Imaging

1. Digital Image Processing

2.  Introduction-Image and Video Processing
 Image Processing Techniques
 Recent trends addressing (Real Time Applications)
 Security Application
 Biomedical Application
 Biometric Application
 Satellite Application
 Photoshop Application

3.  An image can be defined as a two-dimensional signal (analog
or digital), that contains intensity (grayscale), or color
information arranged along an x and y spatial axis.
 Also it is defined as collection of pixels.
 Mathematically it defined inters of Matrix (m x n)
 Pixels – it is point that is having location(x, y) and value(I)
 Two Coordinates – Spatial and Pixel Coordinates

7.  Video Processing is a particular case of signal processing,
which often employs video filters and where the input and
output signals are video files or video streams. Video
processing techniques are used in television
sets, VCRs, DVDs, video codec's, video players, video
scalars and other devices. For example—commonly
only design and video processing is different in TV sets of
different manufactures.

9.  Image Acquisition
 Image Enhancement
 Image Restoration
 Image Compression
 Image Segmentation
 Image Fusion
 Image Recognition
 Image Retrieval
 Image Steganography
 Image Watermarking

11. Security Application

12. Security We have 2 types of application
• Data Security on Transmission Analysis
• Copyright Protection on Image/Video/Audio
Protection

13. Data Security on Transmission Analysis

14. Data Security on Transmission Analysis

15. Copyright Protection on Image/Video/Audio
Visible Watermarking:-
In-Visible Watermarking:-

16. Biomedical Application

17.  Image Segmentation : Tumor Detection and Classification
 Image Enhancement : X-ray [CT / MRI / PET]
 Image Fusion :
Pixel Matching Analysis on
 Average ;
 Maximum and
 Minimum Intensity Matching Method

18. Input Image Segmentation Image
Threshold
Segmentation Image
[70%]

19. Abnormal 50% Tumor
(Cancer Effected)
Abnormal 70% Tumor
(Cancer Effected)
Normal

20. Input Image Histogram Enhanced Image
Noise Image Filtered Enhanced Image

21. CT Images MRI Images Fused Images

22. Biometric Application

23. Human verification Using Security Analysis on
Industrial – Time Analysis [ON / OFF Duty Cycle]
Office –ID Verification
Pass port Verification
Election-Security on Vote ID
Pass Book –Signature Verification

25. DATA Sets
Images
Test
Image
Features Extractions
on Shape / Color/
Intensity / Patterns
Similarity Measurement
between data Set and
Test Image
Features Extractions
on Shape / Color/
Intensity / Patterns
Authenticate / Un-
Authenticate

26. Satellite Application

27.  Satellite imagery consists of images of Earth or other planets
collected by satellites. Imaging satellites are operated by
governments and businesses around the world.
 Satellite imaging companies sell images under license. Images
are licensed to governments and businesses such as Google
Maps.
Various Types of Images:- Hyper-spectral Image /
Multi-spectral Image / Pan Sharpened Image / High
Resolution Image

28. RGB Component on 24 bit Contrast
using Multi Spectral Image

29. Single Plane Component on 8 bit
Contrast using PAN Sharpened
Image

30. Image Fusion Analysis on
 Average Fusion Method
 Maximum Intensity Matching Method
 Minimum Intensity Matching Method
 Laplacian Pyramid Method
 Gradient Edge Operator Method

31. Contrast Analysis on Transform
Features Extraction on Fusion Image

32. Surveillance Application

33.  This method usually increases the global contrast of many
images, especially when the usable data of the image is
represented by close contrast values.
 Through this adjustment, the intensities can be better
distributed on the histogram. This allows for areas of lower
local contrast to gain a higher contrast.
 Histogram equalization accomplishes this by effectively
spreading out the most frequent intensity values.

34. Frame Noise Reduction
Digital Slow Shutter
White Spot Reduction
Removal IR Cut Filter
Histogram Equalization
Retinex Theory Analysis