Object identification using mobile devices

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  • evi

    en

    Shantochnol

    Texture

    m comimpon andture aing cletho

    The Euclidean distance is also used to represent the object similarity. The similarity canreach 87.5%, 62.5%, 75% and 87.5% respectively.

    makeformartmuishto p

    increasing. An object identication system [16] can also

    are located in the corner place, and its security let peopleworried. If the monitored system can be stalled in toiletor outside locker room, when some specic strangers

    base set [13] as an experimental basis, and thener as anproposed m

    can reduce the waiting time of mobile devices whequery because of the less feature vector data and thple vector distance operation. They will help for theidentication according to these characteristics.

    In the past, a lots of researchers majored on how tocombine the image features with the histogram to identifyimage features [2935]. However, different users have

    http://dx.doi.org/10.1016/j.measurement.2014.01.0290263-2241/ 2014 Elsevier Ltd. All rights reserved.

    Corresponding author at: Department of Civil Engineering, ShantouUniversity, Guangdong, PR China. Tel./fax: +86 75482904748.

    E-mail address: puuan.juang@msa.hinet.net (L.-H. Juang).

    Measurement 51 (2014) 100111

    Contents lists available at ScienceDirect

    Measure

    journal homepage: www.elsevibe developed into the security monitor system due to to-day social security letting people panic, more public toilets

    simple vector distance matching classiidentication source. We found that theuses aobjectethod

    n userse sim-objectwith more convenient and friendly service. Think about it,if the service system can identify the object and its charac-teristics, then provides personal service for the differentobject, which can reduce the time when customer searchesfor products. In the other hand, it will also make the cus-tomer feel very intimate and accelerate the service for theirguests, nally it will make the companys performance

    [1728], but in the monitor system the face image has alow resolution or other factors lead to a lower recognitionrate. In this research, we will propose a preliminary designand experimental results of object recognition frommobiledevice that utilizes the texture and the color features witha simple vector distance matching classier to training andextract the characteristics. This paper uses the object data-Color featuresVector distanceMobile phone

    1. Introduction

    Digital product development hason human life and convenience in inIf the identication system in a depstore entrance can accurately distingferent type products, we will be able 2014 Elsevier Ltd. All rights reserved.

    many of benetsation technology.ent store or easybetween the dif-rovide customers

    wander out, the identication system [716] will can earlynotify guards or the related people to handle it, so that canreduce to guard 24 h patrol and also has a protection onthe social security aspects.

    Object identication is an important research at pres-ent. In the early, object identication studies were mostlyfocused on face or the prole feature such as genderKeywords:Object recognition

    reach up to 100% of object identication rate when making a querying in a mobile phone.Object identication using mobile d

    Li-Hong Juang a,b,, Ming-Ni Wu c, Zhi-Zhong WaDepartment of Civil Engineering, Shantou University, Guangdong, PR Chinab The Key Lab of Digital Signal and Image Processing of Guangdong Province,cDepartment of Information Management, National Taichung University of Te

    a r t i c l e i n f o

    Article history:Received 20 September 2013Received in revised form 17 December 2013Accepted 20 January 2014Available online 28 January 2014

    a b s t r a c t

    To detect object frois very difcult buta preliminary desigthat utilizes the textor distance matchthat the proposed mces

    g c

    u University, Guangdong, PR Chinaogy, Taichung, Taiwan, ROC

    plex background, illumination variations and texture by machinertant for adaptive information service. In this research, we presentexperimental results of object recognition from a mobile devicend the color features by image pre-processing with a simple vec-assier to train and extract the characteristics. The result showsd can adopt the few characteristic values and the accuracy can

    ment

    er .com/ locate /measurement

  • different perspectives. In this research, our topic is to solvehow to nd out a specic object rapidly. In this research,we used two characteristics of feature extract and featurecomparison for image retrieval. In the feature extract part,we add the color feature information. Because there waslots of researchers in the past, they only adopted gray im-age or binary image, which cannot represent more detailinformation for image. Therefore, we rst transformedRGB color space to HSV color space, after this processing,we quantized it to 72 color numeric which can be used

    right), shrink and rotation ones. Finally, we used Euclidean

    tract its feature image. The third step bases on the previous

    Fig. 1. HSV color attribute.

    Fig. 2. The micro structure detection processing.

    Fig. 3. The edge pixel similarity judgment.

    Fig. 4. The four kinds of judgme

    L.-H. Juang et al. /Measurement 51 (2014) 100111 101two steps to separate them from their similarity variance.The fourth step makes characteristic comparisons fromthese variances. The fth step uses a simple vector distancematching classier to train and extract the characteristics,then recognize the queried object. They are explained asfollows:

    2.1. HSV color space

    This paper bases on HSV image as shown in Fig. 1 beingas object identication from the original data. In this imageprocess procedure, HSV color-level process was used toconvert a RGB color image to an intensity color-level im-age. Usually, intensity V, saturation S and hue H can be

    nts for their focal points.distance to represent the object similarity. The accuracycan reach up to 100% for the above four deformation cases.The similarity can reach 87.5%, 62.5%, 75% and 87.5%respectively. Eventually, this research has a high accuracyfor different angles, sizes and directions. In the following,we will develop the procedure as follows.

    2. The procedure of the image processing technique

    This research uses some image processing techniquesfor object features extraction, and then bases on the tex-ture and the color features by using the micro structurefeature as an important characteristic reference for objectidentication. The procedure includes the ve steps. Therst step uses HSV color space to deal RGB transformation.The second step uses the micro structure technique to ex-for an object recognition on the mobile. In the feature com-parison part, in order to let every user can take photos byhis perspectives, we need rst to nd out the objects masscenter then transform the centroid into the polar coordi-nates. Using this method, it can solve the image rotationproblem in this object identication. In the experiment,we will transform the original photos into shift (left and

  • Fig. 5. The similar pixel merger.

    102 L.-H. Juang et al. /Measurement 51 (2014) 100111used for RGB to HSV color-level converting which can beexpress as [2]

    H 6 GBMAXMIN 60; if R MAX2 BRMAXMIN 60; if G MAX4 RGMAXMIN 60; if B MAX

    8>: 1

    Fig. 6. The system processing owchart.

    Fig. 7. The process for the mS MAX MINMAX

    2

    V MAX255

    3

    where MAX =max(R,G,B) and MIN =min(R,G,B) representthe maximal value and the minimal value in the RGB colorspace respectively. The hue H value range is 0360, thesaturation S value range is 0100%, the lower value be-comes more gray level, and the intensity V value range isalso 0100%. In this research, we chose to use HSV colorspace in order to reduce the feature number and also re-duce the effects of chromatic aberration on image, there-fore H is divided into 8 sections, S has 3 sections and Vhas also 3 sections, then the total is 72 colors.

    2.2. Micro structure

    The researcher [11] proposed a capturing image methodbased on the micro structure characteristics, and his mainconcept is to use the relationships between texture to de-tect whether there are the same point within a particularregion and its process is shown in Fig. 2. The processingsteps are described as follows:

    Step A: When the change for the axial X and Y of gradi-ent information is acquired, rst of all, we use Eqs. (4)(8) to calculate the angle h between the two vectorsa H00x ; S

    00x ;V

    00x

    and b H00y; S

    00y;V

    00y

    as follows:

    jaj H00x 2 S00x 2 V 00x 2

    q4

    jbj H00y 2

    S00y 2

    V 00y 2r

    5erger of similar pixel.

  • will not affect on their comparison features. Fig. 3(a)shows the calculation results for this step scope.Step C: Here we judge these points with the same pixelvalue in the particular region and compare the sur-rounding points with the center point, and then leavethe same, otherwise delete them. For example, the pixelvalue of Fig. 3(b)s focal point is 2 and is surrounded bythe other eight pixels, and its above and its right arealso 2, therefore we only keep these three pixel dataas shown in Fig. 3(c).Step D: After step C processing, we reserve the outlinewhich their pixel values are same with the center pointas shown in Fig. 3(d). Meanwhile, we extend the com-parison rule to the other reference points to do thesame operation for the bigger image size as shown inFig. 4. Its image size is 6 6 pixels, and we cut it intothe four 3 3 Blocks as shown in Fig. 4(a). After usingstep B and step C operations to process Fig. 4(a), theinterval information block is formed as shown inFig. 5(a). Furthermore, we shift the four blocks inFig. 4(a) to the right, the down and the right down byone1X3 block location respectively as shown inFig. 4(b), (c) and (d)s block cutting position. Meanwhilestep B and step C operations are used again to obtainthe results as shown in Fig. 5(b), (c) and (d) respec-tively. Finally we merger these four cutting operationblocks to form the shaded pixel part, then acquire thecharacteristic pixel location as shown in Fig. 5(e),the characteristic pixel location will be recorded by

    Fig. 8. The histogram statistics.

    L.-H. Juang et al. /Measurement 51 (2014) 100111 103ab H00xH00y S00xS00y V 00xV 00y 6

    Fig. 9. The horizontal turn over.Cosda; b abjajjbj 7h Cos1da; b Cos1 abjaj jbj

    8

    where a H00x ; S00x ;V

    00x

    and b H00y; S

    00y;V

    00y

    are these pixel

    value for hue H, saturation S and intensity V with dou-ble rotation at X and Y directions.Step B: When the included angle value is obtained, thenwe use it to calculate the property of edge image tocheck if they are similar. Meanwhile, we set the 30 asone unit and split into 6 intervals (the value is 05).In the case, a video with a deviation of shooting angle

    Fig. 10. The vertical turn over.the nal merger pixel value.

    Fig. 11. (a) The rst column pixel shift, and (b) the second-fth columnsforward shift.

  • Fig. 12. The trained image database.

    Fig. 13. The test image database examples.

    104 L.-H. Juang et al. /Measurement 51 (2014) 100111

  • 2.3. Characteristic extraction

    In this research, the characteristic extraction process isdivided into six major steps shown in Fig. 6, the followingis their procedure description:

    Step A: A RGB color space image is converted by Eqs.(1)(3) into HSV color space image, then each pixel(R,G,B) in this image is mapped to (H,S,V).Step B: (H,S,V) of each pixel will be quantied into 72colors as the above description (H,S,V) are separatedinto three kind levels of 8, 3, and 3, and the quantitativeresults become (H0,S0,V0). Therefore it can reduce thetime complexity of image processing and improve thetoughness of color identication, Fig. 7(a) shows thequantitative results for this example.Step C: We convert (H0,S0,V0) by Eqs. (9)(11) into theplane coordinate and its conversion result is H00; S00;V 00.

    H00 S cosH0 9

    S00 S sinH0 10

    V 00 V 0 11Step D: Using Sobel edge detection [3] for the pixel val-ues in the converted plane coordinates calculates the

    d feature image example.

    Table 1The original image querying accuracy.

    Original image Accuracy (%)

    Correct number 16 100Similar number Top 3 (10) 100

    Table 2The left shifted image querying accuracy.

    Original image Accuracy (%)

    Correct number 16 100Similar number Top 3 (4) 100

    Table 3The right shifted image querying accuracy.

    Original image Accuracy (%)

    Correct number 16 100Similar number Top 3 (5) 100

    Table 4The shrunk image querying accuracy.

    Original image Accuracy (%)

    Correct number 16 100Similar number Top 3 (12) 100

    Table 5The rotated image querying accuracy.

    Original image Accuracy (%)

    Correct number 128 100Similar number Top 3 (10) 100

    L.-H. Juang et al. /Measurement 51 (2014) 100111 105horizontal as well as the vertical gradient value, thenuses the Eqs. (12), and (13) for the shielding edge detec-tion and the upper left corner image is the startingpoint, then from the pixels of left to right and top tobottom in the entire image will use the shield operationin order to acquire the direction change gradient [5] inX direction, H00x ; S00x ;V 00x, and Y direction, H00y; S00y;V 00y, and

    Fig. 14. The extracte

  • 106 L.-H. Juang et al. /Measurement 51 (2014) 100111then the variance between two vectors can be obtainedby the gradual change, meanwhile acquire its vectorangle for the next step.

    Gx 1 0 12 0 21 0 1

    264

    375 12

    Gy 1 2 10 0 01 2 1

    264

    375 13

    Step E: We will detect and capture its characteristics byusing the above micro structure section as shown inFig. 5(e), which will combine with HSV color informa-tion in steps B to get the color value of the micro struc-ture for its image characteristics as shown in Fig. 7.Step F: In this step, rst we need to obtain the imagecharacteristic value from its histogram statistics whichare created by computing its frequency distribution ofthe elements in a vector input, its Matlab code is asfollows:

    Y histu;n; 14where u is the input vector and n is the number of dis-crete bins. Fig. 7(c) is the results from the numerical cal-culation of the micro structure characteristic value forthe merger of similar pixel. After using the histogram

    Fig. 15. The sorting results from the objectstatistics as shown in Fig. 8, then we can acquire itsimage eigenvector which is for converting the true colorimage into the indexed image as follows:

    e0;e1; .. .;e710;0;0;0;1;1;0;0;0;0;1;1;0;2;0;1;0;1;1;0;0;1;1;1;1;3;1;0;0;0;0;1;0;0;0;1;1;0;0;1;0;0;0;0;0;0;0;1;2;4;0;0;2;1;0;1;0;0;0;1;0;0;0;0;0;0;0;0;1

    2.4. Feature comparisons

    In addition to the previous method for the feature com-parison using the original image characteristics, we alsoproposed the other three methods for the feature compar-ison including the horizontal ipping, vertical ipping andpixel shifting. For the horizontal and vertical ipping sec-tion, rst we need to nd its images symmetry axis, thenmake a mirror reversion as shown in Fig. 9(a) for the origi-nal image characteristics and in Fig. 9(b) for the result fromthe mirror reversion of perpendicular to the symmetryaxis. Similarly, Fig. 10(a) is for the original image charac-teristics and Fig. 10(b) is for the result from the mirrorreversion of horizontally to the symmetry ax...

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