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Image Processing as a Part of Big Data Initiatives

  1. 1. MELTEM BALLAN, PH.D. MELTEMBALLAN@GMAIL.COM HTTPS://WWW.LINKEDIN.COM/IN/MELTEMBALLAN Learning From DATA: Image processing as a part of Big Data Initiatives
  2. 2. Data is like gunpowder! You can make a marvelous firework OR a dangerous weapon from it
  3. 3. Erosion of Boundaries in Information Age •Between industrial sectors •Between products and services •Between producers and users •Between IT and non-IT industries •Between science and industry •Between science disciplines •Between people
  4. 4. New Generation of Products in Information Age • More digital than analogue • Advanced mechanical components enabled by CAD techniques • Increasing powers of embedded IT components • Increased complexity Greater flexibility More functions Higher performance
  5. 5. •Major Advances in Sensor Technology •Major Advances in Sensor DP Technology •Use of Machine Learning and Soft Computing Recognition Technology
  6. 6. Intelligent Recognition Technology • Eyeprint identification in ATM cash machines. In this system developed by NCR, a camera captures a digital record of a user's iris and can verify identity within seconds from a central database. • Supermarket checkout scanner (US Patent 5,673,089) which uses scent sensors to identify fruits and vegetables. • Molecular breath analyzer that can detect diseases such as lung cancer, stomach ulcer and hepatitis at much earlier stages than currently used in radiological and laboratory tests.
  7. 7. What is INTELLIGENCE? "Intelligence is a mental quality that of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one's Britannica
  8. 8. This tells us WHAT but not HOW. Thus opens a room for introducing instrumental definitions. Here we may introduce the definitions ARTIFICIAL INTELLIGENCE or COMPUTATIONAL INTELLIGENCE.
  9. 9. What is ARTIFICIAL INTELLIGENCE? ”The branch of computer science that studies how smart a machine can be, which involves capability of a device to perform functions normally associated with human intelligence as reasoning, learning and self involvement. Expert Systems, Heuristics, Knowledge Based Systems and Machine Learning” Webster’s New World Directory on Computer Terms
  10. 10. AI CI Hard Computing Soft Computing FL NN ES
  11. 11. AI versus CI? An AI program that cannot solve new problems in new ways is emphasizing the artificial and not the intelligence. The vast majority of AI have nothing to do with learning. They may play excellent chess, but they cannot how to play checkers, or anything else for that matter. In essence they are calculators. Any system, whether it is carbon-based or silicon-based, whether it is an individual, a society, or a species, that generates adaptive behavior to meet goals range of environments can be said to be intelligent. In contrast, any system that cannot generate adaptive behavior and can only perform in a single limited environment demonstrates no intelligence. (Fogel, 1995)
  12. 12. What makes the algorithms intelligent ? Chess + Checkers = DATA
  13. 13. DATA Information output by a sensing device or organ that includes both useful and irrelevant or redundant information and must be processed to be meaningful. (
  14. 14. GPR Data GPR systems are able to penetrate under the ground and to detect metallic and non-metallic objects from their dielectric characters.
  15. 15. GPR Data: Business Case Land Mind or Coke Can?
  16. 16. FINGERPRINT DATA • Fingerprints are convex and concave parallel lines that occur on points of fingers. • Those lines are unique and do not change with age.
  18. 18. FOOD MOLDS  Theoretically, 1 billion fungal species  U.S. Depertmant of Agriculture, Agricultiral Research Service  Technical University of Denmark
  20. 20. FAST FOOD PRICE AUDIT  It is usually camera picture with images  Light can differ from angle to angle  Pictures would be too much to separate the image
  22. 22. DATA PROCESSING APPROACH Raw Data Data Preparation Feature Extraction Classification
  23. 23. Which Feature of the DATA is relevant? What Method to use for DATA processing?
  24. 24. PRE-PROCESSING METHODS Grayscale – reduces image to one color channel, ranging from white to black
  25. 25. PRE-PROCESSING METHODS Thresholding – binarizing an image in such a way that the values bigger than a threshold will be 255 (maximum pixel value in bytes), and thus set to white and pixels with smaller intensities will be set to 0 (black). It is a very important operation that is often used to prepare images for vectorization or further segmentation
  26. 26. PRE-PROCESSING METHODS Blurring – useful for generating background effects and shadows. It can also very useful for smoothing the effects of jagged edges: to anti-alias the edges of images, and/or to round out features to produce highlighting effects.
  27. 27. PRE-PROCESSING METHODS Contours – curves joining all the continuous points that have the same color or intensity along a boundary. They’re useful for object or feature detection as well as shape analysis Bounding Rectangles - the smallest rectangle that can contain a contour. You can use them to segment out individual letters and numbers in an image.
  28. 28. PRE-PROCESSING METHODS Edge Detection – points in an image where there is a change in brightness or intensity, which usually means a boundary between different objects. It measures changes in the brightness of areas of an image, which we call the gradient. We can measure both the magnitude(how drastic the change is) and direction of a gradient. If the magnitude of change at a set of points exceeds a given threshold, then it can be considered an edge. The Canny edge detection algorithm is a popular edge detection algorithm that produces accurate, clean edges.
  29. 29. PRE-PROCESSING METHODS Line and Shape Detection – If our objects of interest are of regular shapes like lines and circles, you can use Hough Transforms to detect them.
  30. 30. PRE-PROCESSING METHODS Line and Shape Detection – If our objects of interest are of regular shapes like lines and circles, you can use Hough Transforms to detect them.
  31. 31. OPTICAL CHARACTER RECOGNITION Reading and translating the text into computer readable characters.
  32. 32. TYPICAL PRE-PROCESSING Load image Convert to tiff Convert the resolution to 300 DPI Split image by color channel Edge detection Find contours Identify relevant rectangles Threshold image Find background and foreground intensities Identify the text regions Sharpen the letters Slightly blur image Save the processed image Feed the image to Classifier
  33. 33. TYPICAL CLASSIFICATION • Supervised Learning (mapping known input to a known output) Classification (mold detection) Regression (revenue forecasting) • Unsupervised Learning (figuring out the output with known input) Clustering (grouping by buying behavior) Association (associating similar behaviors) • Mixed Learning
  34. 34. PAST: REAL-TIME DATA PROCESSING  Limited Data Sample  Time Demanding
  36. 36. BOTTOMLINE • Intelligent Recognition Technology is data driven in this matter developing an intelligent system requires: • To understand the nature of the data • To bring the expert from the different disciplines together
  37. 37. AI CI Hard Computing Soft Computing FL NN ES

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Presented by Meltem Ballan, Senior Consultant-Advanced Analytics at Clarity Solution Group


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