Detection of an Object by using Principal Component Analysis

Transcription

1 Detecton of an Object by usng Prncpal Component Analyss 1. G. Nagaven, 2. Dr. T. Sreenvasulu Reddy 1. M.Tech, Department of EEE, SVUCE, Trupath, Inda. 2. Assoc. Professor, Department of ECE, SVUCE, Trupath, Inda. Abstract Ths paper represents the object recognton from dfferent categores of mages by usng prncpal component analyss. The objects are represented by nternal or external descrptors. The descrptors are found by usng sgnature and prncpal component analyss.the sgnature features are helpful to dstngush the set of objects based on ther external shape but when t fals, the prncpal component analyss s used nstead. The proposed method s mplemented, traned, and tested usng matlab on a set of mages. The results show the effectveness of the proposed system n recognzng the objects. Keywords Template matchng, PCA, sgnature. Introducton Object recognton system are used n many applcaton lke robotcs, optcal character recognton, object clusterng and trackng, mage watermarkng, mage panoramas [1, 2,3]. Features are used to dstngush and recognze objects. Ths process s completed by comparng the extracted features wth prevously stored and known features of dfferent objects. The above applcatons needs a computer program, so they can be able to recognze dfferent types of objects as explaned n [1, 2,4]. The work based on Gabor wavelet for object recognton conssts. (a) Salent pont detecton to overcome the strategy of fndng the objects present n the partcular part of the mage. (b) patch extracton to extract the sub square mages from the orgnal mages over the salent ponts. (c)feature extracton to handle varous complex mages whch looks dfferent n dfferent crcumstances. The mages can be dscrmnated by Gabor wavelet features rrespectve of ther localzaton. The obtaned features are dstngushed by SVM classfer. The object recognton task that s to recognze object or non object. Proposed Method The proposed system conssts of extractng sgnature features and ths method works very well n the cases, lke when ther s no dstorton and occlusons. In ths case PCA s consdered as an addtonal feature when t fals n recognzng the object. The paper s organzed as follows: In secton Ι a defnton of template matchng has been presented. In secton ΙΙ the overall system scheme has been started. Extractng sgnature features has been gven n secton ΙΙΙ. The prncpal component analyss has been gven n secton ΙV. Fnally secton V llustrates the dscusson and results. 1. Template Matchng Template matchng s a process of fndng the locaton of a sub-mage (.e. template) nsde another bg mage. It s the man technque and an essental task n mage analyss applcaton. The features extracted from the boundary of objects are compared wth the prevously stored known features for recognton purpose [5]. It s also called features matchng because extracted features are matched wth the known features. Eucldean dstance s measured between the object features and stored features and then calculate the matchng between the object features and the stored features. The Eucldean dstance between ponts p and q s the length of the lne segment. In Cartesan coordnates, f P = (p 1,p 2,------p n ), Q = (q 1,q 2, q n ) 3411

2 are two ponts n Eucldean space, then dstance from p to q or q to p s gven by,,,..., n n d p q d q p q p q p q p d p q q p n 1 When a match s found, t s deemed to be recognzed. 2. Block Dagram of the System Input mage Pre-processng Classfcaton Fgure1: block dagram of the system. Here the nput s an mage. In pre-processng step the regon of nterest (ROI) s determned, then the gven nput mage s converted from RGB to gray scale mage and then to bnary mage. In thrd extractng features step, the features were extracted for all the objects that have been obtaned from the prevous stage. The features we have got are matched wth the features of the objects. The matchng between features s carred out based on Eucldean dstance metrc, and they are used to decde whether the object s recognzed or not. Fnally the system classfes the object and put a descrpton accordng to the object types. An Overvew In order to classfy the objects good features or a set of features are needed. To recognze the objects, the sgnature feature s one of the most useful features. When sgnature comes to the gref, the prncpal component analyss s used. In ths case PCA [6, 7, 8] supports the sgnature feature when t fals n recognzng the object. Because the sgnature of the object s an external descrptor, whch s heavly effected by the nose. PCA s an nternal descrptor, so PCA s adopted, whch wll descrbe the object as a whole mage. 2 Extractng Features Matchng Features 3. Sgnature Feature Extracton The sgnature s an one-dmensonal functon, that can extracted by several method. Here we used the dstance from the centrod to the boundary of the object as a functon of angle.it reduces the boundary dmensons from 2D to 1D sgnature functon. The sgnature features reles on scalng and t s sze and rotaton varant. The sgnature values are normalzed. These ponts were saved for each object as a vector feature. Further t wll be used for recognton and matchng. 4. Prncpal Component Analyss Analyss of multvarate data plays a key role n data analyss. Multvarate data conssts of many dfferent attrbutes or varables recorded for each observaton. It s hard to vsualze multdmensonal space. Prncpal component analyss (PCA) s a famous multvarate technque and s manly used to reduce the dmensonalty of mult varables to two or three dmensons. PCA summarzes the varaton n a correlated mult varables to a set of uncorrelated varables, each of whch s a partcular lnear combnaton of the orgnal varable. The extracted uncorrelated varables are called prncpal components (PC) [12, 13, 14]. The number of Prncpal components s less than or equal to the number of orgnal varables. Prncpal components are found by calculatng the egen vectors and egen values of the data covarance matrx. Let A be an nxn matrx. The egen values of A are defned as the roots of determnant(a-λi) = (A-λI) = 0 Where I s an dentty matrx. Ths equaton s called characterstc equaton. Let λ be an egen value of A. Then there exsts a vector x such that Ax = λx Ths vector x s called an egen vector of A assocated wth the egen value λ. 3412

3 The covarance values are computed usng followng equaton: n 1 Sxy X X Y Y n 1 1 Here n represents sample sze. 5. Results and Dscusson The followng algorthm has been appled on dfferent mages. The result of one sample s shown n dfferent stages. Start The mages are all of the same resoluton and are all equvalently framed. Each pxel can be consdered a varable thus we have a very dmensonal problem, whch can be smplfed by PCA. The man goal of the PCA s to reduce dmensonalty by extractng the smallest number of components that account for most of the varaton n the orgnal multvarate data and to summarze the data wth lttle loss of nformaton. To determne the most dscrmnatng features between mages of components PCA has been used. PCA has been called one of the most valuable results from appled algebra. Formally, n mage recognton an nput mage wth n pxels can be treated as a pont n an n-dmensonal space called the mage space. Most of the mage pxels wll be hghly correlated. Thus we need to consder how to acheve a reducton n the number of varables. PCA helps n reducng the dmensons of the varables. PCA can supply the user wth a lower dmensonal pcture, a projecton or shadow of ths object when vewed from ts most nformatve vewpont. Ths s done by usng only the frst few prncpal components, so that the dmensonalty of the transformed data s reduced. yes Read Image Convert nto Bnary Bnary Morphologcal operatons Get sgnature Recognzed? no Use PCA End fgure2: flow chart of the system. The nput RGB mage was read, then, the ROI s obtaned and t was converted to gray level mage. Fgure3: The nput mage. 3413

4 Fgure4: The gray level of the nput mage. Then the gray level mage was converted to a bnary mage. Fgure7: Orgnal mage wth boundng boxes. To test the ablty of the proposed method dfferent mages are taken and processed. The exstng method s object recognton usng Gabor wavelet features. Fgure5: The bnary mage. The normalsed set s shown n fgure(6). Fgure8: The nput mage. Fgure6: The tranng set of PCA. Fgure9: The nose mage. At frst, RGB mage s taken as an nput shown n fgure(8). The Gaussan nose s added to the nput mage and s shown n the fgure(9). SNR s calculated between orgnal and nosy mage. Smlarly the nose level s ncreased and SNR s calculated between orgnal and nosy mage. 3414

5 Nose(db) Table 1 SNR for Exstng method SNR for Proposed method Applcatons,2008. DICTA 08.Dgtal Image,pp ,(2008). [4]. Turk M., Pentland A.: Face Recognton usng Egen-Faces. Proceedngs of Computer Vson and pattern Recognton,pp ,IEEE(1991) The above table gves the comparson between exstng method and proposed method. From ths we can say proposed method s better. Conclusons An algorthm for recognton of an object s mplemented n ths paper. Here manly the objects are recognzed by usng template matchng, sgnature values and PCA. The proposed system can recognze multple objects based on the features extracted. In ths we fnd the locaton of a sub-mage(.e. template) nsde another bg mage for recognton purpose. The proposed method ncreases the stablty and gves the better results compare to the exstng method. It recognzes the objects wth greater accuracy and wth good recognton rate. References [1]. Neufeld J.E., Hall T.S: Probablstc Locaton of a Populated Chessboard usng Computer vson. Crcuts and systems(mwscas), rd IEEE Internatonal Mdwest Symposum on Crcuts and Systems,pp ,IEEE,(1991). [2]. Blunsden S.: chess Recognton. Ph.D. Dssertaton, Unversty of Plymouth.BSc(Hons)Computng and nformatcs.(2003). [3]. Tam K.,Lay J., Levy D.: Automatc Grd Segmentaton Of Populated Chessboard Taken at A Lower Angle Vew. Computng Technques and [5]. Yang Z.: Fast Template Matchng Based on Normalsed Cross Correlaton wth Centrod Boundng. Proceedngs of the 2010 Internatonal Conference on Measurng Technology and Mechnacs Automaton Vol. 02(ICMTMA 10), Vol. 2. IEEE Computer Socety, Washngton, DC, USA, pp (2010). [6]. Swets D.L., Weng J.: Usng Dscrmnant Egen Features for Image Retreval. IEEE Trans. Pattern Analyss and Machne Intellgence, vol.18, no.8, pp ,(1996). [7]. De la Torre F.,Black M.J.:Robust Prncpal Component Analyss for Computer Vson. ICCV Proceedngs of Eghth IEEE Internatonal Conference on Computer Vson, vol.1,pp ,(2001). [8]. Jollffe I. T.: Prncpal Component Analyss, Second Edton. Sprnger Seres n Statstcs. Sprnger- Verlag New York,(2002). 3415