) x ( t) y ( t) k = (1) 2 2 3/ 2 (( x ( t)) + ( y ( t)) ) In formula (1), x (t) and y (t) represent the frst dervatve of xt () and yt () to the arc length; x () t and y () t represent the second dervatve of xt () and yt () to the arc length. The curvature of the curve reflects the degree of bendng of the curve, and the degree of angle change n the arc length between the two adjacent tangent vectors on the curve. The calculaton results of the contour curvature of the motorcycle are shown n Fg. 5. It can be seen from Fg. 5, the curvature value of the contour has a sharp change regon, where the correspondng target contour segment has a sgnfcant feature, whle the regon wth a smaller curvature change corresponds to the meanngless contour segment. Curvature values Contour pont sequence Fg. 5. Motorcycle contour curvature

6 30 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn 2.3 Contour dvson After obtanng the curvature calculaton results of the target contour, the part wth the steepest contour change or the largest curvature s the place where the nformaton s most concentrated, and the place where the contour drecton s consstent s the place where the nformaton s most redundant. Therefore, the contour dvson scheme based on the curvature data s shown n Fg. 6. Begn Contour dvson ntalzaton n_start=n, the start pont of the contour segment; lengh=0, the length of the contour segment; n=n+1, pont to the next pont; length=length+1; k(n)<0 & k(n-1)>k(n) & k(n+1)>k(n) No Yes k(n)<c u No Yes length>p t No Yes n_end=n*, end pont of the segment n=n_end+1, pont to the next pont Yes n>=the number of samplng ponts No End Fg.6. The contour dvson scheme based on the curvature data

7 Target recognton algorthm based on salent contour feature segments 31 The leftmost pont of the contour s selected as the startng pont of the current segment, and the contour pont adjacent to t s merged n a clockwse drecton. When the segmentaton cutoff condtons are met, the next pont s selected as the startng pont for the new segment. The above processes are repeated untl the end of the contour curve. In Fg.6, k (n) represents the curvature of pont n ; Pt s the threshold for the length of the segment; Cu s the threshold for the curvature of the concave pont. The segmentaton cutoff condtons are as below: (1) The length of the segment s greater than Pt ; (2) There s at least one local extreme pont of concave pont n the segment; (3) The curvature of the concave pont must be less than Cu. The above three condtons must be met at the same tme. The condton for Pt ensures that the segment s not too short. The condton for Cu ensures that the segment has more sgnfcant features. The motorcycle profle segmentaton s shown n Fg.7, where Cu = 1 and Pt = 450. Dfferent lne types represent the results of each fragment of the profle. Fg. 7. The motorcycle profle segmentaton 2.4 Evaluaton and mergng of contour fragments After obtanng the result of contour segmentaton, the value of contour segments must be evaluated. In ths paper, the curvature dfference, fluctuaton ampltude and bendng rato of contour fragments are presented to evaluate the value of segment. (1) Curvature dfference. The more promnent parts of the contour segment the greater the contrbuton to the target recognton. The maxmum curvature max( cur) and the mnmum curvature mn( cur ) on the contour segment are calculated. The curvature dfference s calculated as formula (2): Curvature dfference= max( cur) - mn( cur ) (2) The greater the curvature dfference s, the greater the fluctuaton of

8 32 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn the curvature of the segment wll be, the more promnent parts of the contour segment may be, and the more obvous the characterstcs wll be. (2) Fluctuaton ampltude. The fluctuaton ampltude of the contour segment s the dstance from each pont on the contour segment to the straght lne SE, as shown n Fg. 8. S d Fg. 8. Fluctuaton ampltude of contour segment The start pont of the contour segment s set to be S( xs, y s) and the end pont to be E( xe, y e). The dstance formula from pont ( x, y) to straght lne SE on the contour segment s deduced as below: The slope of straght-lne SE s: ye ys k = (3) xe xs The formula of straght-lne SE s: y y ( ) s = k x xs (4) The dstance formula from pont ( x, y) to straght lne SE s calculated as formula (5): kx y + ( ys kxs ) d = (5) k When the pont on the segment s on the upper sde of straght lne SE, d s postve; when the pont on the segment s on the lower sde of straght lne SE, d s negatve. The maxmum fluctuaton ampltude of the contour segment s max( d )-mn( d ). The larger the fluctuaton ampltude of the contour segment, the more obvous the change of the contour segment, and the more representatve for the contour. (3) Bendng rato. The total number of ponts of the contour segment s calculated as L. The length l( SE ) of the lne SE s calculated as formula (6). E l 2 2 ( SE) = ( xe xs) + ( ye ys) (6) The bendng rato s calculated as formula (7). l ( SE) = (7) L

9 Target recognton algorthm based on salent contour feature segments 33 The smaller the bendng rato s, the greater the degree of curvature of the contour segment s, and the more obvous the feature s. When the bendng rato s close to 1, the contour segment s close to a straght lne, and the contour segment feature s not obvous. The value of the contour segment s evaluated by the evaluaton crtera: (1) the curvature dfference s greater than the threshold value Tc ; (2) the fluctuaton ampltude s greater than the threshold value Tf ; (3) the bendng rato s greater than the threshold value Tr. The above three prncples must be met at the same tme. In order to obtan the contour segments that can effectvely descrbe the target characterstcs, the contour segments that do not meet the evaluaton crtera are processed as below: (1) For the target contour, the contour secton obtaned by ntal contour dvson s represented as S = { s, = 1, 2, 3 }.The mnmum number of contour segments s set to be N. (2) If the number of contour segments s less than or equal to N, the merge process s ended. Otherwse, the next step s carred on. (3) The frst segment S by clockwse. that does not meet the crtera to merge s found out L R (4) The contour segments around S are represented as S and S, respectvely. The curvature dfference, fluctuaton ampltude and bendng rato between S and L S are compared. Then, obvous features of the two. If S s merged to marked as marked as L new R S = S S. If S s merged to S = S S. R new S s merged wth the more L S, the new contour segment s R S, the new contour segment s (5) The new contour segment S new s added to the contour segment set S whle the contour segment S s removed from the set S. (6) The curvature dfference, fluctuatng ampltude, and bendng rato of the new contour segment S are calculated. If the evaluaton crtera are new satsfed, the new segment S new can be used as a feature segment. Otherwse, the second step s carred on. In the contour segment mergng process, the low-value contour

10 34 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn segments are selected to be merged to obtan a new contour segment, whch ensures that the contour segment has obvous local features and can make the orgnal feature change more obvous. The segmentaton and merge results of a horse contour are shown n Fg. 9. The contour of the horse s ntally dvded nto 5 segments and merged to 4 segments. (a) Intal contour dvson results. (b) Contour segment merge results Fg. 9. The segmentaton and merge results of a horse contour After the contour segments are merged, how to measure the mportance of contour segments becomes a key ssue. When the contour segment s longer, the contour segment has more sgnfcant features, whch s more conducve to the target's recognton. Therefore, the mportance of the contour fragment s calculated usng the rato of the relatve length of the contour segment to the overall length of the contour. The contour fragment wth sgnfcant features s selected accordng to the mportance of the contour segment. Fnally, the smlarty measure s performed on the contour segment to obtan the target matchng result. 2.5 Smlarty measure The shape context feature s a very popular shape descrptor that s mostly used for shape matchng and target recognton [11,12]. The shape context s descrbed based on the object contour sample ponts. By performng edge extracton and unform samplng on the target contour, a set of ponts n the shape of an object s obtaned as P = { p, = 1,2, n}. The shape nformaton of a sngle pont s descrbed by the relatve vector formed wth all other ponts. In a log polar coordnate system, a sngle pont and ts neghborng ponts fall nto dfferent bns, whch represent dfferent relatve vectors. These relatve vectors become the shape context of ths pont. The matchng cost of a certan pont p of contour segment P and a pont q of contour segment Q s shown as formula (8): j

11 Target recognton algorthm based on salent contour feature segments 35 C j K [ h ( k) hj ( k)] = C( p, q j ) = (8) h ( k) + h ( k) Where k = {1, 2,..., K} ; K s the number of bns; h( k) and h ( k) are the shape hstogram values correspondng to p and q j, respectvely. After obtanng the matchng cost of each pont, a cost matrx can be formed. Then an optmal matchng algorthm (Hungaran algorthm) [13] s run to fnd the optmal match. The result s a permutaton () such that the sum C( p, q () ) s mnmzed. Fnally, the overall shape cost s obtaned based on ths optmal matchng. The mnmzed shape cost can be measured as the dfference between two shapes. The smaller the cost, the more smlar the shape. The shape cost s shown as formula (9): k = H ( ) = C( p, q () ) (9) Where s a permutaton. The contour shape dstance can be expressed by formula (10): 1 1 Dsc ( P, Q) = arg mn C( p, T( q)) + arg mn C( p, T( q)) n q Q m p P (10) p P Where DSC ( P, Q ) s the contour segment dstance of the contour segment P and Q ; n s the number of ponts of the contour segment P ; m s the number of ponts of the contour segment Q ; arg mn C( p, T( q)) ndcates the value of p and T( q ) at the moment when q Q and C( p, T ( q )) get the mnmum value; arg mn C( p, T( q)) ndcates the value of p and T( q ) at the p P moment when p P and C( p, T ( q )) get the mnmum value; T () denotes the estmated TPS (thn plate splne) shape transformaton[14]. The dfference between the two object shapes can be bascally measured based on the contour shape dstance, and further work on object recognton can be performed. 3. Expermental results and analyss In order to test the accuracy of the target recognton algorthm based on salent contour feature segments proposed by ths paper, many experments were carred out, the matchng results were gven, and the expermental data were analyzed. Frstly, fve types of targets n the MPEG-7 Shape-1 Part-B were selected as the test targets. The outer contours were extracted q Q j q Q j

12 36 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn from the 5 types of targets. 10 types of each class are selected to set up a segmented accordng to the contour dvson scheme proposed n ths paper. The rest s used as the test object to establsh the segmented of the test object contour. In the smulaton experment, the salent contour feature segment of bats, horses, elephants, cars, and dogs were selected and tested. The parameters used n the smulaton were Tc =2, Tf =120, Tr [0.6,0.7], N =2. The bat and horse test contour segments are shown n Fg. 10. (a) bat (b) horse Fg. 10. Test contour segments The frst 10 groups of contour shape dstance calculated from the smlarty of the fragments n the bat, horse, elephant, dog, and car sample lbrares are shown n Table 1 and Table 2 respectvely. If the shape dstance between fragments s less than 0.2, the sample lbrary segment correspondng to the mnmum dstance s selected as the successful result. If the dstance between fragments s not less than 0.2, the matchng fals. As can be seen from Table 1 and Table 2, ths method successfully acheves the correct target match Table 1. The contour shape dstance of bat test segment bat Bat Horse Elephant Dag Car Bat Horse The contour shape dstance of horse test segment horse Table

13 Target recognton algorthm based on salent contour feature segments 37 Elephant Dag Car The matchng result of the contour fragment s shown by the thck lne n Fg. 11. (a) bat (b) horse Fg. 11. The matchng result of the contour fragment The test contour segments of the elephant, car, and dog are shown n (a), (c) and (e) on the left sde of Fg. 12 and the matchng results are shown n (b), (d) and (f) by thck sold lne on the rght sde of Fg. 12. (a) elephant contour segment (b) elephant matchng result (c) car contour segment (d) car matchng result (e) dog contour segment (f) dog matchng result Fg. 12. The contour segment test result of elephant, car and dog The comparson results of the target retreval rate of ths algorthm n the and other representatve methods n recent years are shown n Table 3.

14 38 Janhu Song, Yungong L, Yanju Lu, Yang Yu, Zhe Yn Comparson of recognton rate of each method n MPEG-7 method Target retreval rate Vsual part [15] 76.45% Skeletal context [16] 79.92% SSC [17] 82.39% Polygonal Multresoluton [18] 84.33% Inner Dstance Shape context [19] 85.4% Shape tree [20] 87.7% Paper [10] 91.05% Method of ths artcle 92.5% Table 3. It can be seen from the expermental data that the contour segmentaton crteron and the fragment value scale evaluaton crteron proposed n ths paper ensure that the contour has obvous characterstcs, whch s favorable for object recognton and has a sgnfcant effect on mprovng the target recognton rate. 4. Conclusons Ths paper proposes a target recognton algorthm based on salent contour feature segments. Frstly, the curvature of the acqured target contour s calculated, and the target contour s segmented by the contour segmentaton algorthm. The curvature dfference, fluctuaton ampltude, and bendng rato are used to evaluate the value of the contour segment, and the contour segments that do not meet the condtons are merged. Then, the mportance of the segment s evaluated from the length of the contour segment relatve to the overall contour length to obtan the segmentaton result of the contour segment. Fnally, the contour segment matchng s performed usng the shape context smlarty between contour segments. Compared wth the exstng technology, ths algorthm determnes the evaluaton parameters of the contour segmentaton. Through the value and mportance of the overall evaluaton of the contour segment, t ensures that the contour segment has sgnfcant features and s effectvely appled to the contour segment target. Expermental results show that the algorthm mproves the target recognton rate. Acknowledgement The study s funded by the basc research project of hgher educaton n

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