[[abstract]]The performance of image segmentation depends on the output quality of the edge detection process. Typical edge detecting method is based on detecting pixels in an image with high gradient values, and then applies a global threshold value to extract the edge points of the image. By these methods, some detected edge points may not belong to the edge and some thin edge points in dark regions of the image are being eliminated. These eliminated edges may be with important features of the image. This paper proposes a new mathematical morphological edge-detecting algorithm based on the morphological residue transformation derived from dilation operation to detect and preserve the thin edges. Moreover, this work adopts five bipolar oriented edge masks to prune the miss detected edge points. The experimental results show that the proposed algorithm is successfully to preserve the thin edges in the dark regions.[[conferencetype]]國際[[conferencedate]]20041218~20041221[[booktype]]紙本[[conferencelocation]]Rome, Ital

Hsiao, Ying-tung

The performance of image segmentation depends on the output quality of the edge detection process. Typical edge detecting method is based on detecting pixels in an image with high gradient values, and then applies a global threshold value to extract the edge points of the image. By these methods, some detected edge points may not belong to the edge and some thin edge points in dark regions of the image are being eliminated. These eliminated edges may be with important features of the image. This paper proposes a new mathematical morphological edge-detecting algorithm based on the morphological residue transformation derived from dilation operation to detect and preserve the thin edges. Moreover, this work adopts five bipolar oriented edge masks to prune the miss detected edge points. The experimental results show that the proposed algorithm is successfully to preserve the thin edges in the dark regions.國際20041218~20041221紙本Rome, Ital

Tamkang University Institutional Repository

A Mathematical MorphologicaI Method to Thin Edge Detection in Dark Region Ying-Tung Hsiao*, Cheng-Long Chuani, Shwu-Huey Yen', and Hwei-Jen Lin' 'Department of Etectrical Engineering #Department of Computer Science and Information Engineering Tamkaug University, Taipei, Taiwan, 25 1, R.O.C. Tel: +886-2-26215656 Ext. 2786, Fax: +886-2-26209814 E-mail: clchmg@ee.tku.edu.tw A&strac+The performance of image segmentation depends on the output quality of the edge detection process. Typical edge detecting method is based on detecting pixels in an image with high gradient values, and then applies a global threshotd value to extract the edge points of the image. By these methods, some detected edge points may not belong to the edge and some thin edge points in dark regions of the image are being eliminated. These eliminated edges may be with important features of the image. This paper proposes a new mathematical morphological edgdetecting algorithm based on the morphological residue transformation derived from dilation operation to detect and preserve the thin edges. Moreover, this work adopts five bipolar oriented edge masks to prune the miss detected edge points. The experimental results show that the proposed algorithm is suc- cessfully to preserve the thin edges in the dark regions. fnduF Terms~Mathematical morphology, edge detection, and threshold. I. INTRODUCTION DGE detection is the fundamental process in image seg- E mentation. In the past two decades, there are several methods have been proposed for detecting the edges of the considering images. For examples, Sobel detector used a local gradient operator that is only able to detect edges having certain orientations and is performed very poorly when the edges were blurred and noisy [l]. Prewitt operator tries to fit a least-squares-mor quadratic surface over a 3x3 image window and differentiate the fitted surface [2]. Most of these edge de- tectors are based on the main characteristic of an edge that is to fmd pixels with the abrupt intensity changes bin gray level. Conventionally, a thresholding process is applied as post-processing of edge detection to obtain the points of the edge. A global threshold value is utilized to detect the edge of the image after the entire image is transformed into binary code. For an image with apparent edges, the threshold method per- forms well in extracting edge. But, some points with low gra- dient values near the dark region are usually removed by the threshold method. In some applications, such as computer vision, these points with slight differences on gradation in an 0-7803-8689-2/04/$20.00 02004 IEEE image may be important features for further analyzing. There- fore, it is necessaq to develop an algorithm that is able to distinguish small changes both in lightless and dark region, and preserve these thin edges as well as apparent edges under the same final post-processing threshold value. This paper proposes a novel mathematical morphology based algorithm for thin edge detection in dark region. This algorithm detects the pixels with changing gradation by mor- phological residue method. This morphological residue method is able to sketch out the all pixels with changing levels by sub- tracting the original image from its erosion or dilation results. Then the image is decomposed into several sub-images, and different edge enhancement method will be applied on different sub-image. Some unwanted noise pixel might be also enhanced. For that, we defined five bipolar oriented edge masks to eliminate the noise pixels. Because the thin edges are being enhanced by the proposed method, it is onIy need to apply a global threshold to transfonn the image into binary image for edge detecting. This paper is organized as follows: Section I1 presents the proposed algorithm for thin edge enhancement and the bipolar oriented edge masks for noise pixel pruning strategy. Section 111 presents comparison results between the proposed algorithm with other edge detection methods. The conclusions are dis- cussed in the last section. 1 I . m  PROPOSED ALGORITHM A. General Description The major objective of the proposed algorithm is to enhance and preserve the thin edges, as well as detect them. The pro- posed algorithm can work well for the apparent edges in bright regions. The image is first processed by the morphological contrast enhancement operation for increasing the gradient values of the pixel, thus the edge detector can detect the edge much easier. Then the dilation residue edge detector is applied to the enhanced image. It produces an image with various grey-level strength detected edges. In order to determine the characteristics of each part of the image, we decompose the image into several sub-images, and calculate the mean value and standard deviation of each sub-image. There are four de- 31 0 Authorized licensed use limited to: Tamkang University. Downloaded on March 24,2010 at 02:11:22 EDT from IEEE Xplore.  Restrictions apply. fined cases that used to enhance the edge of the sub-images according to their mean and standard deviation. Then apply the threshold image into the edge image. After that, there are five bipolar oriented edge masks utilized to prune the miss-detected pixels and reduce the noise of the fmal result image. The pro- posed solution algorithm is illustrated in Fig. 1. B. Formal Description As illustrated in Fig. 1, there are five steps in the process of the proposed algorithm, Let F denotes the original image, and B denote the structuring element. The detailed information of these steps is discussed step by step as the following. 1 lnputlmage I 1 Morphological Confra$t Enhancement Process Morphological Dilation Residue Edge Detector Decomposition Edge Enhancement Process Thresholding Process Noise Filter by Edge Masks 1 1 1 1 1 Image Out Fig. 1. Process flow chart of the proposed algorithm. Step I :  Morphological Contrast Enhancement Process. In order to increase the contrast level of the original image F, the mathematical contrast enhancement process is utilized to F to increase the grey-level difference between dark objects and bright objects. Let B denotes a 3x3-structuring element shows in Fig. 2. F, denote contrast enhanced image can be formulated as follow: F, = F + GYTH(F) - BTH(F) (10) The white top-hat and black top-hat transformations ate per- formed to obtain the contrast enhanced image F,. Tbe contrast enhancement process is to accentuate or sharpen the image features to make the image more useful for visualization for human eyes, or easier analysis by morphological residue edge detectors. Step 2: Morphological Residue Edge Detection Process. Morphological dilation residue edge detector is applied to the image F, to extract the edge information. Fe denoted edge detected image can be formulated as follow: The resulted image Fe contains grey-level edges with various edge strengths. Although the original image F has been contrast enhanced for the most of apparent edges, which can be easily detected, some thin or smooth edges are easily be eliminated by applying the global threshold value in the whole image. In order to enhance thin or smooth edges, a recursive quad de- composition edge enhancement process is applied to detect the edge image. F, ="-F, (1 1) Fig. 2. Structuring element for contrast enhancement and dilation residue edge detection Step 3: Quad Decomposition Edge Enhancement Process. Quad decomposition edge enhancement process is applied to the image Fe to sharp thin or smooth edges. In this process, the image Fe is decomposed into four sub-images with same size, and each sub-image decomposed into four sub-images. The process is repeated until the user defined smallest sub-image sue  N, is reached. This process is shown in Fig. 3. t I I Fig 3. IIfustration of quad decomposition of an image. In this process, we defined four cases for enhancing the thin or smooth edges in a sub-image. Mean and standard deviation in each sub-image are calculated for determining which case should be applied to the sub-image. M N  (I 31 where M and N are width and height of the sub-image, respec- tively. By these mean and standard deviation values of each sub-image, we defmed four edge point enhancement cases as follows: Case 1: Redundunt Background If the pixel is that its grey-level value Pg is less than the mean value p of its belonging sub-image and the sub-image with p 2  and 6<2, then the pixel is considered to be a background pixel and will be set as grey-level 0 (black). Cuse 2: Thin Edga Enhancement If the pixeI is that its grey-level value Pv is greater than mean value ,u of its belonging sub-image and less than p+l.SxS as well as the sub-image with p<2 and 6 ~ 2 ,  then the pixel is con- sidered to be a thin edge point and its grey-level value will be enlarged to the square of Pii (i.e. Pvx Pv). Case 3: Intensive Thin Edges Enhancement If the pixel is that its grey-level value Pq is greater than p+l.Sxd and less than pi-3xd as we11 as its belonging 31 1 Authorized licensed use limited to: Tamkang University. Downloaded on March 24,2010 at 02:11:22 EDT from IEEE Xplore.  Restrictions apply. sub-image with p<2 and S<2, then the pixel is considered to be a intensive thin edge point and its grey-level value will be enlarged to cube ofPv (i.e. Pux P t x  PU). Case 4: Thin Edges in Complex Background Enhancement If the pixel is that its grey-level value Pv is greater than p+ 1 S X d  and its belonging sub-images that p 5  and &6, then the pixel is considered to be a thin edge point in a complex background and its grey-level value will be enlarged to square Let Fq denotes the image combined from all processed sub-image. All thin edges are enhanced apparently after all sub-images completed this processing step. Similarly, some pixels, which do not really belong to any edge, are been en- hanced as well. This phenomenon may cause noise points dis- tributing overthe image. To solve this problem, a noise pruning process consisting five bipolar oriented edge masks is applied to the image Fp for reducing noise points. Step 4: firesholding Process. Before we apply the edge masks to the image Fg for removing the noise points that cre- ated by Step. 3, we have to threshold entire image with a global threshold value to reduce the computational complexity for noise removing process in Step 5. The properly threshold value applying to the image can be obtained by experiment. A greater threshold value can extract more thin edges, but also lead to extract more noise points that do not really belong to any edge. A smaller threshold value is still able to extract the enhanced thin edges without producing a huge amount of noise, but it may eliminate some of really thin edges. In this paper, we suggest to apply P threshold value ranging from 20 to 30 for producing the image Ft with the thin edges clearly and without causing too many noise points. Step 5: Noise Removing Process. In this step, five bipolar oriented edge masks shown in Fig. 4 are applied to the image F,. The task of this process is to remove unwanted noise pixels, therefore, the mask only applied to the pixel that satisfies the following terms: of Pij ( P p  PV). 1) The pixel is thresholded to be an edge point, 2) The sub-image contains the pixel and its mean and stan- dard deviation obtained in Step 4 are p<3 and 6<3, respectively. This step can indicate the sub-image belongs to background or dark region of the original image F. We can obtain the score of each scanned pixel by the sum- mation of multiplying the mask elements by the corresponding covered pixels in image Ft. For positive scoring pixel, it is considered to be an edge of the image that can preserve its value. In the other hand, negative scoring pixel is considered to be a noise point, and will be shaded away. By applying these edge masks to the image Ft, it is easy to remove the noise points created in background or dark regions. --2 -2 -2 -2 -2 -2 -2- -2 -2 I 2 1 -2 -2---1 1 2 I -2 -2 -2- -2 -2 -2 -2 -2 -2 -2 -2 -2 1 2 1 -2 -2 -1 1 2 I -I -2 -2 1 1 1 1 1 1 1 - 2 - 2 1  2 1 - 2 - 2  -1 1 2  1 - 1 - 2 - 2  2 2 2 -2 2 2 2 -2 -2 I -2 I -2 -2 -2 -1 1 -2 1 -I -2 I 1 I I 1 1 1 - 2 - 2 1  2 1 - 2 - 2  - 2 - 2 - 1  1 2  1 - 1  -2 -2 -2 -2 -2 -2 -2 -2 -2 1 2 I -2 -2 -2 -2 -I 1 2 1 -1 -2 -2 -2 -2 -2 -2 -2 -2 -2 1 2 I -2 -2 -2 -2 -2 I 2 I -1 IV. THE PROPOSED ALGORKHM In this section, the proposed mathematical morphological edge detection algorithm is compared with other famous methods for edge detection. A famous natural scene, “Camera 2 -2 -2 -2 -2- r-2 I I 2 I -2 -2 1 2  1 - 1 - 2 - 2 - 2  1 I 1  2 1 - 2 - 2  -1 1 2  1 - 1 - 2 - 2  I I 1  2 1 - 2 - 2  -2 -1 I -2 I -I -2 2 2 2 -2 1 -2 -2 - 2 - 2 - 1  1 2  1 - 1  1 1  I I 1  - 2 - 2  -2 -2 -2 - I  1 2 1 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 -1 1 2 -2 -2 -2 -2 -2 -2 -2 Man” is shown in Fig. 5 that was widely used to compare the performances of image processing algorithms. As shown in Fig. 5, the cameraman wears a black coat. The coat has many features on it that is important for analyzing the picture. But it is too dark to see any details in shading part of the coat by human eyes. If we increase the bright level of the pic- ture, shown in Fig. 6, it shows clearly the edge features of hand and pockets in dark region of the coat. But these edge features are still very hard to detect by most of the existing methods for edge detection. For comparison purpose, classical Sobel edge detector [12], mathematical morphological residue edge detector [ 131, DOE edge detector [14], and Lum prefilter [lS] are applied on this image. Their results are shown as in Fig. 7, 8, 9, and 10, re- spectively. All of these methods can only detect the contour of the cameraman, but fail to detect sketch of the tower and edge features on the coat. The result of the proposed mathematical morphological edge detection algorithm is shown in Fig. 11. As in Fig. 11, the algorithm successfully extracts and preserves the edge features in dark regions on the coat. V. CONCLUSION A new mathematical morphology edge detection algorithm has been proposed, The proposed algorithm is able to detect the features in the dark regions, and preserve them as well as other apparent edges. The mathematical morphological contrast enhancement process is applied to enhance the image contrast for processing. The mathematical morphological dilation residue edge detector is used in this algorithm for detecting the edge information, which can produce various strength of edge in the mage. Then we use a quad decomposition scheme to decompose the image into several sub-images, and used a set of methods to enhance the thin edge that existing in the sub-image. Finally, we use a set of bipolar oriented edge masks to remove the noise pixels. Comprehensive comparison experiments have been cartied out on natural scene image named “Camera Man”. 31 2 Authorized licensed use limited to: Tamkang University. Downloaded on March 24,2010 at 02:11:22 EDT from IEEE Xplore.  Restrictions apply. / L  Flg. 5 .  Picture of “Camera Man.” Fig. 6. Picture of enhanced “Camera Man.” Fig. 7. Processed result by Sobel Fig. 8. Processed result by Morphological residue. Fig. 9. Processed result by DOE. Fig. 10. Processed result by Lum pater. Fig. 11. Process rssult by proposed algorithm Experimental results show that the proposed algorithm out- performs other edge detection methods on detecting detail edge features and thin edge features in dark regions, and these thin edges can be preserved as the same as other apparent edges. The proposed algorithm is suitable for applying to many ap- plications, such as detection object in a lightless place, or edge detection without lighting conditions. r31 [41 [51 [41 [71 11’1 REFERENCES [l J [2] W. IC. Pratt, Digital Image Processing, New Tork Wiley Interscience, 1978. V. S. Ndwa, A Guided Tuw ofcomputer Vision, AT&T Bell Labomb ries, 1993. R C. Gonzalez and P. Wink, Digital Image Processing, Addison-Wesley, England, 1992. J. S m ,  Image Analysis and Maihemdcal Morphology, New York Academic. 1982. C. H. Henry Chu and E. J. 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Rainam, “Automatic Edge Detation in Noisy Fringe Patterns,” Proceedings of Internulion 5)wposium on SignOr Processing and its h p  plicutiuns, Vol. I, pp. 140-143, Aug. 2001. S. Shah and P. S. Ssrtry, ”Fingeqnint Classification Using a Feed- Cybernetics, Parl B, Vol. 34, No. 1, pp. 85-94, Feb. 2004. J. E. Abdou, “Qumtitative methods of edge detection,” USCReporf, 830, July, 1978. Soille, Morphological image unatj.sb: principks and applications, Springer, 1999. J. Yoo, CA.  Boumaq E.J. De$, E.I. Coyle, ‘The nonlinear prefltering and difference of estimates approaches to edge detection,” CVGP: Graph. Models Image Process, pp. 140-159, 1993. R.C. Hardie, C. Boncelet, “LUM Nters: a class of rank-order-based 6lters for smoothing and sharpening,” LEEE Trumucfions on Sign01 Procasing, back-€?& Line Detector,” IEEE Trmsactiom on Systems, Mm und vol. 41, NO. 3, p ~ .  1061-1076,Mad1 993. 31 3 Authorized licensed use limited to: Tamkang University. 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A mathematical morphological method to thin edge detection in dark region

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A mathematical morphological method to thin edge detection in dark region

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