Abstract the inner electrons is fast enough to increase

AbstractThe gold nanostructures becameone of the most interests in many fields such as, Nano-medical, biomedical,nonlinear optical device, aerospace and space science technology 1. When thesize appeared in nanometer, new properties have been emerged including optical,magnetic, electronic, and structural properties. Synthesis of gold nanoparticlesand thin film deposition technique are carried out in the experimental setupinitiated at Nano-NRIAG Unit (NNU).The proposal task is the image segmentationfor gold nanoparticles using LAB color model segmentation phase and particlesize distributions will be determined based on image processing. The spectralanalysis and optical properties of the formed nanoparticle films will be performedusing the CCD image process to characterize the nanocomposite and surface structures.Two samples with low and heavy particle concentrations of gold nanoparticlesare considered in these analyses. The obtained results analyzed and comparedwith pre.

1.      IntroductionAccording to the relativity theory, the gold particlesmust be yellow color. hopping of the electrons between or among differentenergy levels “orbits” and bumped of some protons make energy thatabsorbed  by the metal and broadcast itagain if it have long wavelength most of visible light reflected that’s makemost of metals shinyGold “AU79” is one of the heavy atoms; thespeed of the inner electrons is fast enough to increase its relative mass. Whenthe electron is hopping or orbiting near the atom “short path” it becomesactively. In gold when it’s light absorption and re-emitted wavelength is oftenlonger. This means that there is a hodgepodge of light waves that we see fromthe gold tend to have a lower ratio of blue and violet.

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This leads to the appearanceof the gold-color looks like yellow because the yellow color have a longerwavelength than blue 2The most challenging phase of image processingresearch is the Image Segmentation because the result of the image segmentationhas a direct effect overall image analysis process. The image segmentationoverall is defined as the splitting of the image into a number of identicalsegments where if any two neighboring segments are merged Producing aheterogeneous segment 3. These segments are identical in some criteria ascolor, texture, motion and so on. The segmented image may be gray scale or colorimages. So that there are two different types of image segmentation “(1)gray scale image segmentation, (2) color image segmentation” e.g., contentbased image retrieval 4 5.

In addition to that,segmentation can be either Local segmentation “small windows on a whole image dealswith segmenting sub image” or. Global segmentation “deals with segmenting wholeimage”. 6.There exist different methodologies for the image segmentationprocess. CIELAB, Lab “, L*a*b is a Color space defined by the CIE, based on onechannel for Luminance (lightness) (L) and two color channels (a and b). Thecentral vertical axis represents lightness (signified as L*). This scale is relatedclosely to Munsell’s value axis except the value of each step is greater.

Thisis the same lightness valuation used in CIELUV. 2.     COLOR IMAGE SEGMENTATIONThe image is an opticalcounterpart of an object produced by an optical device, which has a meaningfularrangement of regions, and objects. Image analysis is the process ofelicitation meaningful information from an image, which is one of the initialsteps of pattern recognition systems. Image segmentation can be defined as theclassification of all the picture elements or pixels in an image into differentclusters that have similar features 6. The first step in imageanalysis is the image segmentation. It has subdivided the image into itsconstituent parts or objects to a pre-defined level. This level of subdivisiondepends on the nature of the problem.

Sometimes it needs to be segmented theobject from the background to understand the image correctly and identify thecontent of the image. so that , there are mainly two techniques forsegmentation: (1) discontinuity detection technique and “the commonapproach is to partition an image based on abrupt changes in gray-levelimage”  (2) similarity detectiontechnique “based on the threshold and region growing .as it is the mostcommon approach for color image segmentation”.

Color image segmentationcan be defined as a process of extracting from the image domain one or moreconnected regions satisfying uniformity (homogeneity) criterion that is basedon feature(s) derived from spectral components. These components are defined inchosen color space model. Therefore, color space plays the vital rule in colorimage segmentationL*A*B* Color Space modelThis color space was originally defined by CAE andspecified by the International Commission on Illumination.

In this color space,we have one channel for Luminance (Lightness) and another two color channelsare a and b which are known as chromaticity layers. The a* layer indicates wherethe color falls along with red green axis, and b* layer indicates where thecolor falls along with blue-yellow axis. a* negative values indicate greenwhile positive values indicate magenta; and b* negative values indicate blueand positive values indicate yellow. The most important feature of this colorspace is that it is device independent device. Which means that to say thatthis provides us the opportunity to communicate different colors acrossdifferent devices. The following figure is clearly illustrates the coordinate systemof l*a*b* color space. CIELAB, Lab, L*a*b Color space was defined by the CIE,based on one channel for Luminance (lightness) (L) and two color channels (aand b).

One problem of the XYZ color system is that colorimetric distancesbetween the individual colors do not correspond to perceive color differences. Forexample, the difference between green and greenish-yellow is relatively large,whereas the distance distinguishing blue and red is quite small. The CIE solvedthis problem in 1976 with the development of the three-dimensional Lab colorspace (or CIELAB color space). In this model, the color differences,which you perceive, corresponds to distances when measured calorimetrically.

The a axis extends from green (-a) to red (+a) and the b axis from blue (-b) toyellow (+b). The brightness (L) increases from the bottom to the top of thethree-dimensional model. This color space is better suited to many digitalimage manipulations than the RGB space, which is typically used in imageediting programs. For example, the Lab space is useful for sharpening imagesand removing artifacts in JPEG images or in images from digital cameras andscanners.

 Figure 1 The CIELAB color space (from www.linocolor.com)2.1.Converting RGB to LABTheRGB and CMYK color models are dependent device models unlike the L*a*b* color model thatis Mathematically described space and is perceptually uniformed color space.

sothat there are no direct ways “formulas” used or conversion between RGB or CMYKvalues and L*a*b* value. The RGB or CMYK values first are transformed to aspecific absolute color space, such as s-RGB or Adobe RGB or xyz domain. Thisadjustment will be dependent device, but the data result from the transformwill be independent device 7FromRGB to XYZ.

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it is a type ofinstance-based learning or lazy learning, where the function approximatedlocally and all computation deferred until classification.3.     Proposedmethod3.1.

The pseudo model 1)      Acquire Image “must be RGB image” 2)      Calculate Sample Colors in L*a*b* Color Space for Each Region For I= 1 to n           //n is the maximum number of pixels in image Calculate the values of XYZ for every pixel Convert from XYZ to L*ab 3)      Classify Each Pixel Using the K Nearest Neighbor Rule ·   ?nd the k closest training points (small ||Xi-X0|| according to some metric, for ex. Euclidean, Manhattan, etc.) ·   predicted class: majority vote ·   predicted value: average weighted by inverse distance 4)         Display Results of K Nearest Neighbor Classification 5)         Display ‘a*’ and ‘b*’ Values of the Labeled Colors.

  Pseudo code for LAB segmentation using knearest neighbor  Gold nanostructures 4.1 GoldNanoparticles Characterization Transmission Electron Microscope Clarifiesthe gold nanoparticles diameters is determined withTransmission Electron Microscope “TEM”. The images are measured by GEM 2100(GEOL) Japan’ instrument. The figures are taken for the two samples 1 and 2,these samples are corresponding to the preparation of goldnanoparticles by means of light and heavy concentrations.

For sample1, TEM image was taken from different positions and with differentangles of vision not in the plane of view. The obtained resultsof image analysis show the gold particles with different grain sizes.The diameters of the particles vary typically between 5 and 100 nm.Moreover, the assembly behavior of the nanoparticles tends toform the aggregation, which is based on the assumption about thenanoparticle interaction potential. Gold nanoparticles have the propertyof the occurrence of the agglomeration of small particles intolarger aggregates 9.

Figure 2 TEM images of gold nanoparticles (A) for sample 1 and (B)for sample 2. 10 Experimental result 5.1 Datadescription and imaging The properties for the papered gold thin films provided inthe following discussion. In this study, two samples for gold thin films havebeen taken into our consideration.

The first one is considered for the filmwith low values of nanoparticle concentrations, which is namely sample2 (lightintensity). The second presented as sample 1 and considered for the case ofdeposition technique with high density and concentration (heavy intensity).Structural and optical properties of the surface of the two samples will be discussedin details as follows. Nanostructure and morphological analysis of gold thin filmsurface is introduced in Figure 3 the images are taken with an opticalmicroscope connecting with CCD camera (Ts View 5 MP) with the pixel size 2592Hx 1944V.

The surface morphology of the generated Nano films for a selected areawith dimensions (500 x 500 pixels) of the two considered samples 1 and 2 are displayedin Figures 3A and 3B respectively. Morphology, structural characteristics andthe fits’ images are reduced and analyzed from the image processing programs.Some of these programs, such as Ds9 image display program and Maxim-DLastronomical software, used in our analysis. These programs used for theanalysis of the astronomical images taken from space and astronomicaltelescopes .The analysis shows that the distributions of nanoparticles on the surfacehad been occurred. In Figure 3B, more density particles on the surface of thedeposit film, have observed than that found in Figure 3A for the case of lowparticle concentrations.

3 A) 3 B)  Figure 3: Optical microscopic images for aselected area of thin film surfaces for (A) sample 1 and (B) sample 2.5.2  Implemented LAB model In this system the image is acquiredas RGB image then it converted to L*a*b color model the conversion must be doneon 2 steps first one is to convert image to xyz color model then convert fromxyz to L*a*b then using K Nearest Neighbor Rule each pixel is classified thenthe results are displayed.

The needed color is only the yellow color becausethe gold nanoparticles must be yellow according to the theory of relativity 1112. The output of the system is the picture that the gold nanoparticles arevisible as shown in figure 4 the heavy and the light image. The figure 4A showsthe particles in the less density image and (3-D) spatial distribution of thinfilm surface for it where. The figure 4B shows the particles in the heavydensity image and (3-D) spatial distribution of thin film surface for it .thehistogram of the original images are drawn using MATLAB as shown in figure 5and the histogram of segmented image that shown in figure 4 is drawn and shownin figure 6 . The particle size determined by Maxiem-DL the relation betweenthe particle size and the particle number drawn and shown in figure 7 and it isnearly to the previous published papers 9 13. The results of the referencepaper showmen in figure 8   4 A) 4 B) Figure 4:the yellow image aftersegmentation using LAB for  (A)  sample 1 and (B) sample 2.and (3-D) spatial distribution of thin film surface for the yellowimage 5         A) 5  B)  Figure 5:the histigram of  Opticalmicroscopic images for a selected area of thin film surfaces for (A)  sample 1 and (B) sample 2.

6 A) 6 B) Figure 6 : the histigram of the yellow image after segmentation for  (A) sample 1 and (B) sample 2.using 7 A) 7 B)  Figure 7 : The relation between theparticle size and the particle numbers (A)  sample 1 and (B) sample 2.  8 A)     8 B)  Figure 8 : The relation between theparticle size and the particle numbers (A)  sample 1 and (B) sample 2 inreferance paper 96.

    ConclusionThin films made of gold deposited onpolypropylene substrates. The combinations and optical properties of goldparticles, used in thin layer formation given the immersion method used tolocalize the gold particles on the polymer substrate. These thin films areprepared with low and high concentrations of nanoparticles and obtained withgrain sizes up to 100 nm. The output of extracting the Structural and OpticalCharacteristics of Gold nanoparticles Image based on LAB color model and KNN isbetter than performing the Morphological and structural analyses of the formedthin films using the CCD Image Processing and software programs such asMaxim-DL and DS9 display program.

Using LAB color model and KNN give bettervalues and the particles are observed because use of LAB color model makes iteasy to search for the yellow color the color of gold Nano-particles