This document discusses image processing and big data initiatives. It describes how data can be used to create either useful applications or dangerous weapons. It also discusses the erosion of boundaries between different fields due to information technologies. New products are increasingly digital and complex due to advances in areas like sensors, machine learning, and computer technologies. Intelligent recognition technologies can now identify people from iris scans or detect diseases from molecular breath analysis. Both artificial intelligence and computational intelligence are discussed in the context of using data and algorithms to enable adaptive and intelligent systems. Various methods for preprocessing and classifying data are also outlined.

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. Data is like gunpowder!
You can make a marvelous firework
OR
a dangerous weapon from it

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. 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. •Major Advances in Sensor Technology
•Major Advances in Sensor DP Technology
•Use of Machine Learning and Soft Computing
Recognition Technology

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. 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. 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. 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. AI CI
Hard
Computing
Soft
Computing
FL
NN
ES

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. What makes the algorithms intelligent ?
Chess + Checkers = DATA

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.
(http://www.merriam-webster.com)

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. GPR Data: Business Case
Land Mind or Coke Can?

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.

17. SMART HOME AND PHONE TECHNOLOGIES:
BUSINESS CASE

18. FOOD MOLDS
 Theoretically, 1 billion fungal species
 U.S. Depertmant of Agriculture,
Agricultiral Research Service
 Technical University of Denmark

19. BUSINESS CASE: EDIBLE OR NOT

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

21. BUSINESS CASE: SECRET PRICE
AUDIT

22. DATA PROCESSING APPROACH
Raw Data
Data Preparation
Feature Extraction
Classification

23. Which Feature of the DATA is
relevant?
What Method to use for DATA
processing?

24. PRE-PROCESSING METHODS
Grayscale – reduces image to one color channel, ranging from white to black

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. 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. 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. 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. 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. 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. OPTICAL CHARACTER RECOGNITION
Reading and translating the text into computer readable
characters.

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. 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. PAST: REAL-TIME DATA PROCESSING
 Limited Data Sample
 Time Demanding

35. NOW: REAL-TIME DATA PROCESSING

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. AI CI
Hard
Computing
Soft
Computing
FL
NN
ES

38. THANK YOU VERY MUCH.
FURTHER QUESTIONS AND SUGGESTIONS:
MELTEMBALLAN@GMAIL.COM