Counting Sugar Crystals using Image Processing Techniques Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Lucky Daniel January 22, 2019 Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 1 / 23
Contents Introduction Objectives Data Methodology Results Conclusion Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 2 / 23
Background Commercial sugar crystal analysis consists of: Crystal Habit: A description of the crystals shape. Includes Elongation Ratio , Coefficient Variation , Mean Aperture Sugar Counting : Reflects efficiency of sugar refinement process Figure 1: A microscope image of unrefined sugar from Illovo Sugar in Swaziland Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 3 / 23
Objectives The objectives of the project are: 1 to count the number of sugar crystals per unit of area 2 to determine the length-to-width ratio of each crystal (the Elongation Ratio) 3 to comment on the “D” shape occurrence We design and implement an image processing pipeline to address the first two objectives Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 4 / 23
Processing Pipeline Our Processing Pipeline consists of the following steps: Pre-processing Segmentation Object Detection Object Classification We compare actual in-sample results with algorithmic results to determine optimal parameters. Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 5 / 23
Data Data consists of: 1300 Microscope images: may differ by resolution, and level of magnification Unrefined Sugar: vary by company, location of the sugar mill, week of the year Refined Sugar: year, the run, the testing point, and duration of heating Spreadsheets of image details: crystal count, crystal size distribution Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 6 / 23
Data: Some Examples a: A Well-behaved Image b: Large Overlap c: Varying Magnification d: High transparency Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 7 / 23
Methodology: Implemented Pipeline Our results are generated from the following pipeline: 1 Conversion to Gray Scale 2 Filtering (Gaussian Blurring) 3 Contrast Stretching 4 Image Dilation 5 Segmentation (Otsu’s Method) 6 Object Detection (Countours) 7 Object Classification Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 8 / 23
Methodology: Grayscale Conversion Grayscale conversion changes image from a 3-D array to a 2-D array, simplifying subsequent manipulation. Figure 2: A BW image represented as a 2D matrix. Numbers in the matrix are pixel intensity. An RGB image is represented by three 2D matrices. Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing TechniquesJanuary 22, 2019 9 / 23
Methodology: Spatial Filtering The Filtering Transformation: Figure 3: Spatial Filtering. A linear filter equally weights elements in the input mask Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 10 / 23
Methodology: Contrast Enhancement We consider two methods of Contrast Enhancement: Histogram Equalisation Transforms the histogram of pixel intensities to have a uniform empirical Cumulative Distribution Function. Contrast Stretching Enhance contrast using the following function: × a ( x , y ) − min ( a ) � � 2 N − 1 b ( x , y ) = max ( a ) − min ( a ) where a is the original image, b is the transformed image, and 2 N − 1 is the dynamic range. Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 11 / 23
Methodology: Contrast Enhancement Figure 4: An example of contrast enhancement applied to an image of sugar crystals. The increase in contrast is represented by the widening of the histogram. Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 12 / 23
Methodology: Dilation and Segmentation Dilation is a morphologocial operation that expands objects and increases connectivity between them. Thresholding is a binary function that sets all pixels above the threshold to the max value (white), and all values below the threshold to min value (black) Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 13 / 23
Methodology: Dilation Figure 5: Dilation has the effect of making the background (light areas) wider and dark areas smaller. The net result is improved noise reduction Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 14 / 23
Methodology: Segmentation Figure 6: A series of automatic thresholding procedures to seperate foreground objects from the background Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 15 / 23
Methodology: Summary Figure 7: Preprocessing: Conversion to Grayscale, Blurring, Contrast Enhancement, Dilation, Segmentation Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 16 / 23
Methodology: Summary Figure 8: Preprocessing: Conversion to Grayscale, Blurring, Contrast Enhancement, Dilation, Segmentation Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 17 / 23
Methodology: Crystal Detection We want to identify the crystals in the binarised image: Gradients of pixel intensities are calculated to determine the edges of objects. A boundary is drawn where there is a large change in gradient. The boundary is stored in memory as an array of pixels. The length of the array proxies the size of the crystal. 1 . A threshold array length was chosen to separate crystals from noise, impurities, and other objects 1 An equivalent micro-meter unit of measurement can be calculated Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 18 / 23
Methodology: Crystal Detection Figure 9: Original and labelled images Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 19 / 23
Results: Crystal Counting Table 1: Actual vs predicted number of crystals in out-of-sample images Image Actual Predicted Error 1 15 20 5 21 20 -1 2 3 16 19 3 13 15 2 4 5 17 20 3 13 21 8 6 7 16 26 0 16 14 -2 8 9 24 24 0 10 15 18 3 16 21 5 11 12 17 17 0 Bill Seota, Netshiunda Emmanuel, GodsGift Uzor, Risuna Nkolele, Precious Makganoto, David Merand, Andrew Paskaramoorthy, Nouralden, Counting Sugar Crystals using Image Processing Techniques January 22, 2019 20 / 23
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