High-Speed Recognition Algorithm Based on BRISK and Saliency Detection for Aerial Images

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1 Research Joural of Applied Scieces, Egieerig ad Techology 5(23): , 2013 ISSN: ; e-issn: Maxwell Scietific Orgaizatio, 2013 Submitted: November 29, 2012 Accepted: Jauary 17, 2013 Published: May 28, 2013 High-Speed Recogitio Algorithm Based o ad Saliecy Detectio for Aerial Images Teg-Jiao Xiao, Da-Pei Zhao, Ju Shi ad Mig Lu Image Processig Ceter, School of Astroautics, Beihag Uiversity, Beijig, Chia Abstract: A fast groud object recogitio method for aerial images, taig airports, oil depots, harbors, etc., as research objects, is proposed i this study based o ad the visual saliecy detectio. Accordig to the characteristics of aerial images, such as high resolutio ad complex bacgroud iterferece, saliecy detectio is applied to select the cadidate object regio where the target may exist. Therefore, it ca reduce the searchig rage effectively. Ad the, matchig method is used to recogize the object efficietly. A variety of experimets uder differet iterferece factors are carried out based o the typical object database of aerial images i this study. Experimetal results show that the proposed algorithm ca ot oly maitai the validity of features uder the coditios of rotatio, scale, illumiatio ad viewpoit chages, but also shorte the matchig time, satisfyig real-time demad. Keywords: Aerial images, matchig, object recogitio, saliecy detectio INTRODUCTION Image matchig techology is oe of the importat factors restrictig the developmet of aerial recoaissace ad precisio-guided system, which plays a very importat role i moder warfare. The aerial images obtaied real-time were matched to the pre-stored target image to achieve the target accurate recogitio ad precise positio. Due to the high resolutio ad large size of the aerial images, the timeliess ad accuracy of the matchig algorithm become very importat performace idicators. At the same time, ot oly the great variatio of illumiatio, scale, rotatio, viewpoit betwee the target image ad the real-time image, but also may iterferece such as cloud shelter, oise, low cotrast, image blur may affect the matchig result. Therefore, looig for a accurate ad fast matchig algorithm becomes very importat i order to meet the eeds for practical applicatio. Over the past decade, for the good robustess of matchig algorithm based o the iterest poit, this id of matchig methods becomes the research focus i matchig field. Oe of the most classical matchig algorithms is SIFT (Scale Ivariat Feature Trasform) (Lowe, 1999), which is based o the Gaussia scale space image pyramid ad ca be ivariat to illumiatio chages ad affie or 3D projectio. I 2004, Lowe improved the algorithm (Lowe, 2004), maig it further robust to affie distortio, 3D viewpoit chage, oise ad illumiatio chages. But the large calculatio ad slow ruig speed restrict its egieerig practical applicatio. Herbert Bay etc., proposed SURF (Speeded Up Robust Features) (Bay et al., 2008) usig the itegral image to accelerate the matchig speed. But compared to SIFT, there exists a big gap of the matchig performace. From 2010, a series of matchig algorithm based o the FAST corer detectio such as BRIEF (Caloder et al., 2010), (Leuteegger et al., 2011) ad the ORB (Rublee et al., 2011) appear. These algorithms use biary strigs to describe the features ad Hammig distace to measure the feature distace, ehacig the speed of the matchig largely. But searchig i the whole realtime image still cosumes much time ad waste a lot of redudat computatio. So far, this id of matchig methods has already peaed for the aspect of speed. To outperform this id of matchig methods i terms of speed is extremely difficult. Recetly, saliecy detectio has attracted a lot of attetio i may fields. It imitates huma visual system to see iterestig regios i images to reduce the search effort i tass such as object detectio ad recogitio. Ispired by the above discussio, a high-speed recogitio algorithm is proposed i this study, which combies ad saliecy detectio. The algorithm further accelerates the speed of ad achieves accuracy ad real-time requiremets. SALIENCY DETECTION AND Saliecy detectio: Visual attetio aalysis which imitates the huma visual system ca detect the saliet area i a image automatically. I object detectio or recogitio tas, just searchig the iterested target i Correspodig Author: Teg-Jiao Xiao, Image Processig Ceter, School of Astroautics, Beihag Uiversity, Beijig, Chia 5469

2 Res. J. App. Sci. Eg. Techol., 5(23): , 2013 where, N is the pixel umber of a image. Give a image, the value of each pixel is ow. Equatio (1) ca be further structured i the way that pixels with the same value are arraged to be together: 255 SalS( I) f 0 I I (3) Fig. 1: Distace map where, f is the frequecy of the same value I appears i the image. The frequecy ca be expressed i the form of histogram. For I [0, 255], the color distace I -I is also bouded i [0, 255]. Sice it is a fixed rage, a distace map ca be costructed before the saliecy map computatio. I the distace map, elemet D (x, y) = I x -I y is the color differece betwee the pixel value I x ad I y, as show i Fig. 1. Give a frequecy histogram ad a color distace map, the saliecy value of pixel I is computed as: (a) Origial images 255 SalS( I ) f D, 0 (4) Fig. 2: Saliecy detectio result the saliet areas, ca greatly reducee the amout of computatio. Saliecy evaluatio ca be classified ito three folds: biologically ispired methods, computatioally orieted models ad methods combied both. Most stimuli-drive attetio aalyses are based o the biological model utilizig cotrast as the ifluetial factor. While the cotrast are based o various types of image features, such as color, edges, gradiets, spatial frequecies, histogram, or combiatios thereof. Through the observatio ad aalysis of real-time aerial images, the color cotrastt of the airport ad oil depots are saliet. Motivated by the spatiotemporal cues saliecy detectio (Zhai ad Shah, 2006), a attetio model based o the color cotrast was proposed i this study. The saliecy value of a pixel I of a image I is defied as: SalS( I ) I I II where, I is i the rage of [0, 255] ad Eq. (1) ca be expad ito the followig form: SalS( I ) I I (b) Saliecy map 1 I I I 2 N (1) I (2) 5470 Thereby, there is o eed to use Eq. (1) to calculate the value of all the pixels, oly eed to calculate the value of the colors {i, = } to geerate the fial saliecy map. The saliecy detectio result of the method is show i Fig. 2. : feature detectio is based o the AGAST (Mairm et al., 2010), which is essetially a extesio for accelerated performace of FAST. For ivariace to scale is a importatt idicator for highquality ey poits, algorithm exteds FAST algorithm to image plae as well as scale-space ad gives the optimal solutio of the cotiuous scale space. The scale-space pyramid layers cosist of octaves c i ad itra-octaves d i, for {i = 0, 1-1} ad = 4 typically. The the FAST 9-16 detector is applied o each octave ad itra-octave with the same threshold T to detect the potetial iterested regios. The poits belogig to the regios should be opoits. maximaa suppressed i scale-space to get the ey For the etire detected maximum, a sub-pixel ad cotiuous scale refiemet are performed. Through the 2D quadratic fuctio fittig ad 1D parabola fittig alog the scale axis, the fial optimal estimate ca be determied. The descriptio of feature plays a importat role i the matchig process, which affects the matchig efficiecy greatly. The samplig patter of is based o the eighborhood of the ey poit, which defies N locatios equally spaced o circles cocetric with the ey poit. descriptor is composed as biary strig by cateatig the results of simple brightess compariso tests betwee the ey poit ad the samplig poits. A characteristic directio of each ey poit is idetified to allow for orietatio- which is a ey to geeral robustess. ormalizatio, so thus to achieve rotatio ivariace

3 Res. J. App. Sci. Eg. Techol., 5(23): , 2013 (a) Biary image (b) Morphological processig (a) Beijig capital iteratioal airport (c) Saliet patch Fig. 3: The result of extractig saliet patch (b) Pudog airport HIGH-SPEED RECOGNITION ALGORITHM I this study, a ovel method combiig saliecy detectio ad matchig for target recogitio is proposed. Firstly, the real-time image is performed by saliecy detectio processig, thus the whole image is cut ito several saliet patches. The, for each patch, is used to recogize the target. (c) Huagdao oil ports Extractig saliet patches: After saliecy detectio, we obtai a saliecy map as show i Fig. 2b. I the saliecy map, if the area is brighter, it will be more saliet. We use simple threshold segmetatio to detect objects i a saliecy map, with the threshold 2/3. Due to the complex bacgroud, the resultig biary image always cotais may small bright spots. Besides, due to the texture of the target, the regio of the target is ot etirely bright, as show i Fig. 3a. For this reaso, we apply a morphological processig. Firstly, expasio processig, through this we ca combie the bright spot together to obtai coected regios. For airport or oil depot usually occupy a large area of the aerial image, they should locate i the largest coected regio. Based o this assumptio, i the biary image, we reduce the o-object patch by removig smaller coected area, thus to further reduce the umber of saliet patch sigificatly. The results of processig are illustrated i Fig. 3b. I each coected regio, the x ad y directios of the maximum ad miimum coordiate are recorded to extract the cadidate object patches i the origial real-time image, amely gettig the cadidate object patches, as preseted i Fig. 3c. Matchig ad recogitio: For each saliet patch, we perform matchig to recogize the object. Matchig two descriptors is a simple computatio doe by their Hammig distace. The umber of bits differet i the two descriptors is the measure of their feature distace. The operatios ca be 5471 Fig. 4: Examples for real-time images of each object performed i a bitwise XOR followed by a bit cout, computig quite efficietly. EXPERIMENTAL RESULTS I order to verify the validity of the proposed algorithm, we use BCIA (Beijig Capital Iteratioal Airport), PA (Pudog Airport), HOD (Huagdao Oil Depots) as objects to validate the high-speed target recogitio experimets. We use the screeshot from Google Earth as real time image, with 10 images for each object. The image size is all about pixels. The target image is captured from Google Earth stochastically. Examples for real-time images of each object are show i Fig. 4. The umber of saliet patches extracted from each real-time image is as show i Table 1. For each saliet patch, we perform to recogize the iterested target. All the process of the proposed algorithm icludes saliecy detectio, extractig saliet patches ad matchig. We add the time of saliecy detectio ad extractig saliet patches together, called detectio ad extractio. We compare the ruig time of the proposed method to the origial. It is worthwhile to ote that maes use of some SSE2 ad SSSE3 commads, which leadig the high-speed of. I

4 Table 1: The umber of saliet patches BCIA PA HOD Res. J. App. Sci. Eg. Techol., 5(23): , 2013 Table 2: Time of compariso with accelerate commads Detectio ad extractio matchig Total time Origial BCIA ms ms ms ms PA ms ms ms ms HOD ms ms ms ms (a) Result of BCIA Table 3: Time of compariso without accelerate commads Detectio ad extractio matchig Total time Origial BCIA 0.318s 9.06s 9.378s s PA 0.322s 8.93s 9.252s s HOD 0.324s 8.97s 9.294s s (b) Result of HOD Fig. 6: Recogitio results of o-object saliet patches (a) Recogitio result of BCIA (b) Recogitio result of PA (c) Recogitio result of HOD Computer produced before 2007 are i such cofiguratio. The time of the compariso with accelerate commads is show i Table 2 ad the oe without accelerate commads is show i Table 3. From the two tables above, we ca see the fuctio of the proposed framewor, especially cooperatig with the relative slow matchig method, the acceleratig effect is distict. Examples of the recogitio results are show i Fig. 5. The images o the left are the saliet patches extracted from the real-time images, while the images o the right are target images. About the o-object saliet patches, o matchig result ca be obtaied, as show i Fig. 6. CONCLUSION This study studied o the groud object recogitio issue of aerial images, taig airports ad oil ports as iterested targets. The experimetal results show that the proposed method ot oly maitais the accuracy of matchig algorithm, but also further accelerates the speed of matchig. Especially cooperatig with some relative slow matchig methods, the advatage of the proposed framewor is obvious. Fig. 5: Examples of the recogitio results this study, we perform two groups of experimets, with accelerate commads ad without accelerate commads, to see the fuctio of saliecy detectio framewor. We perform the first group experimet o the computer with 3.40 GHz CPU, 4.00 GB RAM ad 64-bit operatig system, while the secod group experimet is o the computer with 1.60 GHz CPU, 2.50 GB RAM ad 32-bit operatig system. We arrage the experimets i this way because the SSSE3 commad appears i computer from the year 5472 ACKNOWLEDGMENT This study is supported by a grat from the Natural Sciece Foudatio of Chia (No ) ad Natioal Basic Research Program of Chia (No. 2010CB327900). REFERENCES Bay, H., A. Ess, T. Tuytelaars ad L.V. Gool, SURF: Speeded up robust features. Comput. Visio Image Uderstadig, 110(3):

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