1. Yingcai Xiao Chapter 10 Image Processing

2. Outline Motivation DWA: a real world example Algorithms Code examples

3. Motivation: Visualization vs. Computer Vision Visualization: information to image Computer Vision (CV): image to information Inverse of the process Visualization: Information/Data -> Graphics Objects -> Images Visualization Rendering Computer Vision: Images->Graphics Objects ->Information/Data Image Processing Pattern Recognition

4. Motivation: Applications Augmented Virtual Reality: visualization meets image processing Google Glass google.com/glass Kinect Fussion https://www.youtube.com/watch?v=quGhaggn3cQ https://www.youtube.com/watch?v=GKRHWBS6guM

5. Motivation: Applications Medical Imaging tumor detection, wound assessment Monitoring traffic, surveillance, defects detection Automation robotics, factory, driving Google autonomous car

6. Applications: DWA Digital Wound Assessment Can be done locally or remotely Can be 2D or 3D

7. Applications: Remote DWA

8. DWA: Web-based Image Processing Multi-tier Web Application: Client (phone app) Web Server Application Server Database Server

9. Architecture of a Four-Tier Application DBMS / Database Server Database User Interface Database Engine Supporting Software Application Server Database API Application Logic App User Interface WEB S E R V E R Architecture of a Four-Tier Application WEB C L I E N T

10. Architecture of Remote DWA DBMS / Database Server Database User Interface Database Engine Supporting Software SANA (sana.mit.edu) OpenMRS DWA Image Processing Mobile Dispatch Server WEB APP S E R V E R Architecture of Remote DWA Four-Tier Application MOC A Phon e APP

11. DWA: Data Representation 2D array of colors Image header: info describe the image (dimensions, …) Compressed or not VTK image data (nxnx1) Java image readers

12. DWA: Image Processing (1)Preprocessing (2)Segmentation (3)Image Analysis (4)Healing Projection

13. DWA: Image Preprocessing (1) (1)Calibration (2) (2)Ruler processing (3) (3)Outlier remover

14. DWA: Segmentation (1) (1)Grey Scale (2) (2)Gradient (3) (3)Edge formation

15. Segmentation: Grey Scale Conversion /// grey scale as the length of the RGB color vector Public GrayScaleImage convertToGrayScale( ColorImage colorimage, int width, int height) { …. for(int i = 0; i < total; ++i) { newimage[i] = Math.sqrt((image[i * 3] * image[i * 3] + image[i * 3 + 1] * image[i * 3 + 1] + image[i * 3 + 2] * image[i * 3 + 2] ); } return new GrayScaleImage(width, height, newimage); }

16. Segmentation: Grey Scale Conversion /// grey scale as I in the IYQ model Public GrayScaleImage convertToGrayScaleYIQ( ColorImage colorimage, int width, int height) { …. for(int i = 0; i < total; ++i) { newimage[i] = 0.299 * image[i * 3] + 0.587 * image[i * 3 + 1] + 0.114 * image[i * 3 + 2]; } return new GrayScaleImage(width, height, newimage); }

17. Segmentation: Gradient Computation /// Compute the gradient of grey scale, public void computeXDifImage(GrayScaleImage image){ …. for(int i = 0; i < total-1; ++i) { newimage[i] = Math.abs(image[I + 1] - image[i]);; } return new GrayScaleImage(width, height, newimage); }

18. Segmentation: Edge Formation Create an array list for each edge. ArrayList edge = new ArrayList () for(int i = 0; i < total; ++i) { if(image[i] > threshold) addEdgePixel(i); // …… } edges = new ArrayList >(); edged.add(edge);

19. Segmentation: Edge Formation Geometric Descriptors: (1) (1)List of edge pixels. (2) (2)List of line segments. (3) (3)Thining.

20. DWA: Image Analysis (1)Size (2)Color (3)Shape (4)Depth (3D)

21. Image Analysis: Size (1)Registration (2)Calibration (3)Measurement count pixels by region growing

22. DWA: Healing Projection (1)Fit into existing healing trajectories. (2)Numerical results of predication.

23. Image Processing Tools: ITK by Kitware: http://www.itk.org/ OpenCV: http://opencv.org OpenCV application in robotics: http://opencv.willowgarage.com/