EDGE DETECTION USING MULTISPECTRAL THRESHOLDING

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1 ISSN: (ONLINE) DOI: 0.97/jvp ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 06, VOLUME: 06, ISSUE: 04 EDGE DETECTION USING MULTISPECTRAL THRESHOLDING K.P. Svagam, S.K. Jayanth, S. Aranganayag 3 and A. Geetha 4,3,4 Department of Computer Scence, J.K.K. Nataraja College of Arts & Scence, Inda E-mal: kpsvarav@gmal.com, 3 arangbas@gmal.com, 4 geeta.alagarsamy@gmal.com Department of Computer Scence, Vellalar College for Women, Inda E-mal: jayanthskp@gmal.com Abstract Edge detecton s a fundamental tool n mage processng and computer vson, partcularly n the areas of feature detecton and extracton. Among varous edge detecton methods, method s one of the best optmal thresholdng methods for general real world mages wth regard to unformty and shape measures. In ths paper, a multspectral thresholdng algorthm usng method s proposed to detect the edges n multspectral mages. Natural, art and smulated mages are consdered for testng. Snce the edges are well known n the smulated mages, they are consdered for performance evaluaton. The results of proposed method, Edge Detecton usng MultSpectral Thresholdng (EDMST), are compared aganst the results of Canny,, Medan based and Gray Image edge detecton algorthms based on the human vsual system, the number of edges and the number of pxels. The expermental results show that the proposed method acheves better performance and hence appled on Satellte mages. Keywords: Edge Detecton, Multspectral Thresholdng, Method, Satellte Images, EDMST. INTRODUCTION Edge pxels are pxels at whch the ntensty of an mage functon changes abruptly, and the edges are sets of connected edge pxels. Edge detectors are local mage processng methods desgned to detect edge pxels []. Edge detecton s one of the most commonly used operatons, and there are probably more algorthms n the lterature for enhancng and detectng edges due to the fact that the edges form the outlne of an object, whch s subject of nterest n mage analyss and vson systems. An edge s the boundary between an object and the background, and also between overlappng objects. Hence f the edges n an mage can be dentfed accurately, all the objects can be located and basc propertes such as area, permeter, and shape can also be measured. Snce computer vson nvolves the dentfcaton and classfcaton of objects n an mage, edge detecton s an essental tool []. From an nformaton theory perspectve, even though as edge pxels typcally consttute less than 5% of the total pxels, they are very rch n nformaton. In ths context, edge detecton can be vewed as an nformaton flter that greatly reduces the number of pxels that have to be consdered wth lttle mpact on the nformaton content [3]. Many of researchers have keenly studed the performance of dfferent edge detecton technques and analysed them. Thresholdng s one of the smplest and most commonly used technques to separate the foreground from ts background. Among many threshold selecton methods, method s optmum n the sense that t maxmzes the between-class varance, a well known measure used n statstcal dscrmnant analyss. The basc dea s that well-threshold classes should be dstnct wth respect to the ntensty values of ther pxels and, conversely, that a threshold gvng the best separaton between classes n terms of ther ntensty values would be the best (optmum) threshold. In addton to ts optmalty, method has the mportant property that t s based entrely on computatons performed on the hstogram of an mage, an easly obtanable -D array []. Several successful thresholdng methods based on hstogram technques have been proposed. In all the cases, the nput mage must be n gray scale. The satellte mages have multple bands and each one have specfc nformaton. When the mult spectral mages are converted nto gray scale, some nformaton may be lost. To avod the loss of nformaton, a novel algorthm EDMST s proposed based on the concept of multspectral thresholdng usng method. Ths algorthm s tested wth natural, art and smulated mages. For performance evaluaton, the smulated mages are used due to the fact that the edges are well-known n advance. Expermental results show that the proposed algorthm s effcent and generate more number of edges. The rest of the paper s organzed as follows: secton deals wth the related work on edge detecton. Secton 3 descrbes the methodology. Results and the expermental analyss are presented n secton 4. Fnally, secton 5 concludes the paper.. RELATED WORK Contours of mages or, edges provde valuable nformaton towards human mage understandng. Edge detecton process s the most mportant mage processng step n human vsual system. Naturally, t has become a serous challenge to the mage processng scentsts, and snce the last two decades, n partcular, numerous methodologes have been proposed for edge detecton [4]. It s unversally acknowledged that the method, proposed n 979, s the best method of choosng threshold value automatcally. Its basc prncple s to splt the pxels of the gray mage nto two classes, and confrms the best threshold value through the maxmum varance between the two classes [5]. In based segmentaton for Thermal mage, the RGB mage has been converted nto gray scale by usng the Eq.(), R G B R' G' B' 0.333R 0.333G 0.333B () 3 and t has been converted nto thresholdng mage by method [6]. The Canny detector (Canny [986]) s the most powerful edge detector, n whch the local gradent and edge drecton are computed at each pont n the smooth Gaussan fltered mage. 67

2 K P SIVAGAMI et al.: EDGE DETECTION USING MULTISPECTRAL THRESHOLDING Locally maxmum strengthen pont n the drecton of the gradent s consdered as edge pont and are lnked by double thresholdng and connectvty analyss, set by the user. method has been appled to choose the threshold value, k*, automatcally by consderng the standard devaton, σ =, Th = k*, and Th = 0.5 * Th as three parameters to obtan the contour [5]. method has been appled on the basc global thresholdng mage n an mproved medcal mage segmentaton algorthm. The computatonal tme and separablty factors were calculated on medcal MRI and CT-Scan mages [7]. In the orgnal algorthm, n denote the number of pxels wth ntensty n L dstnct ntensty level {0,,,...,L-} of M N pxel sze dgtal mage. The normalzed hstogram components p has been calculated as, p n MN where, MN = n 0 + n + n n L-, p 0 () L 0 p Select the threshold value, T(k) = k, 0 < k < L-, and use t to splt the mage nto two classes, C and C, wth ntensty values n the range [0,k] and [k+, L-]. The between-class varance B of C and C has been calculated based on the cumulatve sums, the mean ntensty values and the global mean. But n mproved medan based mage thresholdng, medan gray level values are used nstead of mean values to calculate the between-class varance [8]. Shannon entropy and Tsalls entropy has been used as global and local threshold values to segment the mage and apply the 3 3 mask to detect the edges [9]. Fuzzy relatve pxel value algorthm has also been used to detect an edge from the satellte mage, n whch a set of fuzzy condtons were used to hghlght all the edges that are assocated wth an mage and tested the relatve values of pxels whch s present on an edge [0]. The ultrasound mage edges have been detected n [] by usng the combnaton of Blateral Flter, Threshold and Gabor Flter. In general, the qualty of ultrasound mages has been corrupted due to the exstence of speckle nose. After ths nose has been suppressed by applyng blateral flter, threshold s used to segment the mage and the edges have been detected by Gabor flter. Conventonal edge detecton methods have also been used to detect the edges on the mage before and after segmentaton and the results were compared. Usng the concept of Genetc algorthm, the edge detecton method wth optmal threshold value, over the gray scale mage, has been proposed n []. The nput mage has been dvded nto m segments based on multlevel threshold. The ftness functon, whch s the rato between the class varance and total class varance of gray levels for the whole mage, has been calculated. The Genetc algorthm has been ntroduced to maxmze the ftness functon for optmal threshold value. Based on between class varance the Synthetc Aperture Radar (SAR) mages have been segmented n [3]. method and the technques that related to t, such as valley emphass technque, neghborhood valley emphass technque, varance and ntensty contrast technque and varance dscrepancy technque have been compared and dentfed that for smallest regon unformty, s the best method. In [4], the optmal number of level for dfferent thresholds has been automatcally dentfed. The author has constraned the searchng space n the valleys of the hstogram nstead of entre hstogram of the mage. The valleys are selected by removng monotoncally ncreasng area, monotoncally decreasng area, local peaks and nvarant area, from the hstogram. For each valley, the neghborhood gray value, cumulatve sums, cumulatve means, global means and the correspondng betweenclass varances have been calculated and from those values, the optmal number of levels and ther correspondng thresholds has been obtaned. Based on these results the gray mages have been segmented and the edges are dentfed. The characters n the number plate, present n any mage have been recognzed n [5]. An adaptve threshold based global bnarzaton and locally appled bnarzaton was combned to extract the number plate from the gray scale mage. Then the characters have been segmented by projecton profle technque and were recognzed usng Support Vector Machne. From the study, t has been observed that most of the edge detecton algorthms convert the color mage nto gray scale mage before processng, whch may lead to nformaton loss. Moreover, remote sensng or meteorologcal satellte mages may have more spectral band, whch cannot be converted nto gray scale. Hence the EDMST method has been proposed to overcome ths problem. 3. METHODOLOGY A multspectral mage s a collecton of several monochrome mages of the same scene, each of them taken wth a dfferent sensor. Each monochrome mage s referred as a band. In mage processng, multspectral mages are most commonly used n Remote Sensng applcatons. Satelltes usually take several mages from frequency bands n the vsual and non-vsual range. All the standard sngle band edge detecton operators can also be appled to multspectral mages by processng each band separately. The smplest way to fnd the edges n a multspectral mage s to obtan a threshold ndependently n each band, detect the edges and combne them to form a sngle contour mage. 3.. EDMST ALGORITHM Step : For each spectral band, a. Smooth the mage wth a Gaussan flter. b. Compute the normalzed hstogram of the mage usng Eq.(). c. The cumulatve sums, P (k), P (k), the cumulatve means, m (k), m (k), the global mean, m G and the between-class varance, B k, has been calculated as follows, for k = 0,,, L-. k 0 p ( k) p (3) L p ( k) p (4) k 68

3 ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 06, VOLUME: 06, ISSUE: 04 and k m ( k) p (5) P( k) 0 L m ( k) p (6) P( k) m G L 0 k p (7) B( k) P k m k mg P k m k mg (8) d. Obtan the threshold, k* by averagng the values of k correspondng to the varous maxma detected n B k. Step : Usng the thresholds obtaned n step, segment the regon n each spectral band and detect the edges. Step 3: The detected edges are combned by mage addton. Step 4: The edges are made thn by applyng the morphologcal operaton. (c) Canny (d) Gray (e) (f) Medan Fg.. Output of Natural Image 4. RESULTS AND DISCUSSION The EDMST algorthm for color mage has been mplemented and tested wth numerous natural, art and smulated mages. A smulated mage s useful because the number of edges s well known n advance and can therefore be used to evaluate quanttatve performance. Thus to valdate the performance of the proposed algorthm, a sample of twenty smulated mages of sze are consdered. The orgnal mages and ther correspondng edges of proposed EDMST and other methods are shown n Fg., Fg. and Fg THE PERFORMANCE EVALUATION (a) Orgnal (b) EDMST To evaluate the performance of the proposed edge detector, three approaches has been used. One s the human vsual system because ths s the most general purpose vson system. From Fg., Fg. and Fg.3, t can be easly seen that the proposed method detect more edges than the other methods. Other performance measures are based on number of edges and number of pxels. The samples used n ths work are smulated one. Snce the edges are well known n advance, total number of edges present n each mage has been calculated manually. The results of contours whch has been detected usng the proposed EDMST method, Canny [5], Gray wth [6], [7] and Medan based [8] are compared wth the actual edges computed. The orgnal edges and the edges detected n the proposed and other methods are lsted n the Table.. (c) Canny (d) Gray (e) (f) Medan Fg.. Output of Art Image (a) Orgnal (b) EDMST (a) Orgnal (b) EDMST 69

4 K P SIVAGAMI et al.: EDGE DETECTION USING MULTISPECTRAL THRESHOLDING ffteen mages. There s only less than 0.05% dfference n the number of pxels obtaned n the remanng fve mages. (c) Canny (e) (d) Gray (f) Medan Input Table.. Number of Pxels Detected EDMST Canny Gray wth Medan based Image Image Image Image Image Image Image Image Image mage Fg.3. Output of Smulated Image From the Table., t s observed that out of twenty mages the proposed method detect maxmum edges n sxteen mages (80 %). In the samples, all the edges are detected n Image5 (95 edges), Image9 (40 edges) and Image4 (6 edges), and n total 85.5% edges are detected n the proposed method. Input Table.. Number of Edges Detected Orgnal EDMST Canny Gray wth Medan based Image Image Image Image Image Image Image Image Image Image Image Image Image mage Image Image Image Image Image Image Image Image Image Image Image Image Image Total The total number of edges and the total number of pxels present n the samples and ther percentage (%) have been tabulated n Table.3 and the correspondng graphcal representatons are gven n Fg 4 and n Fg 5. Table.3. Detected Edges and Pxels n Percentage Methods No. of Edges out of 33 edges Edges obtaned No. of Edges n % No. of Pxels out of 6,00,000 pxels Pxels obtaned No. of Pxels n % EDMST Canny Gray wth Medan based Image Image Image Total Table. shows that out of twenty sample mages the proposed algorthm detect more number of pxels whch are true postve n 70

5 % of Pxels % of Edges ISSN: (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, MAY 06, VOLUME: 06, ISSUE: No. of Edges n % 4.. SATELLITE IMAGES From the results, t can be easly seen that the proposed EDMST has stronger edge detecton capablty and dentfy more complete edges. Hence the proposed EDMST s appled for Google Map mages. The Fg.6 and Fg.7 shows the result obtaned EDMST Canny Gray wth Algorthms Medan based Fg.4. Graphcal representaton of detected edges n % (a) Fg.6. Google Map Image captured on 6, June 04 (a) Orgnal Image (b) Image Obtaned usng EDMST Algorthm (b).4 No. of Pxels n % (a) Orgnal Image 0 EDMST Canny Gray wth Algorthms Medan based Fg.5. Graphcal representaton of detected pxels n % The edges detected by an edge detector can be grouped as correct edges (true edges), whch correspond to edges n the scene, false edges (false postve), whch do not correspond to edges n the scene, and mssng edges (false negatve), that should have been detected n the scene. False edges and the mssng edges are of msclassfcaton errors n edge detecton [6]. Performance of the proposed method has been presented n Table.4. In smulated mages, the proposed algorthm detects nearly 85% of true edges and remanng as mssng edges. The false edges have not been dentfed. Total no. of edges Table.4. Performance of EDMST True edges False negatve edges False postve edges 33 3 (85.5 %) 90 (4.5 %) 0 (b) Image Obtaned usng EDMST Algorthm Fg.7. Google Map Image captured on 4, December CONCLUSION In ths paper an effcent algorthm s proposed to detect the edges for multspectral mages. The method s used to calculate the threshold value. The proposed algorthm s tested wth natural, art and smulated mages, compared wth exstng algorthms and appled on satellte mages. Expermental results prove that the proposed algorthm s effcent and mprove the effect of edge extracton. All the methods are purely based on ntensty values. Due to nose and nterference, the dscontnuty n ntensty leads to false edges. From the study t s dentfed that f the varaton n the ntensty s less, then there s a chance for false edges. In future, the proposed algorthm can be modfed to consder ths ntensty varaton also. 7

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