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	   "source": [
       "# Chapter 7: Image Segmentation"
	   ]
	},
{
		   "cell_type": "markdown",
		   "metadata": {},
		   "source": [
			"## Example 7.23: Scilab_code_for_Differentiation_of_Gaussian_function.sce"
		   ]
		  },
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"source": [
"//Caption: Scilab code for Differentiation of Gaussian function\n",
"//Fig7.23\n",
"//page388\n",
"clc;\n",
"close;\n",
"sigma=input('Enter the value of sigma:')\n",
"i=-10:.1:10;\n",
"j=-10:.1:10;\n",
"r=sqrt(i.*i+j.*j);\n",
"y=(1/(sigma^2))*(((r.*r)/sigma^2)-1).*exp(-r.*r/2*sigma^2);\n",
"plot(i,y)\n",
"legend(sprintf('The sigma value is %g',sigma))\n",
"xtitle('Differentiation of Gaussian function')"
   ]
   }
,
{
		   "cell_type": "markdown",
		   "metadata": {},
		   "source": [
			"## Example 7.25: Scilab_code_for_Differentiation_of_Gaussian_Filter_function.sce"
		   ]
		  },
  {
"cell_type": "code",
	   "execution_count": null,
	   "metadata": {
	    "collapsed": true
	   },
	   "outputs": [],
"source": [
"//Caption: Scilab code for Differentiation of Gaussian Filter function\n",
"//Fig7.25\n",
"//page389\n",
"clc;\n",
"close;\n",
"sigma1 = input('Enter the value of sigma1:')\n",
"sigma2 = input('Enter the value of sigma2:')\n",
"i=-10:.1:10;\n",
"j=-10:.1:10;\n",
"r=sqrt(i.*i+j.*j);\n",
"y1 = (1/(sigma1^2))*(((r.*r)/sigma1^2)-1).*exp(-r.*r/2*sigma1^2);\n",
"y2 = (1/(sigma2^2))*(((r.*r)/sigma2^2)-1).*exp(-r.*r/2*sigma2^2);\n",
"y = y1-y2;\n",
"plot(i,y)\n",
"xtitle('Shape of DOG Filter')\n",
"//Result\n",
"//Enter the value of sigma1: 4\n",
"//Enter the value of sigma2: 1\n",
"// "
   ]
   }
,
{
		   "cell_type": "markdown",
		   "metadata": {},
		   "source": [
			"## Example 7.27: Scilab_code_for_Edge_Detection_using_Different_Edge_detectors.sce"
		   ]
		  },
  {
"cell_type": "code",
	   "execution_count": null,
	   "metadata": {
	    "collapsed": true
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	   "outputs": [],
"source": [
"//Caption: Scilab code for Edge Detection using Different Edge detectors \n",
"//[1]. Sobel [2].Prewitt [3].Log  [4].Canny\n",
"//Fig7.27\n",
"//page389\n",
"close;\n",
"clc;\n",
"a = imread('E:\DIP_JAYARAMAN\Chapter7\sailing.jpg');\n",
"a = rgb2gray(a);\n",
"c = edge(a,'sobel');\n",
"d = edge(a,'prewitt');\n",
"e = edge(a,'log');\n",
"f = edge(a,'canny');\n",
"ShowImage(a,'Original Image')\n",
"title('Original Image')\n",
"figure\n",
"ShowImage(c,'Sobel')\n",
"title('Sobel')\n",
"figure\n",
"ShowImage(d,'Prewitt')\n",
"title('Prewitt')\n",
"figure\n",
"ShowImage(e,'Log')\n",
"title('Log')\n",
"figure\n",
"ShowImage(f,'Canny')\n",
"title('Canny')"
   ]
   }
,
{
		   "cell_type": "markdown",
		   "metadata": {},
		   "source": [
			"## Example 7.30: Scilab_code_to_perform_watershed_transform.sce"
		   ]
		  },
  {
"cell_type": "code",
	   "execution_count": null,
	   "metadata": {
	    "collapsed": true
	   },
	   "outputs": [],
"source": [
"//Caption: Scilab code to perform watershed transform\n",
"//Fig7.30\n",
"//Page396\n",
"clc;\n",
"close;\n",
"b = imread('E:\DIP_JAYARAMAN\Chapter7\teaset.png');\n",
"a = rgb2gray(b);\n",
"global EDGE_SOBEL;\n",
"Gradient = EdgeFilter(a, EDGE_SOBEL);\n",
"Threshold1 = CalculateOtsuThreshold(Gradient); // determine a threshold\n",
"EdgeImage = ~SegmentByThreshold(Gradient,Threshold1);\n",
"DistanceImage = DistanceTransform(EdgeImage);\n",
"Threshold2 = CalculateOtsuThreshold(DistanceImage) // determine a threshold\n",
"ThresholdImage = SegmentByThreshold(DistanceImage,Threshold2);\n",
"MarkerImage = SearchBlobs(ThresholdImage);\n",
"SegmentedImage = Watershed(Gradient,MarkerImage);\n",
"figure\n",
"ShowColorImage(b,'teaset')\n",
"title('teaset.png')\n",
"figure\n",
"ColorMapLength = length(unique(SegmentedImage));\n",
"ShowImage(SegmentedImage,'Result of Watershed Transform',jetcolormap(ColorMapLength)); "
   ]
   }
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			 "text": "MetaKernel Magics",
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