Содержание
- 2. Introduction Object Recognition via Classical Moments Control of a Robot Arm System Conclusion Contents:
- 3. In this project, an application of computer image processing to recognize various objects and the vision-based
- 4. Introduction Image Processing (Object Recognition via feature extraction, Edge Detection to define the place of the
- 5. After Hu presented moment invarients in 1962, they are widely used in many applications. Subsequently, Resis
- 6. Image Processing via Classical Moments Central Moment of an Area: Whereas; and If we calculate the
- 7. Image Processing via Classical Moments Hu moments can be found. (2) (3) (4)
- 8. Image Processing via Classical Moments (5) (6) (7) Six of these invariants are invariable if the
- 9. Image Processing via Classical Moments Zernike Moments: Zernike polynomials, form a complete orthogonal set over the
- 10. Image Processing via Classical Moments Zernike moments of order p with repitation q for a digital
- 11. Image Processing via Classical Moments Orthogonal Fourier-Mellin Moments: r is the length of the vector from
- 12. Image Processing via Classical Moments The magnitude of Hu moments, OFMMs and ZMs are rotation, translation
- 13. Image Processing via Classical Moments Light Shields (on both sides) CCD Camera (connected to the grabber
- 14. Image Processing via Classical Moments Trigerring Line Boundary Lines Object Direction Object Conveyor Band CCD Camera
- 15. Image Processing via Classical Moments In this project, four types of image processing techniques are used:
- 16. Image Processing via Classical Moments Image Data Reduction a. Digital Conversion: It reduces the number of
- 17. Image Processing via Classical Moments 2. Segmentation a. Edge Detection In Edge Detection it is considered
- 18. Image Processing via Classical Moments Some examples of Canny Edge Detection Algorithym can be seen below
- 19. Image Processing via Classical Moments 2. Segmentation a. Edge Detection b. Tresholding Tresholding is a binary
- 20. Image Processing via Classical Moments 2. Segmentation a. Edge Detection b. Tresholding Max{[((k-j)2*h[k])] | (0 J:
- 21. Image Processing via Classical Moments Images of some of the objects, tresholded images and histograms of
- 22. Image Processing via Classical Moments 3. Feature Extraction Hu moments, Legendre moments, ortogonal moments, geometrical moments,
- 23. Image Processing via Classical Moments 4. Object Recognition Object Recognition process can be defined as labeling
- 24. Robot Arm System and Control Motor # 5 Motor # 4 Motor # 3 Motor #
- 25. Robot Arm System and Control First, kinematic analysis and “link table “ of the revolute jointed
- 26. Robot Arm System and Control Second step in forward kinematics is to construct a table to
- 27. Robot Arm System and Control Transformation matrices according to table are,
- 28. Robot Arm System and Control In deriving the kinematical equations, we formed the product of link
- 29. Robot Arm System and Control Px , Py, Pz, can be found with respect to the
- 30. Robot Arm System and Control Before the path control applications, the position following capability of the
- 31. Robot Arm System and Control While planning a trajectory, first two or more points should be
- 32. Robot Arm System and Control After defining these angles, the trajectory of a joint can be
- 33. Robot Arm System and Control As an example the trajectory and parameters of the manipulator are
- 34. Robot Arm System and Control By this table, we can easily determine the needed time of
- 35. After theoretical study, to test the performance of the presented algorithym, an application mechanism is prepared
- 37. Скачать презентацию