Spatially Localized Circular and Overlapped Feature Extraction for Gray Scale Images using Gabor Jets

Transcription

1 Internatonal Journal of Computer Applcatons ( ) Spatall Localzed Crcular and Oerlapped Feature Extracton for Gra Scale Images usng Gabor Jets Sddhalng Urolagn Dept. of Computer Sc. and Engg., Manpal Insttute of Technolog, Manpal-57610, Karnataka Inda. Prema K.V Mod Insttute of Technolog and Scence, Rajastan, Inda. Jaakrshna R Dept. of Computer Sc. and Engg., Manpal Insttute of Technolog, Manpal-57610, Karnataka Inda. ABSTRACT Extractng the effecte features from the mage for object recognton s one of the most mportant problems n computer son. One of the usual methods of feature extracton s through bnarzaton process. Bnarzng the gra scale mage has man dsadantages: tal brghtness nformaton loss, dffcult to set proper threshold. In ths paper feature-extractng method drectl from the gra scale mages usng Gabor jets s dscussed. Rather than naïe use of Gabor jets as features, a spatall localzed crcular and oerlapped method for computng the features of Gabor jets to form feature ectors has been proposed. To demonstrate the effecteness and effcac of feature extracton method, classfcaton of objects under presence of sgnfcant amount dstorton usng smple classfer such as K-nearest neghbor s done. A good rate of recognton s obsered n the expermental results. Kewords: Vson, Feature Extracton, Gra Scale Images, Gabor Jets. 1. INTRODUCTION One of the bggest challenges of object recognton s to effectel extract features, whch are able to dstngush one object from another. One of the tradton methods of feature extracton s through the bnarzaton process. In whch gra scale mage s bnarzed and then feature from t are extracted. But bnarzaton s the trck process and requres doman specfc threshold settng. Also b bnarzng the mage, tal araton n the brghtness of an mage s lost, whch ma be requred to effectel form feature ectors. Feature extracton method drectl from the gra scale mage usng Gabor flters s proposed n ths paper. The Gabor flters hae wdel appled to mage processng and computer son applcatons. In the feld of computer son D Gabor flter appled for edge and lne detecton [][3], texture classfcaton [] [5], and compresson [6] [7], etc. Basc reason for successful applcaton of Gabor flter n these areas s that the possess man propertes such as tunable to specfc orentatons; spectrall localzed, whch allows dfferentaton of lnear patterns occurrng oer a range of spatal scales or channels; spatall localzed, whch allows accurate dentfcaton of lnear patterns wthn a specfc orentaton; t has been shown that the complex Gabor functon achees the lower bound of the uncertant prncple; the D Gabor flter has the form of a complex snusod multpled b a D Gaussan flter; extracts local nformaton from the mage, as opposed to global technques such as the Fourer transform. For the feature extracton, thee Gabor flterng prodes smultaneous nspecton of mage both n the spatal and frequenc domans. The essence of success of Gabor flters n the feld of feature extracton s that the remoe most of the arablt n mages due to araton n lghtng and contrast [8]. At the same tme the are robust aganst small shfts and deformatons. [8]. The Gabor flter representaton n fact ncreases the dmensons of the feature space such that salent features can effectel be dscrmnated n the extended feature space [8]. A stud has been made n [1] on the use of Gabor flterng based feature extracton for the dstorted and nos mage recognton. The normalzed Gabor flter response at a specfc spatal locaton can tself be used as a local feature. In [1] the authors hae proposed the use of global Gabor features, whch s summaton of the response oer whole mage. A new method for feature extracton usng Gabor flters drectl on the gra scale mages s proposed. A set of Gabor flters wth dfferent parameters s consdered. Gen a gra scale mage s conoluted wth set of the Gabor flters to produce Gabor jets. One mght consder formng feature ectors for classfcaton b takng Gabor jets as the are. Howeer, such a naïe use of Gabor jets n recognton s ulnerable to deformaton and araton present n the object tpe. So a spatall localzed crcular and oerlapped method for accumulatng the features of Gabor jets to form feature ectors s proposed. In secton dscusses ntroducton about Gabor flterng, n secton 3 explans the nose tolerance capablt of the Gabor flters. In secton the proposed feature extracton method explaned. Proposed method for computng features n spatall localzed crcular and oerlapped manner s explaned n secton 5. In secton 6 the experment results and followed b concluson n secton 7.. GABOR FILTERING A two-dmensonal Gabor flter s a snusodal plane wae modulated b a Gaussan enelope. It can be defned as { 1[( x ) ( ) ]} e cos(f ) 0 x (1) Where Gaussan Enolope Snusod and x xcos sn cos xsn 0

2 Internatonal Journal of Computer Applcatons ( ) Where (x, ndcates D coordnates n pxels, f 0 s the frequenc of the plane wae, s the antclockwse rotaton of the Gaussan enelope and the plane wae, the sharpness of the Gaussan along the major axs parallel to the wae, and s the sharpness of the Gaussan mnor axs perpendcular to the wae. The Gabor flters can be establshed n the dscrete spatal doman usng and for gen mage the response of the flter r r can be calculated a conoluton as ( f,,, x, ( x, ) () 0 The Gabor flter (1) can be consdered a band pass flter tuned to a specfed D spatal frequenc f 0 wth a bandwdth controlled b the parameters and and on a specfed orentaton. In mage processng, the Gabor flters can be used as spatall localzed frequenc senste feature extractors where the localzaton s realzed b computng the flter response at each spatal pont. 3. NOISE TOLERANCE The Gabor flter can be used for low-leel feature extracton. When the ntended features can be constructed from the responses of the Gabor flters, the Gabor flterng prodes an effecte method due to ts optmalt n terms of mnmal jont uncertant n the spatal and frequenc domans. In addton, frequenc doman features hae been found to be dstnctl more robust than features n the presence of addte nose [9]. Stll also the adantages of spatal processng can be utlzed the Gabor flters. Hang the Gaussan shape n the both domans, the Gabor flters can be consdered as spatall concentrated band pass flters that are localzed n the frequenc and spatal domans. Thus, dsturbances n dstnctl dfferent locaton, ether n the frequenc or spatal domans, do not affect the flter responses. For examples, low frequenc flters are tolerant for hgh frequenc nose, such as the salt and pepper or whte nose. The Gabor flter response s senste to changes n the nput mage, but preferabl a small change n the nput should cause onl a small change n the flter response to prode a stable behaor. The stable behaor of the steerable flters, such as the Gabor flters, can be acheed b relaxng the orthogonalt propert of the selected flters [10]. Snce the Gabor flters can be selected to hae oerlap n the spatal and frequenc domans, prodng an oercomplete representaton, a smooth behaor of the flter responses can be realzed to tolerate small changes of object pose, e.g., translaton, scale, and rotaton. In addton, due to the spatal concentraton the Gabor flters are tolerant to small changes n llumnaton condtons, e.g., araton n llumnaton gradent.. FEATURE EXTRACTION USING GABOR JETS Unlke the usual method of extractng the feature from the mage through mage bnarzaton, a scheme of feature extracton drectl from the gra mage usng Gabor flters s proposed as shown n fg. 1. The use of the Gabor flters contrbutes to freeng a trck bnarzaton process for cluttered mages, and furthermore prodes localzed drectonal edge features, whch hae phase-shft narance to edge postons. A set of Gabor flters s formed wth specfc parameters such as fundamental frequenc, sharpness of the Gaussan along major and mnor axs and dfferent rotaton angles. The gra scale object mage undergoes conoluton wth these Gabor flter to produce the flter s response called as Gabor jets. The feature ector s obtaned b accumulatng the Gabor jets along projecton lnes n crcular and oerlapped local regons. The scheme of extractng spatall localzed features s explaned n followng secton. Thus produced features can be used for object recognton. 5. SPATIALLY LOCALIZED CIRCULAR AND OVERLAPPED FEATURE EXTRACTION In ths secton proposng method for computng features n spatall localzed crcular and oerlapped manner. A set of Gabor flters G each flters s formed b dfferent parameters s emploed. G { f,,, )} (5) Where 0 and 0 3. The spatall localzed feature extracton s carred out as follows 1. Normalze the gen mage sze to 0 x m and n 0.. Carr out the mage conoluton wth the flter f,,, ) to produce the flter s 0 r response ), whch s of same sze as nput mage. f,,, r (6) 0 for

3 Internatonal Journal of Computer Applcatons ( ) Fg1: The schematc dagram of the proposed feature extracton method usng Gabor Jets. 3. Let d be the dson factor, whch ddes entre response r nto d d d number of blocks.. A submage S(x, from each block s formed and addng to t number of neghborng pxels from t s three neghborng blocks as shown n fg. (a). For blocks at the extreme end, number of pxels s taken from the neghborng blocks n a crcular manner as shown fg. (. The sze of thus formed submage S(x, s and n d n. m n where m m d 5. Next, accumulate number of drectonal features (, ) from each submage S b based on the where 0 d b., 0 3 and a. When =0, 0, features are extracted along the horzontal drecton x( k 1) n (, S k b d xk 0 (8) b. When =1,, feature are extracted along the prncple dagonal n xm 0 nk x (, S k b d x0 n 0 n( k 1) x1 (9) ) c. When =,, features are extracted along the ertcal drecton x m ( k 1) (, S k b d x0 k (10) 3 d. When =3,, feature are extracted along the secondar dagonal kb d xm (, S x0 0 ( k1) x1 0 k x (11) Repeat aboe steps for all 0 k, 0 d b, where 0 3. Here ntroducng range defnng operator l f ( ) u on functon f ( ) (1) n n such that, 7. At the end of the feature extracton for each response where 0 3 r a ector V of sze V G d wll result. Fg (a): Sub-mage formaton. Fg (: Sub-mage formaton at extreme mage.

4 Rec. Rate Internatonal Journal of Computer Applcatons ( ) 6. EXPERIMENT AND RESULTS Electrc smbols of [1] for object recognton are used n the experment. These smbols can be downloaded from the address gen n the [1]. There are 5 electrc smbol mages as shown n fg. 3. Fg3: Electrc smbol mages The gra scale mages are normalzed to 6X6 sze. A set of Gabor flters s formed wth,, fundamental frequenc s f 11 and dfferent angles ,5,90,and135. The normalzed mages undergo conoluton wth each of the four flters to produce responses. For each responses thus produced s dded nto 16 blocks. Takng one block at a tme, a submage s formed of sze 0X0 b appendng neghborng pxels from ts neghborng block as explaned n secton 5. From each of the submage features are accumulated along horzontal, prncple dagonal, ertcal or secondar dagonal drecton based on the angles 0,5,90,135 respectel used for constructng the Gabor flter. Thus from the each mage 56 features are accumulated. In order to know the effcac of the feature extracton method dscussed so far, K-nearest neghbor method for classfcaton of the mages s used. K-nearest neghbor s chosen because t s smple and dstance based classfcaton method, so that emphass on and measure performance of feature extracton technque. A base mages set s formed b undstorted electrc smbol mages of fg. 3 along wth smbol mages hang Gaussan nose of mean zero and arance of 0.01, 0.05, 0.09, 0.1, 0.5, 0.9, 1.1, 1.5, 1.9,.1,.5,.9, 3.1, and 3.5. A test mages set s formed b electrc smbol mages hang Gaussan nose of mean zero and arance rangng from 0.0 to 3.6 other than the mages of base mages set. The recognton of test mages s carred out usng K-nearest neghbor based on base mages set. The K- nearest neghbor fnds the K samples of base set hang mnmum dstance between the features of test mage to the features of base set mages. The gen test mage assgned the class label of the base set mage hang the majort among the K samples. The fg. shows the recognton rate when K-nearest neghbor s used wth K=3, 5 and 7 to classf test mages based on the base mages set. The recognton rate for test mages aganst arance arng from 0.0 to 3.6 s shown n the fg. Recognton rate for all most all the cases s 100% and a mnmum of 9% recognton rate s obsered when K=3 at arance 3.6. For hgher alue of K (K=5,K=7) better recognton rate s obtaned than K=3. The fg. 5 shows the recognton rate for each class of the test mages set. There are 5 dfferent classes n the problem space. The fg.5 shows the recognton rate at arous K alues. Except for ammeter and batter, for all the classes recognton of 100% s obsered. For ammeter class recognton rate s 86.95% when K=3,5,7 and for batter class t s 95.65% when K=5,7 s obtaned. 7. CONCLUSION In ths paper new method for feature extracton drectl on gra scale mages s proposed. Usng a set of Gabor flter and conoluted the gen mage to produce Gabor jets. Rather than usng Gabor jets as features, whch are ulnerable to deformaton and aratons, proposed spatall localzed crcular and oerlap method for computng features from Gabor jets. In order to measure performance of and emphass on proposed feature extracton method, K-nearest neghbor for classfcaton s used, whch s smple and dstance based method. The recognton rate for the test mages hang Gaussan nose s obsered to be 100% n all most all the cases. A better recognton rate s obsered for all the classes of the problem space. The proposed feature extracton method for gra scale mages prodes better recognton results under sgnfcant amount of dstorton Recognton Rate Varance. k=3 k=5 k= Fg : Recognton rate for the test mages hang Gaussan nose wth arance from 0.0 to 3.6 3

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