Journal of Digital Information Management Volume 13 Number 1 February 2015 25

Denoising and Segmentation of Digital Feather Image Using Mean ShiftAlgorithm

Journal of DigitalInformation Management

ABSTRACT: In this study,mean shift algorithm and regionmerging were combined to automatically segment a digitalfeather image and remove the noise in digital images moreeffectively for segmentation of a feather quill and a featherleaf. First, the mean shift algorithm employed to calculatethe convergence value of each pixel can obtain a filteredsmoothened image; then, setting region merging criteriawere used to merge filtered images. Finally, the thresholdsegmentation algorithm was used to extract the featherquill. Experimental results indicated that the mean shiftalgorithm could be used to denoise the digital featherimages, thereby achieving a high value of evaluation indexand improved visual quality.Moreover, this algorithm canaccurately segment texture image and effectively filterout unwanted background texture information to segmenttexture image with unclear color information.

Categories and Subject Descriptors(according to ACM CCS): Computer Graphics [I.3.3]: Picture/Image Generation—Computer Graphics [I.4.9]: Applications—Image Processing and Computer Vision

General Terms: Image Processing, Algorithms

Keywords: Feather Digital Image, Mean Shift,Image Denoising,Image Segmentation

Received: 30 July 2014, Revised 2 September 2014, Accepted 7September 2014

1. Introduction

Image segmentation techniques have been extensivelyused for object detection to ensure product quality andproduction yield in manufacturing[1–3]. Objects on uniformor nontextured surfaces, such as a vessel tissue image[4],can be effectively segmented using thresholding or edge-detection techniques if the object images have distinctpixel values with respect to those of the other parts.

In particular, image segmentation may be considerablydifficult for the images with intensity inhomogeneitiesbecause of the overlaps between the ranges of theintensities in the regions to be segmented. Thus,distinguishing these regions based on pixel intensity isimpossible. Widely used image segmentation algorithmsusually rely on intensity homogeneity[5].Therefore, thealgorithms are not applicable to the images with intensity

Hongwei YUE1, Ken CAI2*, Bing LUO1, Yingying JIN3, Zhaofeng ZENG4

1School of Information Engineering, Wuyi University, Jiangmen, China2School of Information Science and Technology,Zhongkai University of Agriculture and Engineering, Guangzhou, China3School of Mathematics and Computational ScienceWuyi University, Jiangmen, China4Department of Mathematics and Computer ScienceCalifornia State University, East Bay, U.S.Aicken@126.com

26 Journal of Digital Information Management Volume 13 Number 1 February 2015

Inhomogeneities. In general, intensity inhomogeneity hasbeen a challenging difficulty in image segmentation.Thepresent study focuses on the segmentation of a featherquill and a feather leaf in a digital feather image that has aheterogeneously textured surface.

The traditional method uses camber and curvaturemachines to measure camber and curvature of a digitalfeather image to produce badminton products. Thetraditional method has disadvantages, such as manualoperation, high labor intensity, and instability in sortingquality. To solve this problem,machine vision can be usedto complete the extraction of a feather quill and acquirethe measurement of a relevant camber and curvatureparameter.Some studies of the problem have beenconducted[6–8]; the digital feather image online detectionsystem has been put into production line.However, thesemethods that use prior knowledge do not process anyslanting feathers to obtain a better segmentationresult.Analysis shows that the gray value of the slenderfeather quill and feather leaf overlaps to a significantextent;in addition, camber and curvature exacerbate theinfluence of uneven illumination. Therefore, these casesmake segmentation between leaf and quill regionsextremely difficult with traditional image segmentationmethods.The problem is solved through the analysis ofdigital feather image surface texture characteristics. Thispaper introduces the mean shift technology[9]. The featherquill can be extracted by region merging method tosmoothen texture.

The rest of this paper is organized as follows.Mean shiftalgorithm is reviewed and the proposed imagesegmentation scheme for the digital feather image withheterogeneous textures is presented in Section 2.Experimental results are provided in Section 3. Thecontents of the present study are summarized in Section4.

2. Feather Segmentation Using Mean Shift

Mean shift is a nonparametric density-estimation techniquethat finds the modes in multiple-dimensional data usingkernel density estimators. It has been successfully appliedto edge-preservation [10] and object tracking[11]. Themean shift is not limited to digital images, and it can beused in place of other segmentation algorithms[12] andwatersheds.

2.1 BackgroundGiven a set of N data points xi, i = 1, 2, ... N, and xi ∈ Rd, theprobability density of x can be obtained from the kerneldensity estimator:

(1)

(2)

(3)

support satisfying the regularity constraints, and H is thesymmetric positive definite d × d bandwidth matrix. Forthe radially symmetric kernel, the kernel H (x) is generallygiven by a uniform function:

p (x) =====1N ∑

N

i = 1

KH (x − xi )

where K (x) is the d variate kernel function with compact

KH (x) =|H|− 1/2 K(H − 1/2 x)

K (x) = c. k (||x||2) > 0 ||x|| ≤ 1

where K (||x||) is the profile of the kernel, and c is thenormalization constant that makes K (x) integrate to one.If one assumes a single global spherical band width) H =h2I, then the kernel density estimator becomes

p (x) =====1

N(h)d∑N

i = 1K ( )

x − xih

(4)

The gradient of the density function can be obtained fromthe estimate of the density gradient because the denseregions in the feature space correspond to the localmaxima of the probability density function. Thus,

∇p (x) =====1N

∑N

i = 1∇K (x − xi) (5)

By using the kernel form

K (x − xi) = c.k ((x − xi)

h) (6)2

∇p (x) =cN

∑N

i = 1∇k (

(x − xi)h

)

(7)

2

cN

∑N

i = 1k' (

(x − xi)h

) .2

∑N

i = 1xi. k'(

x − xi

h)2

∑N

i = 1

k'( (x − xi)h

)2

x

the gradient of the density function becomes

where h is the bandwidth to adjust the resolution for thedifference between x and xi. The difference between theweighted mean and the center of the kernel is called “meanshift,”i.e.,

xi + 1 = m(x) = (8)

∑N

i = 1xi. k'(

x − xi

h)2

∑N

i = 1

k'((x − xi)

h)2

x

The mean shift vector always points to the direction ofmaximum increment in the density. The mean shiftalgorithm moves iteratively to each data point x in thefeature space by the mean shift vector m (x) until the meanconverges to an estimate of the local mode of the dataset.In image processing applications, a pixel x of an digitalimage is a data point represented by the two-dimensionalpixel coordinates xs = (x, y) in the spatial domain and the

=====

Journal of Digital Information Management Volume 13 Number 1 February 2015 27

pixel values xr, such as RGB color features in the rangedomain. The kernel K(.) is defined as the product of thespatial profile ks(.) and the range profile kr(.).

where hs is the spatial bandwidth for feature vector xs,and hr is the range bandwidth for feature vector xr. Acommon profile k(.) is usually used for ks(.) and kr(.) inboth domains.In the present study, the simple uniformkernel is used in the mean shift procedure. The Gaussiankernel generates similar detection results.

2.2 mean shift segmentationf is the original digital image of size M × N to be inspected.f ’represents the digital image after denoising and fmdenotes the segmented image.The proposed mean shiftscheme for segmentation in the digital feather image isexpressed in detail as follows:

1) The kernel function,initialization parameter hs,hr, andtermination threshold ε of the mean shift iterations aredetermined.

2) The center of mass for the points in this kernel iscalculated. For each pixel (x, y), the converged value mz isfound,and the gray value is f '(x, y) = mz.

3) Each point within the radius of the trajectories convergesto the same mode to image f '.

4) The gray value of each point of f ' is replaced by themean value of each mode to obtain a segmented imagefm.

3. Analysis of Eperimental Results

3.1 Experimental PrincipleIn the current study, experimental samples are collectedfrom a digital feather image detection system,and rawmaterial is duck feather. The detection system throughCCD camera obtains digital feather image with side-lighting.A homemade feather acquisition system and itscorresponding schematic are presented in Figure 1.

From the gray histogram of digital feather image (as shownin Figure 2),we can find that the gray value distribution ofthe feather leaf includes gray value distribution of thefeather quill;thus, the traditional image segmentationmethods based on image gray feature,such as edgedetection algorithm based on gray discontinuity betweendifferent regions and threshold segmentation algorithmbased on regional gray-scale similarity,cannot effectivelyextract the feather quill.In the present paper, the digitalfeather image is first grayed and smoothed using meanshift technique and the image is segmented to extractthe feather quill.

Figure 1. Acquisition system of feather image

Figure 2. Gray histogram contrast of feather leaf and featherquill

3.2 Contrast of DenoisingIn this section, our experimental results are presentedwith mean shift algorithm and compared with the resultsof adaptive and anisotropic diffusion filter(AADF)algorithm[13] and heat kernel denoising (HKD)algorithm[14].The results demonstrating different aspects are presentedin different subsections. Figure 3(a) shows a unprocesseddigital feather image. Figure 3(b)–(d) present the resultsof denoising using the given methods. hs = 5, hr = 0.2, isthe best range bandwidth given by selection model. Toobserve the effect of the various noise reduction algorithms,the three-dimensional spatial gray information distributionof Figure 3 is given, as shown in Figure 4. According tothe subjective visual qualities, the method that the presentthesis proposes leads to the expected result, which cankeep the edge of the image clearer and sharper, as wellas reduce image noises. The results obtained with otheralgorithms show that the noise was depressed but theimage details were blurred.

)2

kr (K(x) = c.k ( (9)xs

hs) .2 xr

hr

28 Journal of Digital Information Management Volume 13 Number 1 February 2015

(a)

(b)

(d)

(c)

Figure 3. Denoising results contrast: (a) Original digitalimage, (b) AADF denoising, (c)HKD denoising, and (d)

mean shift denoising

(a)

(b)

(c)

(d)

3.3 Objective Evaluation Index and SegmentationA subjective analysis is not enough. Therefore, qualitativeresearch is necessary. To further verify the reliability ofthe aforementioned denoising algorithms, we conduct acomparative study based on the performance evaluationcriteria of entropy, background suppressionfactor(BSF),mean squared error(MSE),equivalent numberof looks(ENL), and edge preserved index(EPI)[15].

Background-removing abilities,which can be predicted byBSF,are defined as:

outinSF σ

σ=B (10)

where inσ is the standard deviation of input digital image,

and outσ is the standard deviation of output digital image.

ENL,which can be used to measure the noise suppression

Figure 4. Three-dimensional comparison chart of grayinformation

Journal of Digital Information Management Volume 13 Number 1 February 2015 29

ability is defined as

ENL = σ 2(11)

μ 2

EPI =Σ| Ps−Psn|Σ| Po− Pon|

(12)

where sP is the pixel gray value of denoised image,Psn isthe adjacent pixel gray value of Ps, Po is the pixel grayvalue of original digital image, and onP is the adjacent pixelgray value of Po.

Table 1 presents the following comparison results ofdifferent denoising methods. MSE, BSF, and ENL reflectthe ability of noise suppression. As shown in the table,the BSF,MSE, and ENL of a processed image by meanshift algorithm are highest among all the algorithms; thus,the mean shift algorithm can remove the noise moreefficiently. Moreover, EPI value is the smallest among theresults of other methods. It shows that the mean shiftmethod can suppress background texture and alsopreserve a more useful image edge.

where μ is the gray-scale digital image mean, and σ 2 isthe digital image variance. The higher ENL has a smootherimage.

The ratio of the original digital image to denoising imageis EPI,and the smaller EPI has a smoother image.EPI isdefined as

After denoising filtering, the feather quill and feather leafare clearly distinguishable. At this point, using regionmerging based on threshold can obtain imagesegmentation. The result of merging regions is shown inFigure 5, which shows different gray-scale characteristicsbetween the feather quill and the feather leaf. The extractedfeather quill is shown in Figure 6.

4. Conclusion

To solve the problem of high noise and small contrastbetween the feather leaf and feather quill in a digitalimage,we use mean shift technology to smoothen theimage.Comparison results show that denoising the imageby mean shift method offers better visual effect than other

Figure 5. Image region merge result

Figure 6. Extraction of feather quill

Table 1. Denoising results with different algorithms

methods.By using region merging in the digital featherimage, the feather quill can be extracted automatically.Infuture research, multiple feature fusion by mean shift willbe considered to obtain a more effective denoising method.

Acknowledgements

This work was supported by China Postdoctoral ScienceFoundation (No.2013M542158), Guangdong NaturalScience Foundation (No.S2013040014993), StateScholarship Fund (CSC No.201408440326), GuangdongProvince Innovation Project of Education Department(No.2013KJCX0185), Natural Science Foundation of WuyiUniversity (No.2014zk10) and PhD Start-up Fund ofNatural Science Foundation of Wuyi University.

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References

[1] Xie X. (2008). A review of recent advances in surfacedefect detection using texture analysis techniques.Electronic Letters on Computer Vision and ImageAnalysis, 7 (3) 1-22.

[2] Ngan, H Y T., Pang, G K H., Yung, N H C.(2011).Automated fabric defect detection—a review. Imageand Vision Computing, 29 (7) 442-458.

[3] Arbelaez P, Maire M, Fowlkes C, et al. (2011). Contourdetection and hierarchical image segmentation. PatternAnalysis and Machine Intelligence, IEEE Transactionson, 33 (5) 898-916.

[4] Marín, D., Aquino, A., Gegúndez-Arias, M E, et al.(2011). A new supervised method for blood vesselsegmentation in retinal images by using gray-level andmoment invariants-based features. Medical Imaging, IEEETransactions on, 30 (1) 146-158.

[5] Samson, C., Blanc-Féraud, L., Aubert, G, et al. (2000).A variational model for image classification and restoration.Pattern Analysis and Machine Intelligence, IEEETransactions on, 22 (5) 460-472.

[6] LIU Hongjiang, WANG Renhuang,LI Xuecong. (2011).Improved algorithm of feather image segmentation basedon active contour model. Journal of ComputerApplications,31(8)2246-2248.

[7] Yue, Hongwei., Wang, Renhuang., Zhang, Jinghua etal. (2013). Fusion of Gray and Grads Invariant Momentsfor Feather Quill Crease recognition. In: Proceedings of2013 Intelligent Automation Conference, p. 121-128.

[8] Hongwei, Yue., Renhuang, Wang., Zuihong, He.(2012).Automatic Extraction of Feather Quill Based onNormalized Cut Algorithm, .Journal of ComputerApplications,32 (7) 1899-1901.

[9] Comaniciu, D., Meer, P. (2002).Mean shift: A robustapproach toward feature space analysis. Pattern Analysisand Machine Intelligence, IEEE Transactions on, 24(5)603-619.

[10] Hu, G., Peng, Q., Forrest, A R. (2006).Mean shiftdenoising of point-sampled surfaces. The Visual Computer,22 (3) 147-157.

[11] Comaniciu, D,. Ramesh, V., Meer, P. (2003). Kernel-based object tracking, Pattern Analysis and MachineIntelligence, IEEE Transactions on, 25 (5) 564-577.

[12] Fauzi, M F A., Lewis, P H. (2006).Automatic texturesegmentation for content-based image retrievalapplication. Pattern analysis and applications, 9( 4) 307-323.

[13] Rahman, M M., Rana, M S., Islam, M A, et al. (2013).A New Filtering Technique for denoising Speckle Noisefrom Medical Images Based on Adaptive and AnisotropicDiffusion Filter. IJRCCT, 2 (9) 689-693.

[14] Yue, H W. (2013). Research on Feather Quill ImageDenoising .Computer Modelling and New Technologies,17 (4) 51-57.

[15] Yang, H X., Wang, X S., Xie, P H, et al. (2011). Infraredimage denoising based on improved threshold and inter-scale correlations of wavelet transform. Acta AutomaticaSinica, 37 (10) 1167-1174.