A Method of Line Matching Based on Feature Points

Transcription

1 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY A Method of Lne Matchng Based on Feature Ponts Yanxa Wang and Yan Ma College of Comuter and Informaton Scence, Chongqng Normal Unversty, Chongqng, , Chna Emal: cloudwyx@163.com, mayan@cqnu.edu.cn Qxn Chen Deartment educaton admnstraton, Guzhou Unversty of Fnance and Economcs, Guyang, , Chna Emal: cqx_szy1@163.com Abstract--Ths aer rooses a method for lne matchng based on nvarance of feature onts. Frstly, feature onts are roughly matched by Normalzed Cross Correlaton (NNC) and Average of Square Dfference (ASD). Addtonally, feature onts obtaned from the two vews are groued nto matched ont ars. Fnally, curve segments between matched ont ars are matched by dynamc rogrammng algorthm wth edge otental functons (EPF) taken as the measure. The roosed method makes full use of feature onts, the relatonsh between feature onts and the curve, and sace nformaton of the gray mage. Index Terms--Feature Pont; Dynamc Programmng; Edge Potental Functons; Curve Potental Functon; lne matchng. I. INTRODUCTION Feature matchng lays an mortant role n many alcatons, such as mage regstraton, 3D reconstructon, obect recognton and vdeo understandng. In feature matchng, ont matchng has receved much attenton and varous aroaches have been roosed [1, 2, 3, 4]. Lne matchng s another challengng task because of the defcences n extractng lnes and the naccuracy of lne endont locatons. And we cannot drectly exlot the eolar geometry as a global geometrc constrant. In the case of onts (corners), corresondences must satsfy the eolar constrant. However, n the ast years there are varous aroaches roosed. The exstng aroaches to lne matchng can be groued by two tyes [5]: matchng ndvdual lne segments; and matchng grous of lne segments. Indvdual lne segments are generally matched on ther geometrc attrbutes such as orentaton, length, grayscale, extent of overla. Schmd and Zsserman [6, 7] take the eolar constrant of lne endonts for short baselne matchng, and one arameter famly of lane homograhes for wde baselne matchng. Groung matchng strategy [8] are avalable for removng ambgutes because of more geometrc nformaton, and t s able to coe wth more sgnfcant camera moton. However, t often has hgh comutatonal comlexty and s senstve to lne toologcal connectons or naccuracy of endonts. Herbert and Vttoro [9] exlot the comlementary nformaton of lne segments and regons to deal wth a broader range of scenes for man-made envronments wth homogeneous surfaces. Wang [10] rooses mean-standard devaton lne descrtor (MSLD for short) for automatc lne matchng wthout any ror knowledge. MSLD has two maor aealng characterstcs: t s urely mage content-based and can work wthout any other ossble constrants; t s alcable to general scenes for only constructng descrtor on a sngle scale and not scale-nvarant. Wang and Lu [11] roose the lne descrtor (HLD), whch can be used for automatc wde-baselne stereo matchng. Woo and Park [12, 13] resent a lne matchng method for the reconstructon of 3D lne segment based on geometrc and ntensty nformaton, and a stereo matchng method of 2D lne segments for the detecton of 3D lne segment. Pascal Vasseur [14] resents a method for catadotrc lne matchng across multle mages. A lne matchng algorthm [15] s resented based on the lne ntersecton context feature, and t can esecally work n oorly textured ndoor scenes. The class of scenes addressed n ths aer s tycally general scene lke flowers. Deste of occluson, these scenes often contan onts whch can be used as addtonal features. Ponts convey locatons of occlude, whereas curve segments convey amount of mortant geometrcal and toologcal nformaton about the consttuton of the scene. Usng both tyes of features allows exlotng ther comlementary nformaton for lne matchng. The aroach resented n ths aer matches feature onts roughly, and then matches lnes based on matchng feature onts. Our aroach exlots not only the ersectve nvarance of feature onts but also the aearance smlarty of ars of curves, the toologcal relatons among all curve segments, and gray sace. The remander of ths aer s organzed as followng. Secton 2 ntroduces the defnton of Edge Potental Functons (EPF for short) and fundamental concets. Secton 3 descrbes our aroach based on feature onts to matchng curves exlotng dynamc rogrammng. The aer concludes wth a dscusson of the lmtatons and future work n Secton 4. do: /sw

2 1540 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY 2012 II. FUNDAMENTAL CONCEPTS In ths aer, matchng lnes s comosed of two man stes: onts matchng and lnes matchng. So we frstly ntroduce the concets of ont matchng and lnes matchng n ths secton. A. Concets about Matchng Feature Ponts 1) Normalzed Cross Correlaton For two feature onts x 1 and x 2 to be matched, Normalzed Cross Correlaton calculates the brghtness mean and the varance of two sldng wndows whose central onts are x 1 and x 2 resectvely (2m+1, 2n+1 are the length and wdth of the two wndows). The NCC s defned as followng [16]: NCC( x, x ) = 1 2 [ I ( x + k, y + l) I ( x, y )] [ I ( x + k, y + l) I ( x, y )] (1) k= m l= n (2m + 1) (2n + 1) σ ( x1, y1 ) σ( x2, y2 ) 1 I( x, y) = I( x + k, y + l) (2m + 1)(2n + 1) k = m l = n 2 I ( x + k, y + l) ( x, y) k = m l= n 2 I ( x, y) σ = (2m + 1)(2n + 1) Where I( x, y ) and σ ( x, y) denote the brghtness mean and varance of all xels n sldng wndow centered feature ont ( x, y ) resectvely. Formula (1) ndcates that the range of NCC s[ 1,1]. The larger value ndcates that the feature of two sldng wndows s more smlar, and vce versa. NCC s very effectve measure for matchng feature onts, and t s nvarance for the lnear change of mage brghtness and has a very good dstncton for the wndows of dfferent areas. 2) Average of Square Dfference (ASD for short) (2) Average of Square Dfference s the most drect way for the smlarty measure between the two neghborng wndows, whch uses mean root square of absolute dfference of corresondng xels brghtness value between the two sldng wndows as the metrc. The smaller value ndcates that the feature of two sldng wndows s more smlar; on the contrary, the larger value ndcates that the smlarty of two sldng wndows s smaller. For the same confguraton wth the revous two sldng wndow, ASD s defned as the formula (3) 1 ASD( x, y ) [ I ( x k, y l) I ( x k, y l)] = (3) (2m + 1) (2n + 1) k = m l = n B. Concets about Matchng Lnes The dea of alyng the electrcal otental to model dfferent hyscal domans has been aled successfully n other stuatons. O. Khatb [19] resents an artfcal otental feld to drve a robot n a comlex envronment usng the feld generated by obects as attracton (target) and reulson (obstacles) forces. The otental model roosed by Mnh Son Dao [17] s talored to the context of mage matchng and t can be used to attract a temlate of the searched obect or a sketch drawn by a user n the oston where a smlar shae s resent n the mage and the concet of edge otental functons (EPF). In fact, the more smlarty the two shaes take, the hgher total attracton can be gendered by the edge feld. In ths secton, we gve the concet of curve otental functon accordng to EPF. 1) Edge Potental Functon (EPF for short) In the mage, the coordnates of the th edge ont x, y, whch s assumed to be equvalent to a ont s ( ) charge (, ) Q x y, contrbutng to the otental of all eq mage xels. (, ) 1 Qeq x y EPF ( x, y) = (4) 4 πε ( x x ) + ( y y ) 2 2 eq Where ε eq s a constant that measures the equvalent ermttvty of mage background, takng nto account the extent of attracton of each edge ont. That s, ε nfluences the sread of the otental functon whch makes t more stee or smooth deendng on ts magntude. 3) Curve Potental Functon (CPF for short) eq In our aroach, the onts n curve s taken as the feld onts, and the xels n the surroundng area of the curve are looked as source charges (the area around the curve segment can be taken as suort for the border regon as shown n Fgure. 1). The dfference value of xels gray between onts n the curve and the surroundng area of the curve s the quanttes of charge. The curve otental energy s:

3 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY Where (, ) n m I( x, y ) I( x, y ) = (5) = 1 = 1 ( x x ) + ( y y ) 2 2 x y and ( x, y ) are the coordnates of the th ont n the curve and the th ont n the range l k surroundng the th ont resectvely. I( x, y ) and I( x, y ) are ther gray values, n and m are the numbers of onts n the curve and the range l k resectvely. d d Fgure 1. Suort Regon Suort Regon of Curve Curve 3) Smlarty Measure based on Curve Potental Functon (CPF for short) For the rght and left vews I( x, y) and I ' ( x, y ), L and L' are the corresondng curves of the rght and left vews. The formula for the matchng metrc s: ρ ( L, L ) = CEPF( L ) (6) and CEPF( L ) are the average otental energes of the curves L and L' resectvely = (7) n Where and n resectvely denote curve otental and the number of onts n the curve. The value ofρ s very small but greater than 0, and the two curve segments are more smlar. By the formula (5), (6) and (7), we know that the segment matchng based on the otental energy of a curve not only makes use of gray-scale characterstcs of a suort regon, but also exlots the length of a curve and the geometrc relatonsh between xels, so t has a good smlarty measure for a curve segment. III. THE CURVE MATCHING METHOD Frstly, our method marks the crossng onts of mage contour, and then detects feature onts of curves wth the feature ont marked smultaneously. So each marker s the endont of one or more curve segments. Based on these markers we resent a new matchng method of curve segment. The man dea of the method s to use NCC and the brghtness varance of ASD for matchng markers roughly, the two vew markers set s dvded nto many-to-many matchng ont sets, and then adot dynamc rogrammng algorthm for matchng curve segments among matchng onts usng curve otental as smlarty measure. A. Pont Matchng Marker sets of two vews are dvded nto many-to-many matchng ont sets by gray nformaton of marker onts and the correlated method. The smlarty between marker onts n mage I and marker ont q n mage I' s calculated accordng to formula (1). To ncrease seed of searchng and accuracy of matchng, For the 3 3 rectangular area takng as the central ont 1 1 n mage I, we fnd a W H rectangular area n 4 4 mage I' (W and H are the wdth and heght of mage I' resectvely) and central ont of ths area has the same coordnates as. Accordng to formula (1), the smlarty NCC between marker and q can be calculated (f NCC>ε, then and q match): { (, ) } P = q NCC q ε (8) Where P s the feature onts set n the mage I' that match wth the feature ont n the mage I. Smlarly, feature onts n the mage I' can search matchng feature onts set n the mage I. B. Curve Segment Matchng 1) The Idea of Curve Segment Matchng Because of contour segment based on marked onts (feature onts and crosses), each marked ont s endonts of one or more curve segments. We assume that curve segments set { PL} s made u of curves whose endonts are the marker ont and s a startng ont and onts of the set { P } are endng onts n the mage I (Fgure.2 (a)). The feature onts set { P } n mage I' s made u of onts matchng the feature ont n mage I. Assume. k { P }, then curves set { PL } s made u of curves whose startng and endng onts are onts set { } P resectvely, as shown n Fgure 2 (b). P and { } Fgure 2 (a) Lne segments startng at feature ont n mage I,, (b) Lne segments startng at feature onts n mage I' matchng wth n mage I

4 1542 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY 2012 Search the ont n set { P } matchng wth the onts P, suose + l { P }. The curve segment s n set { } occluded f there s no matchng ont n{ P }, so there s no matchng curve segment n set{ PL }. If only ont n set { }, k + l other hand, only ont + P matches wth ont l (on the + n set { } l P matches wth ont, ) and the curve segment only ends at k + l ont, k + l, then the curve segment k k k + l + matches. Two sets of curve segments wll be wth,, comosed by the endng onts and ther corresondng startng onts n one of the followng cases: 1) the onts + are not unque; 2) n { P } matchng wth ont l n { } ont + l matches only one ont + l P, but may have more than one matchng ont n mage + l I; 3) ont + l matches only one ont + l n { P }, but more than one curves end at + l. As shown n Fgure 2, suose onts, k wth ont n { },, k + 1 n { P } s ncluded n set {, k, k,, k, k + 1,, k + 1, k} P match, then the curve whch matches wth. So curve segments sets { PL} and{ PL }, whch take and { P } as startng onts resectvely, are dvded nto some corresondng subsets whch can be matched by dynamc rogrammng. The curve segment and curve otental resectvely are the matchng unt and matchng measure between the corresondng subsets of curve segments. 2) Dynamc Programmng Curve segments can be dvded nto several subsets by matchng relatonshs among the endonts of curve segments, and the ntersecton among these subsets cannot be emty. Usng the otental energy of curve segments as the smlarty measures, we know that the two curve segments match f the absolute value of the otental dfference closes to zero. m curves start at marked ont n mage I and end at m marked onts. If n onts n mage I' match wth, then there are k curve segments startng at these n onts, whch nclude matchng curves wth the m curves startng at. Among the endng onts of these k curves n mage I', we search the onts whch match wth the endng onts of the m curves n mage I. Then a set can be made u of c curves that end at the matchng onts, and the k curves n mage I' can be dvded nto m subsets EI = e, x = 0,1,, z, z = c, the EI, { x } ntersecton among whch may not be emty. The m curves startng at n the mage I can be dvded nto m subsets whch has one element ( EI { e } = ). The curves can be matched among corresondng sets EI and EI by dynamc rogrammng. Dynamc rogrammng equaton can be exressed as: m k k (9) e q e f F( d) = mn( d = e e ) d = e e = CEPF( e ) CEPF( e ) (10) k k Where e EI, e k EI, m s the number of the subsets, and d s the dfference of the absolute value of average otental energy of two curves e, e. C. Exerment and Analyss In order to verfy the effect of the roosed method n ths aer, we frstly rotate the mages artfcally, match the edges between the transformed mages and the orgnal ones, and analyze quanttatvely the erformance of the method by the match evaluaton crtera (namely, the average reeatablty [18]). Fnally, we adot mages taken from dfferent drectons by a dgtal camera to verfy the method. Fgure 3 gves the schematc dagram of edge matchng between the rotatonally transformed mage and the orgnal one, and Fgure 4 and Fgure 5 resectvely show the exerment results of comlex and smle scene mages taken from dfferent drectons. The crteron of average reeatablty s the number of curve segments determned by feature onts. In the orgnal mage, the number of curve segments n each of the transformed mages s the same as the number of matched curve segments between the orgnal and transformed mages. It can be defned as: Where N and o Nm ( 1 1 ) avg 2 N N R = + (11) o t N t are the numbers of curve segments n the orgnal and transformed mages resectvely and N m s the number of matched curve segments between them. Fgure 3 llustrates the curve matchng between the edges of the orgnal and transformed mages. In Fgure3, (a) s the orgnal mage, (b) s the transformed mage obtaned by rotatng the orgnal one by 10 degrees, (c) and (d) are the edges of (a) and (b) resectvely. The curve segment labels are gven n Fgure3 (c) and (d), and curve segments wth the same label are the match segments. Fgure 3 (a) and (b) show that the edges of the two mages are resectvely dvded nto 24 curve segments accordng to feature onts and the number of the matched curve segments s 21. The value of average reeatablty R s 87.5 ercent accordng to formal avg (11). The exermental data shows that ths method has a hgh degree of matchng. k

5 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY (a) (b) (a) (c) (d) Fgure 3. Curve matchless between the edges of the orgnal and transformaton mages. (a) Orgnal mage, (b) Transformed mage, (c) Edge of orgnal mage, (d) Edge of transformed mage We aly the resented method to curve segment matchng of a real word scene. As Fgure 4 shows, mages are taken from dfferent drectons by dgtal camera. (Fgure 4 (a), (b) are the left and rght vews resectvely). Fgure 4 (c) and (d) dect the edges of the mages n Fgure 4 (a) and (b) resectvely. The black marks n Fgure 4 (c) and (d) are the matchng curve segments, and the matchng curve segments have the same labels, the red ellses are used to hghlght the ncorrect matchng of curve segments. Fgure 4 (c) and (d) show that there are some omssons of curve segment matchng because of the error n feature ont detecton. For examle, n Fgure 4 (c) and (d), we can see that the curve segment marked by red ellse and labeled by 1 n the left vew does not match wth the one marked by red ellse and labeled by 1 n the rght vew. But n the orgnal mages shown n Fgure 4 (a) and (b), the corresondng arts do really match by human observaton. In Fgure 4 (c) and (d), the curve segments, whch are hghlghted by red ellses and labeled by 2, 3, 4, have no corresondng matchng segments because of the occluson of real word scene and the edge extracton. Comarng Fgure 3 (c), (d) wth Fgure 4 (c), (d), we can see that the contour matchng of real word scene mages taken from dfferent drectons s less effectve than the contour matchng of orgnal and theoretcal rotaton mages, because there s no occluson between them. (b) (c) (d) Fgure 4. The curve segment matchng of rofle n the left and rght vews. (a) Left vew, ( b) Rght vew, (c) The edge of left vew, (d) The edge of rght vew.

6 1544 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY 2012 (a) (b) (c) (d) Fgure 5 the curve segment matchng of rofle n the left and rght vews. (a) Left vew, ( b) Rght vew, (c) The edge of left vew, (d) The edge of rght vew. Fgure 5 shows edge matchng results of smle scene (leaf). Fgure 5 (a), (b) are the left and rght vews resectvely. Fgure 5 (c) and (d) dect the edges of the mages n Fgure 5 (a) and (b) resectvely. The black marks n Fgure 5 (c) and (d) are the matchng curve segments, and the matchng curve segments have the same labels, the red marks ellse. From Fgure 5 (c), we know that the feature onts marked by ellses labeled by 1, 2 are the false detecton onts, but they don t nfluence edge matchng because the otental of curve segment started them are bg dfference wth other otental of curves. The curve segments marked ellses labeled by 4, 2 n Fgure 5 (c) have not corresondng match curves, but n the orgnal mages they should have, whch s due to false detecton onts marked by ellse labeled by 2 n Fgure 5 (d). Comarng Fgure 4 wth Fgure 5, we know that the method roosed n the aer has good effect for smle scene mages. For the mages taken from dfferent drectons, the effects of curve segment match are nfluenced by edge and feature ont detectons whch are dfferently affected by external envronment (such as lght) and occluson. The method roosed n the aer frstly realze the rough match of feature onts, so that curve segments of mage contours can be dvded nto curve segments subsets accordng to the subset of rough match feature onts. The curve segments wll match between the corresondng subsets. Therefore the method greatly reduces the search sace of matchng, greatly mroves the tme comlexty, and makes full use of the geometrc relatonshs among the curve segments. Meanwhle, the method also take advantage of the satal relatonsh among xels around curve segments snce the otental energy n ssuort regon of curve s used n the measurement crtera of curve segments matches. IV. CONCLUSION The aer analyzes onts feature, lnes feature matchng methods and ther measurement crtera, and rooses a curve segment matchng method based on the exstng mature technology of onts matchng and the concet of the curve segment otental energy functon. The man dea of the roosed method s that the key (corners and ntersectons) onts are dvded nto subsets by rough match feature ont subsets, and curve segments among corresondng subsets can be matched by usng dynamc rogrammng algorthm. The roosed method makes full use of feature onts, the relaton between feature onts and curves, the geometrc relatonshs among the curve segments and gray sace. Through analyss, the roosed method has better matchng accuracy and hgher effcency. However, mages taken from dfferent drectons are affected dfferently by envronment and have dfferent occluson, whch affects edge detecton, feature ont detecton and matchng wth varous degrees. ACKNOWLEDGEMENT The authors would lke to thank the anonymous revewers for ther helful comments and suggestons. Ths work s suorted by Natural Scence Foundaton Proect of CQ CSTC (No: cstca40006), Dr. Research Funds of Chongqng Normal Unversty (No: 10XLB19), Chongqng Educaton Commttee (No: KJ090809) and Chongqng Hgher Educaton Educatonal Reform (No: ).

7 JOURNAL OF SOFTWARE, VOL. 7, NO. 7, JULY REFERENCES [1] R. Guta and A. Mttal. SMD: A locally stable monotonc change nvarant feature descrtor. In Euroean Conference on Comuter Vson, [2] M. Hekkla, M. Petkanen, and C. Schmd. Descrton of nterest regons wth local bnary atterns. Pattern Recognton, 2009(42): [3] F. Tang, S. H. Lm, and N. L.Chang. A novel feature descrtor nvarant to comlex brghtness change. In Proceedngs of the IEEE Comuter Socety Conference on Comuter Vson and Pattern Recognton. IEEE, 2009, [4] S. Wnder, G. Hua, and M. Brown. Pckng the best DAISY. In Proceedngs of the IEEE Comuter Socety Conference on Comuter Vson and Pattern Recognton. IEEE. 2009, [5] Cordela Schmd, Andrew Zsserman. Automatc Lne Matchng across Vews. Internatonal Conference on Comuter Vson & Pattern Recognton (1997) [6] C. Schmd, A. Zsserman, Automatc lne matchng across vews. IEEE Internatonal Conference on Comuter Vson and Pattern Recognton, [7] C. Schmd, A. Zsserman, The geometry and matchng of lnes and curves over multle vews, Int. J. Comut. Vson 40 (3) (2000) [8] Y. Deng, X.Y. Ln, A fast lne segment based dense stereo algorthm usng tree dynamc rogrammng, n: Euroean Conference on Comuter Vson, [9] Herbert Bay, Vttoro Ferrar, Luc Van Gool. Wde-Baselne Stereo Matchng wth Lne Segments. Proceedngs of the IEEE Conference on Comuter Vson and Pattern Recognton, San Dego, June [10] Zhheng Wang, Fuchao Wu, Zhany Hu. A robust descrtor for lne matchng. Pattern Recognton 42 (2009) [11] Zhheng Wang, Hongmn Lu, Fuchao Wu. HLD: A robust Descrtor for Lne Matchng. 11th IEEE Internatonal Conference on Comuter-Aded Desgn and Comuter Grahcs, 2009, [12] Dong-Mn Woo, Dong-Chul Park. Stereo lne matchng based on the combnaton of geometrc and ntensty data. 24th Internatonal Symosum on Comuter and Informaton Scences, 2009, [13] Dong-Mn Woo, Dong-Chul Park, Seung-Soo Han and Seunghwa Beack. 2D Lne Matchng Usng Geometrc and Intensty Data. Internatonal Conference on Artfcal Intellgence and Comutatonal Intellgence, 2009, [14] Pascal Vasseur, Cédrc Demonceaux. Central Catadotrc Lne Matchng for Robotc Alcatons IEEE Internatonal Conference on Robotcs and Automaton Anchorage Conventon Dstrct May 3-8, 2010, Anchorage, Alaska, USA. 2010, [15] Hyunwoo Km, Sukhan Lee. A Novel Lne Matchng Method Based on Intersecton Context IEEE Internatonal Conference on Robotcs and Automaton Anchorage Conventon Dstrct May 3-8, 2010, Anchorage, Alaska, USA. 2010, [16] Deng BaoSong. Researeh on 3D Reeonstructon of Mult-Vew Wde Baselne Images Based on Pont and Lne Features. PhD Thess, Natonal Unversty of Defense Technology, Changsha, Hunan, P.R.Chna. Setember [17] Mnh-Son Dao, Francesco G. B., De Natale, Andrea Massa. Edge Potental Functons (EPF) and Genetc Algorthms (GA) for Edge-Based Matchng of Vsual Obects. IEEE Transactons on Multmeda, Vol.9, No. 1, January [18] Qngsheng Zhu, Yanxa Wang, Huun Lu. Auto-detecton corner based on egenvectors of covarance matrces. Journal of Software. Vol.. 5, No. 8. August 2010, [19] O. Khatb. Real-tme Obstacle Avodance for Manulators and Moble Robots [J], Internatonal Journal of Robotcs Research. Vol. 5, No. 1, 1986, Yanxa Wang was born n Shandong, Chna, n She receved PhD, MS and BS degrees n Comuter Scence from Chongqng Unversty, Chna n 2010, 2007 and 1999 resectvely. Her current research nterests nclude mage rocessng, attern recognton. She s a lecture at College of Comuter and Informaton Scence, Chongqng Normal Unversty, Chongqng, Chna. Yan Ma Receved Bachelor degree n hyscs electroncs from yunnan unversty, Kunmng, Chna n 1982, Master degree n Informaton technology from Huazhong Normal Unversty, Wuhan, Chna n 1993 and PHD n artfcal ntellgence from Southwest Unversty, Chongqng, Chna. Hs current research nterests nclude artfcal ntellgence, semantc meshes, educaton grd. He s a rofessor n College of Comuter and Informaton Scence, Chongqng Normal Unversty, and comuter and nformaton scences dean, chongqng normal unversty, academc advsor for master's canddate. Mr. Ma s a member of Edtoral Board of Internatonal Journal of Dgtal Content Technology and ts Alcatons and Journal of Communcatons and Informaton Scences. Qxn Chen Was born n Guzhou, Chna, n He receved MS and BS degrees n Comuter Scence from Chongqng Unversty, Chna n 2007 and 1998 resectvely. Hs current research nterests nclude mage rocessng and feature reduces. He s a lecture at College of Informaton Scence, Guzhou Unversty of Fnance and Economcs, Guyang, Chna.