Interesting Target Detection and Matching Algorithm Based on Phase Information

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1 009 Intenational Confeence on Machine Leaning and Comuting IPCSIT vol IACSIT Pess Singaoe Inteesting Taget Detection and Matching Algoithm Based on Phase Infomation hitao Xiao Jun u Lei Geng Jianming ang Nini Xu higui Lin School of Infomation and Communication Engineeing Tianjin Poltechnic Univesit Tianjin China Abstact. Inteesting taget detection and matching algoithm in comle natual backgounds images is studied in this ae. istl loggabo filte bank is analzed which is consistent with human visual sstem chaacteistics. Seveal kinds of local featues fom the filte bank can fom the integated featue. Integated featue conguenc IC model is established. And uon comensating noise fo IC an imoved integated featue conguenc IIC model is obtained in which taget detecting is tanslated to find the inteest oints that ae significant acoss scales and oientations. This model is alied to comle natual backgounds images fo taget detection and matching. Eeimental esults show that this method s efomance. Kewods: inteesting taget detection image matching hase infomation integated featue conguenc. Intoduction Taget detection and ecognition has been widel used in man fields. And inteesting taget detection is the ke at in which the main oblem is how to select elevant infomation into a limited attention bottleneck. The taget image shows a comle scene it contains a mitue of vaious tes of elevant featues unwanted details and noise and an useful eesentation must emove such noise and unwanted details. The eisting taget detection methods can be categoized into thee kinds. The fist one is the CAR detection algoithm []. The second one is using the factal featue fo taget detection. In the detection of given size taget etended factal featue method is ut fowad [ 3]. The thid one is the mohologic techniue fo given shae taget. ith the develoment of wavelet wavelet-based multiscale methods fo taget detection have aeaed such as method based on wavelet multiscale etended factal featue [4] and eneg featue based on wavelet [5]. In inteesting taget detection it seems that ocessing is just fo the taget eisting in the image. The eason ma be simle because the imotant oblem is how to select useful infomation fom ve limited infomation. And thee eists anothe eason that diffeent visual tasks ma involve the analsis of ve distinct satial locations on the image. bject featue detection can be imlemented b local hase conguenc i.e. local eneg model [6 7]. But the ediction of inteesting taget can not siml be based on the satial locations of moe elevant objects in the scene because the object to be detected taget ma be alteed b some kind of camouflage fo eamle. A camouflaged taget ma emain undetected b means of the local eneg model using nonmaimal suession via the theshold of 0% o 0% of the global maimum value since it is alteed b camouflage. Howeve it is obvious that the taget might still be eceived in the scene in case it would be judged unlikel to be accidental in oigin and theefoe it should Coesonding autho. Tel.: ; fa: addess: zhitaoiao@ahoo.com. 08

2 have some degee of alignment in its statistical stuctue acoss a numbe of scales and oientations. Conseuentl it ma be detected via a featue ecetion model in which inteest oints ae locations of locall maimal conguence in cetain combinations of seaable featues [8]. In this ae seveal seaable featues ae analzed and the integated featue conguenc IC model is established fistl. Then b noise comensation fo IC we get the imoved integated featue conguenc IIC model. And it s used in taget detection and matching.. Phase filte bank Multi-scale analsis is an effective method in the field of taget detection and ecognition of natual images. ilte bank used in the decomosition of image consists of loggabo filtes of diffeent satial feuencies and oientations [9]. B definition loggabo filte has no DC comonent and its tansfe function has etended tail at the high feuenc end. So it should be able to encode natual images moe efficientl than odina Gabo functions which would ove-eesent the low feuenc comonents and unde-eesent the high feuenc comonents in an encoding ocess. Anothe oint in suot of the loggabo function is that it is consistent with measuements on human visual sstems which indicate we have cell esonse that ae smmetic on the log feuenc scale. A loggabo filte is a Gaussian function in the satial feuenc domain aound some cental feuenc 00. In the feuenc domain it can be eesented as a comle. The eal at is called the smmetic loggabo filte and the imagina at is called the -smmetic loggabo filte as follows G G G [log / 0] 0 e e [log / 0 ] whee 0 is the oientation angle of the filte 0 is the cental adial feuenc and and ae the angula and adial sigma of the Gaussian esectivel. The convolution of a loggabo function whose eal and imagina ats ae in uadatue with a eal image esults in a comle image. Its nom is called eneg and its agument is called hase. Let be the image convolved with the -smmetic loggabo filte and be the image convolved with the -smmetic loggabo filte. In this ae fou diffeent esolutions and si diffeent angles fo each esolution ae chosen. To ensue that the filte bank coves the -D feuenc lana smoothl its distibution stuctue shae likes ose. 3. eatue comuting Local infomation of comle natual image can be eesented b a collection of seaable featues etacting seveal individual chaacteistics of the scene. o comle natual image inteesting taget can be defined as the aea whee seveal featues ae most consistent. Befoe defining the integated featue conguenc function we fist define seaable featues and integated featues. 3.. Seaable featues comuting Using each loggabo filte G we can get the seaable featues catuing chaacteistics of the esective filte outut and [8 0]. Nomalized -smmetic filte outut Nomalized -smmetic filte outut 3 Measue of smmet ma ma ma ma 09

3 0 ma ma Measue of asmmet ma ma Local contast of -smmetic filte outut 5 Cad 5 whee Cad ma ma Cad 6 Local contast of -smmetic filte outut 6 Cad 6 whee Cad ma ma Cad 3.. Definition of integated featue In eseach on theo of human attention featue integated featue is defined as a aticula subset of seaable featues at a fiation oint []. o an loggabo function in the filte bank the integated featue at can be defined as an L-D featue vecto ] [ L 7 whee i is the seaable featue of filte outut i L. Diffeent combination of these seaable featues can get diffeent integated featue which leads to diffeent detection esults. 4. Integated featue conguenc model To measue the conguenc of integated featue acoss scales and oientations integated featue conguenc function IC is given. undamental to this definition of the integated featue conguenc function is the selection of a oimit measue between integated featues at each scale and oientation. This oimit measue should be given in a numeical fom to indicate the degee of esemblance of integated featues ove all loggabo filtes at each scale and oientation. And integated featues at each scale and oientation ae eessed as vectos and conseuentl the most obvious choice fo measuing conguenc among integated featues is the nomalized coelation defined as the cosine of the angle between nomalized vectos. This definition is fast to comute and usuall suffice. Definition: The conguenc function in cetain integated featue at location is defined as z IC 8 whee and the coelation is defined as cos

4 with cos being the cosine of the angle between vectos and. The nomalization is whee is a small ositive constant to avoid division b zeo. Given a aticula definition of integated featue and the integated featue conguenc function IC inteest oints ae siml detected as locations of local maima of the function IC. 5. Imoved integated featue conguenc model IIC In taget detection fom comle backgound noise ocessing is a ke at. Geneall the following assumtion about noise is tue: noise is additive noise is evewhee in an image and its level is geneall constant and 3 featues occu sasel in an image. Then noise comensation method can be used [7]. e use noise comensation in integated featue conguenc function IC and get the imoved integated featue conguenc function IIC. 5.. Noise comensation Kovesi studied the noise and its elationshi with filte esonses in hase conguenc eseach [6]. He thought that the esonse of the smallest scale highest cente feuenc filte ai in the filte bank will be almost entiel due to noise. Using Kovesi noise comensiation method we can get new integated featue conguenc function IIC T 9 z Note that the noise comensation is efomed in each oientation indeendentl. This is because the image noise content is anisotoic that is the noise vaies with diffeent oientation. And in actice this has been found to give significantl bette esults. 5.. Comutational comleit of IIC om the definition of IIC we know that the comutational load of IIC is fail high. It needs T IT and mati multilication duing its imlementation. If the image size involved is N N it will need about 5 N log N comle multilications 50 N log N comle addition and N eal multilications. Hee the adi- T and IT ae assumed. 6. Eeimental esults To demonstate the efomance of IIC we comae IIC with IC hase conguenc model PC [6] and taget detection algoithm based on wavelet and eneg [] esectivel. Hee the numbe of scales used in loggabo filte bank is 4 the numbe of oientations is 6 and the scaling of cente feuenc is. The image size is 8 8. And the featue combination is { 3 4 }. 6.. Comaisons between IIC and detection algoithm based on wavelet and eneg This comaison mainl tests the detection abilit to weak-small taget. The esult is shown in ig.. The contast in ig.a between the taget and the backgound is lowe than 3%. The IIC detection esult is consistent with human visual chaacteistics and shows that IIC has good effect fo weak-small taget detection. In the detecting esult based on wavelet and eneg thee is still some noise. aiginal bresult of IIC cresult of Ref.[4] ig.: Comaison fo weak-small 6.. Taget matching based on IIC and PC Combining IIC and Phase nl Coelation algoithm PC we get a new kind of hase-based image matching method PIM.

5 Subseuentl b mathematical moholog and egion labeling we eliminate comle backgound distubance and locate the matching tagets eactl with ocessing esults of the algoithm of Phase nl Coelation. And the matching tests ae shown in ig.. 7. Conclusion Some eisting taget detection algoithms ae discussed. LogGabo filte bank used in this ae is intoduced. Si seaable featues ae analzed and defined and integated featue conguenc model IC is established. Then b noise comensating fo IC we get the imoved integated featue conguenc model IIC. And its comutational comleit is analzed biefl. The test esults show that IIC can detect tagets fom comle natual backgounds scene effectivel. Not onl can it be used in single taget image but also in multi-tagets image. Not onl is it suitable to big taget but also to weak-small taget. And it is geneal and has good anti-noise abilit. Eeimental esults show that the PIM algoithm is effective in detecting inteesting tagets and locating the matching tagets eactl. This algoithm is invaiant to image illumination contast otation and scaling. And this model is obust geneal and accods with the human vision sstem HVS chaacteistics. 8. Acknowledgements e thank the National Natue Science oundation of China Refeences a Little taget b IICM esult c Matching esult aiginal biicm esult cmatching esult a3multi tagets b3 IICM esult c3matching ig.: Matching esults [] Anastassooulos V.Lamooulos G. A new and obust CAR detection algoithm. IEEE Tans. AES 99 8: [] Kalan L.M. Imoved SAR taget detection via etended factal featues. IEEE Tans. AES 00 37: [3] Kalan L.M. Etended factal analsis fo tetue classification and segmentation. IEEE Tans. Image Pocessing 999 : [4] ei Y. ang X.. Shi.L. et al.. Taget detection method based on wavelet multi-scale etended factal featue. Jounal of Notheasten Univesit 006 7: [5] Chen X.. Sun H.Y. Taget detection based on eneg featue. Infa and Lase Engineeing 00 30: 30-3 [6] Kovesi P. Invaiant measues of image featues fom hase infomation. Deatment of Comute Science Univesit of esten Austalia Peth 996 [7] Robbins B. The detection of D image featues using local eneg. Deatment of Comute Science Univesit of esten Austalia Peth 996 [8] Gacia J.A. Vidal X.R. Sanchez R.R. et al. Minimum eo gain fo edicting visual taget distinctness. tical Engineeing : [9] Xiao. Hou. Miao C. et al. Using hase infomation fo smmet detection. Patten Recognition Lettes : [0] Xiao. u J. Geng L. et al. Imoved integated featue conguenc model and its alication. Poc. 0 th IEEE Intenational Confeence on High Pefomance Comuting and Communications Dalian 008 : [] Teisman A.M. Gelade G. A featue-integation theo of attention. Cognitive Pscholog 980 : [] ang L.R. Reseach of taget detection based on wavelet tansfom. Jilin: Jilin Univesit 006