Comparison of Methods for Horizon Line Detection in Sea Images

Transcription

1 Comparion of Method for Horizon Line Detection in Sea Image Tzvika Libe Evgeny Gerhikov and Samuel Koolapov Department of Electrical Engineering Braude Academic College of Engineering Karmiel 2982 Irael (correponding author) and Abtract Four algorithm deigned to find a horizontal line eparating ea from ky in real-life condition were implemented and compared by their accuracy and relative peed. The algorithm elected were H-COV-LUM baed on regional covariance in luminance image H-HC baed on edge detection and the Hough tranform H-LSC baed on maximal edge detection and the leat quare method and H- MED baed on ian filtering and linear regreion. Reallife image were ued for comparion. The mot accurate line with repect to angular error wa obtained by uing the H-HC algorithm and with repect to the error in the poition of the line by H-COV-LUM wherea the highet peed wa achieved by uing the H-LSC method. Keyword-horizon detection; marine image; edge detection; ian filtering; image analyi I. INTRODUCTION The horizon line i ued for different purpoe uch a navigation in airborne and marine vehicle and military urveillance. A number of horizon line detection method are known [25670]. Some of thee method are baed on edge detection [9] while other employ different technique. Due to the variety of technique a comparion of the detection performance that they can achieve can be very helpful. The goal of thi reearch i to implement and compare the accuracy of a number of well-known and modified horizon-line detection approache. Conidering that in the later tage of thi reearch the elected algorithm i to be implemented on a tand-alone hardware unit algorithm complexity and their relative peed i alo evaluated. The tructure of thi paper i a follow. In the next ection we preent the algorithm dicued in thi work for horizon line detection in marine image. Then in Section III we decribe the method and criteria ued in the comparion of thee algorithm. Section IV i dedicated to horizon detection reult: quantitative and viual and finally Section V preent a ummary of thi work and our concluion. II. ALGORITHMS COMPARED Four algorithm are compared in thi work. The motivation for their choice i comparion of local feature baed algorithm uch a thoe baed on edge with global feature baed method uch a H-COV-LUM that ue regional covariance. The H-MED algorithm extend the meaning of a local feature (edge) at a pixel to it mall neighborhood and then look for the maximal edge in the vertical direction. Thu it introduce a compromie between local and global feature. No algorithm that require a training tage uch a neural network and upport vector machine were choen for the comparion but only imple low complexity method were taken. The compared method are: H-COV-LUM an algorithm that ue regional covariance a introduced in [5] but calculated on luminance image. Although there are cae where the color information i important [8] in thi cae uing achromatic image data only improve the algorithm peed ignificantly with minimal lo in accuracy. H-HC uing pre-proceing Canny edge detector [3] and Hough tranform [4]. H-LSC uing pre-proceing edge detection and calibration by the leat-quare method approach. H-MED eeking for the maximal edge in the vertical direction followed by ian filtration in order to reect outlying point and linear regreion. The algorithm are decribed in more detail in the next ubection. A. Regional covariance baed algorithm (H-COV-LUM) An algorithm for horizon detection for remotely piloted Micro Air Vehicle wa introduced in [5]. The algorithm receive an image taken from the air a input and earche for an optimal partition of the image into two region: ky and ground (or ky and ea) uing a line which i the detected horizon. The optimization criterion i baed on the determinant and the trace of the covariance matrice of the two region. More pecifically if we denote a ky pixel by [ G B ] T x R where Ri G Bi are the primary red green and blue value at the pixel (i) and we x [ R G B ] g g g g T denote a ground pixel by i then the covariance matrice of the region are given by T xi x E i E x and T g g x x E i g g g g g E x. E() denote here tatitical mean. The optimization criterion conidered for all the poible horizon line orientation and poition and maximized i given by [5]: Copyright (c) IARIA 202. ISBN:

2 J 2 2 det( ) det( g ) trace ( ) trace ( g ) () where det() denote the determinant and trace() denote the g trace of the covariance matrice and. We conider a imilar criterion to the one in () for the luminance image thu the optimization term J become J (2) 2 2 var( Y ) var( Y g ) var ( Y ) var ( Y g ) g where var() tand for variance and Y Y are the luminance value of the ky and ground region repectively. A implified optimization criteria J (3) g var( Y ) var( Y ) can be ued intead of the one in (2) with imilar reult. Alo defining a region of interet (ROI) in the image and earching the horizon line only in thi area peed up the algorithm ignificantly. Alternatively the input image can be down-ampled prior to the application of the algorithm to reduce it runtime but thi will decreae the accuracy a well. B. Edge detection and Hough tranform baed algorithm (H-HC) The tage of thi method can be ummarized a follow:. Pre-proce the image uing morphological eroion to reduce the probability of the detection of weak edge in the later tage. A mall circular tructuring element can be ued here. Alternatively the image can be moothed uing a low pa filter but we found eroion to provide better performance. 2. Apply Canny [3] edge detector to the pre-proceed image. 3. Apply the Hough tranform [4] to the edge map. 4. Chooe the horizon line to be the longet line found in the previou tep. C. Edge detection and leat quare calibration baed algorithm (H-LSC) Thi algorithm i baed on edge detection a well but ue a imple algorithm to detect the maximal vertical edge in each column of the image. It tage are decribed below.. Pre-proce the image uing morphological eroion. 2. Find the maximal vertical edge in each column of the image. The implet way to meaure the edge trength i uing an approximation of the vertical Y Yi derivative e.g.. Store the (i) coordinate of the maximal edge. 3. Ue the leat quare method to find the optimal line paing through the maximal edge coordinate. Edge with very mall value a well a very big one can be dicarded here for better algorithm reilience to noie. D. Median filtering and linear regreion baed algorithm (H-MED) Thi algorithm employ ian filter in everal tage providing high performance in the preence of noie. The tage of the algorithm are:. Pre-proce the image uing morphological eroion. 2. Find the maximal vertical edge in each column of the image. Here the edge at pixel (i) i meaured a the abolute difference between two ian value of the 5 pixel above and including pixel (i) and the 5 edge where pixel below it i.e. 2 ian Y k ian Y 2 k i i k i 4 5 k i and. The (i) coordinate of the maximal edge are tored. 3. Ue linear regreion to find the optimal line paing through the maximal edge coordinate. 4. An optional tep of ian filtering can be added to remove outlier. Thi tep can be applied to the vertical coordinate of the maximal edge (prior to Step 3) or to the regreion error (following Step 3). We define the regreion error a the error at coordinate (i) of a maximal edge i.e. 0. (4) err i a a (5) where a0 a are the optimal line coefficient found in err Step 3. We define the ian filtered error err i after applying a ian filter. Now the outlier are (i) where err err Th. threhold (e.g. a value of ). III. a Th here i the COMPARISON METHODS AND CRITERIA Next we decribe the image and criteria ued for the comparion of the algorithm in thi work. A. Image ued to compare the algorithm The reult of horizon detection for a group of 9 marine image are preented in thi work. Image input format i true color (24 bit per pixel) non-compreed BMP. Reolution ued vary from 249x69 to 900x675 pixel. Mot image contain a horizon line eparating the ea and the ky clearly ditinguihed by the human eye. However ometime the horizon line i lightly ditorted by camera optic and ea wave or concealed by marine veel. To further challenge the elected algorithm everal image contained cloud or un light effect near the urface of the ea water. Copyright (c) IARIA 202. ISBN:

3 B. Comparion criteria The algorithm were compared with repect to accuracy and peed. The accuracy wa meaured for the detected horizon angle relative to a horizontal line (in degree) a well a the height of the line above the bottom left corner of the image (in pixel). The error provided in the next ection for thee two horizon line parameter are meaured relative to the line height and angle a determined viually. The algorithm peed wa meaured in term of run time (in econd). IV. RESULTS The accuracy comparion for the algorithm decribed above (height and angle deviation) i given in Table in term of the mean error for 9 tet image. In ome of thee image the horizon i not horizontally aligned (e.g. ee Fig. ). A it can be een the angular deviation i very mall on average for the H-LSC and H-HC algorithm. The height deviation i mallet for the H-COV-LUM method baed on regional covariance in luminance image. However the fatet algorithm i H-LSC baed on maximal edge and leat quare optimization a een from the run time comparion in Table 2. The H-COV-LUM and H-MED algorithm are ignificantly lower than H-LSC due to the required computation of regional covariance or local ian in the proce of the horizon detection. Algorithm Mean height deviation Mean angle deviation H-LSC H-COV-LUM H-HC H-MED TABLE. MEAN HEIGHT DEVIATION (PIXELS) AND ANGLE DEVIATION (DEGREES) FOR THE FOUR ALGORITHMS TABLE 2. MEAN RUN TIMES (SECONDS) FOR THE FOUR HORIZON DETECTION ALGORITHMS A. Viual reult Algorithm Mean Time (ec.) H-LSC 0.3 H-COV-LUM 2.8 H-HC 0.4 H-MED 2.5 Viual reult are provided in Fig. 2 and 3. A it can be een all the algorithm provide imilar good reult for the Horizon_ image (Fig. ) although the H-COV-LUM method lightly mie the horizon line. Thi i due to the effect of the cloud and the unlight reflection in the ea water. A imilar effect can be een for the Horizon_5 image (Fig. 2) where the H-COV-LUM method provide a lightly le accurate etimate of the horizon line than the other algorithm that achieve viually imilar performance with good detection of the horizon. H-COV-LUM i thu an efficient algorithm for locating the poition of the center of the horizon line but ometime the line i lightly rotated compared to the optimal one introducing an angular error. The algorithm cope well with image where the ky and the ea are uniform in appearance even when marine veel are preent but it may be confued by cloud un reflection effect (Fig. ) and trong wave (Fig. 2). The olution to thi may be a pre-proceing tage which remove ome of the cloud light reflection effect and wave from the image reulting in more uniform ky and ea area. Thi i currently under reearch. In Fig. 3 both H-COV-LUM and H-HC method provide good etimate of the horizon line while H-LSC i le accurate. A for H-MED it performance i inferior to the other due to a bigger angular error. Thi method i more affected by the cloer hip concealing the horizon line. The reaon for the performance decreae for H-LSC and H- MED i that both detect maximal edge at pixel of the larger marine veel intead of the horizon that i partly hidden. Than the leat quare technique or the linear regreion employed to find the optimal line paing through the maximal edge location produce a line that i hifted downward relative to the optimal horizon line. The olution to thi problem can be calculating the optimal line many time uing partial data and then chooing the one paing through or cloe to the maximal number of edge pixel. Thi i currently under reearch. The H-HC algorithm on the other hand i robut to the hindrance introduced by the ea veel in the image of Fig. 3 due to the ue of the Hough tranform that detect the line paing through the maximal number of pixel in the edge map. Thu even though ome of the hip pixel are detected a edge thi doe not confue the method a long a more pixel are marked a edge on the real horizon line. V. CONCLUSIONS Four different algorithm for horizon detection in marine image were examined in thi work. The technique employed by thee algorithm vary from uing regional covariance of ky and ea region (H-COV) to uing edge detection and Hough tranform (H-HC) uing maximal edge detection and the leat quare method (H-LSC) and uing ian filtering and linear regreion (H-MED). The algorithm were implemented and compared for a group of tet image with repect to accuracy a well a peed. The mot accurate method with repect to the angular error wa found to be H-HC while the other algorithm do not lag far behind. The H-COV algorithm provided the highet accuracy when etimating the height of the horizon line above the bottom left corner of the image. Alo when Copyright (c) IARIA 202. ISBN:

4 comparing the algorithm peed the fatet method wa H- LSC. We conclude that all the algorithm examined in thi work can be ued for horizon detection in till marine image. Moreover the algorithm can be ued alo in image taken by infrared camera which i the ubect of future reearch. ACKNOWLEDGMENT We would like to thank the adminitration of Ort Braude college and the Department of Electrical Engineering for providing the opportunity to conduct thi reearch and the financial mean to preent it at the conference. REFERENCES [] G. Bao Z. Zhou S. Xiong X. Lin and X. Ye Toward micro air vehicle flight autonomy reearch on the method of horizon extraction Proc. IEEE Conf. on Intrumentation and Meaurement Technology 2003 vol. 2 pp [2] G.-Q. Bao S.-S. Xiong and Z.-Y. Zhou Viionbaed horizon extraction for micro air vehicle flight control IEEE Tran. on Intrumentation and Meaurement 54(3) 2005 pp [3] J. Canny A computational approach to edge detection IEEE Tranaction on PAMI 8(6) 986 pp [4] R. O. Duda and P. E. Hart Ue of the Hough Tranformation to Detect Line and Curve in Picture Comm. ACM 5() 972 pp. 5. [5] S. M. Ettinger M. C. Nechyba P. G. Ifu and M. Wazak Viion-guided flight tability and control for micro air vehicle Proc. IEEE Conf. on Intelligent Robot and Sytem Lauanne Switzerland 2002 pp [6] S. Fefilatyev V. Smarodzinava L.O. Hall and D.B. Goldgof Horizon detection uing machine learning technique. Proc. Intern. Conf. on Machine Learning and App pp [7] S. Fefilatyev D.B. Goldgof and L. Langebrake. Toward detection of marine vehicle on horizon from buoy camera. Proc. SPIE 2007 pp. 6736:67360O. [8] E. Gerhikov and M. Porat On color tranform and bit allocation for optimal ubband image compreion Signal Proceing: Image Communication 22() Jan pp. -8. [9] S. Koolapov Robut Algorithm Sequence for Structured Light 3D Scanner Adapted for Human Foot 3D Imaging Journal of Comm. and Computer 8(7) 20 pp [0] K. Nonami F. Kendoul S. Suzuki W. Wang and D. Nakazawa Autonomou flying robot (Springer Tokyo Dordrecht Heidelberg London New York 200). [] Y. Wang Z. Liao H. Guo T. Liu and Y. Yang "An Approach for Horizon Extraction in Ocean Obervation" Proc. IEEE Congre on Image and Signal Proceing 2009 Tianin China pp. -5. Copyright (c) IARIA 202. ISBN:

5 H-COV reult for Horizon_ H-LSC reult for Horizon_ H-MED reult for Horizon_ H-HC reult for Horizon_ Figure. Horizon detection reult for image Horizon_. The (yellow) line mark the detected horizon. Note the cloud and reflected light effect in thi image. Copyright (c) IARIA 202. ISBN:

6 H-COV reult for Horizon_5 H-LSC reult for Horizon_5 H-MED reult for Horizon_5 H-HC reult for Horizon_5 Figure 2. Horizon detection reult for image Horizon_5. The (yellow) line mark the detected horizon. Depite the wave and the hip preent all the algorithm detect the horizon correctly. Copyright (c) IARIA 202. ISBN:

7 H-COV reult for Horizon_6 H-LSC reult for Horizon_6 H-MED reult for Horizon_6 H-HC reult for Horizon_6 Figure 3. Horizon detection reult for image Horizon_6. The (yellow) line mark the detected horizon. Note the wave and the ea veel preent in thi image epecially the cloer one blocking the horizon.. Copyright (c) IARIA 202. ISBN: