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By: Navid Einackchi

به نام خدا. Isfahan University of Technology Electrical and Computer Department. Master Thesis of Computer Engineering- Artificial Intelligence and Robotic. Image alignment and stitching using Object Recognition Methods. By: Navid Einackchi. Supervisors: Dr Rasoul Amirfattahi

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By: Navid Einackchi

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  1. به نام خدا Isfahan University of Technology Electrical and Computer Department Master Thesis of Computer Engineering- Artificial Intelligence and Robotic Image alignment and stitching using Object Recognition Methods By: Navid Einackchi Supervisors: Dr Rasoul Amirfattahi Dr javad Askari Advisor: Dr M. Saraee Spring 2007

  2. Presentation Process • Introduction • Definition • Aligning concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions Some slides are from other sources

  3. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  4. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  5. تصوير حاصل Image Alignment and Stitching • Definition • Applications • Mosaic Image • Panorama • Virtual Environment • Robotic

  6. Image Alignment and Stitching • Definition • Applications • Mosaic Image • Panorama • Virtual Environment • Robotic

  7. Image Alignment and Stitching • Definition • Applications • Mosaic Image • Panorama • Virtual Environment • Robotic

  8. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  9. Aligning Concepts • Image Transformations • Applicable Transformations • Find transformations • How many parameters? • Calculating Parameters

  10. Image Transformations • Transition • Number of Parameters: 2 • Number of Points: 1

  11. Image Transformations • Euclidean • Number of Parameters: 3 • Number of Points: 2

  12. Image Transformations • Similarity • Number of Parameters: 4 • Number of Points: 2

  13. Image Transformations • Affine • Number of Parameters: 6 • Number of Points: 3

  14. Image Transformations • Perspective • Number of Parameters: 8 • Number of Points: 4

  15. Image Transformation Computation • Direct • Mapping one image into another using different parameters • Define an Error Function based on pixel intensity difference • Using Corresponding points • Obtaining Corresponding points • Computing Transformation parameters using corresponding points

  16. Direct Method Error Function

  17. Direct Method Error Function ؟

  18. Direct Method

  19. Using Corresponding points

  20. Using Corresponding points

  21. Using Corresponding points

  22. Using Corresponding points • How many points? • How to select? • Using human • Automatically

  23. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  24. Stitching • Stitching • Choices • Final Plane • Pixel weighting

  25. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  26. Object Recognition Methods • Local Feature Detection • Edge • Corner • Hole • Interest Regions Matching • Appearance Matching • Geometric Matching • Finding Object Relations in Image

  27. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  28. Salient Features • Features of Feature! • Invariants against transformations • Ability to explain the image • Ability to being Matched (Repeatability) • Selected Features • Corners • Holes

  29. Harris Detector Plain No changes in every direction Edge High changes in Edge Direction Corner High changes in every direction

  30. Harris Detector(Continued) Fast change Direction (max)-1/2 Slow change direction (min)-1/2

  31. Harris Detector(Continued) Edge2 >> 1 2 “Corner”1,2both big1 ~ 2 Edge1 >> 2 Plain 1

  32. Harris Detector(Continued) • Harris measurement

  33. Harris Detector(Continued) • Harris measurement response 2 “Edge” R < 0 “Corner” R > 0 Local Maximum bigger than Threshold “Plain” “Edge” |R| small R < 0 1

  34. Threshold R R x x Harris Detector(Continued) • Features • Invariant against rotation • Invariant against intensity

  35. Harris Detector(Continued) • Example

  36. Scale Invariant • Scale Problem

  37. Scale Invariant Finding Appropriate Window Size

  38. - = Scale Selection • Using Differential of Gaussian Filters • Laplacian of Gaussian (LoG) • Difference of Gaussian (DoG) Window thst maximize filter

  39. Automatic Scale Selection

  40. Automatic Scale Selection

  41. مقیاس  DoG  y x  DoG  SIFT Detector • Bulb Detector • Invariant against scale • Using DoG • Bulb detection • Scale detection

  42. Bulb position Scale position SIFT Detector • Detecting Bulb and Scale Simultaneously • Bulb position = maximum in plain • Scale = maximum in third dimension

  43. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

  44. Descriptor • Vector which describes region of interest point • Pixels • Histogram • Differential • Feature of Descriptor • Invariant against image distortion (view, angle, intensity) • Able to express similarities and differences

  45. 2p 0 SIFT Descriptor • Based on value and direction of gradients • 128 dimensional Vector • Construction • Calculation of value and direction of gradient of each pixel • Making direction histogram of gradients • Rotating interest region based on dominant direction • Dividing the region into 16 region • Constructing direction histogram for each region

  46. Construction Example

  47. DescriptorFeatures • Invariant against • Rotation • Position • Affine changes in intensity(I  Ia+ b)

  48. Presentation Process • Introduction • Definition • Aligning Concepts • Stitching Concepts • Object Recognition Problem • Salient Feature • What is Descriptor • Matching • Proposed Method for Aligning • Using Harris Detector • Using SIFT Detector • Stitching • Concluding and Suggestions

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