Homography example sentences

"Homography" Example Sentences

1. Homography is a fundamental concept in computer vision.
2. Understanding homography matrices is crucial for image stitching.
3. The algorithm accurately estimated the homography between the two images.
4. We used a RANSAC algorithm to robustly compute the homography.
5. Accurate camera calibration relies on precise homography estimation.
6. This paper presents a novel method for homography computation.
7. The homography transformation maps points from one plane to another.
8. Errors in homography estimation can lead to inaccurate results.
9. Calculating the homography requires a set of corresponding points.
10. The homography is represented by a 3x3 matrix.
11. A projective transformation is defined by a homography.
12. The application of homography in augmented reality is widespread.
13. Robust homography estimation is a challenging problem.
14. Efficient homography algorithms are highly sought after.
15. Many computer vision tasks utilize homography estimation.
16. Without a correct homography, image registration fails.
17. The homography matrix relates the coordinates in both images.
18. We analyzed the performance of different homography estimation methods.
19. The accuracy of the homography is affected by noise.
20. This technique uses a direct homography computation approach.
21. Advanced homography methods improve image alignment.
22. The homography represents a planar projective transformation.
23. Determining the homography is a key step in many applications.
24. This new algorithm provides faster homography estimation.
25. The homography can be used for image rectification.
26. Our experiments show improved homography accuracy.
27. Image stitching relies heavily on precise homography calculations.
28. The homography is a powerful tool in computer vision.
29. Improving homography robustness is an ongoing research area.
30. The computed homography was then used for image warping.
31. Real-time homography estimation is crucial for many applications.
32. Accurate homography is essential for object tracking.
33. The use of homography simplifies perspective correction.
34. This method is particularly effective for homography estimation in challenging scenarios.
35. The results demonstrate the effectiveness of the proposed homography algorithm.
36. Further research will focus on improving the speed of homography computation.
37. Homography estimation is a well-studied topic.
38. The homography defines the mapping between two images.
39. This thesis explores new approaches to homography estimation.
40. Learning-based methods offer promising solutions for homography estimation.
41. The homography significantly improves image alignment.
42. Understanding projective geometry is essential for mastering homography.
43. Homography estimation is sensitive to outliers.
44. The algorithm’s accuracy is heavily dependent on the quality of the homography.
45. Feature matching precedes homography estimation.
46. We evaluated the homography using various metrics.
47. The homography matrix is a fundamental tool in computer vision.
48. The paper introduces a novel approach to homography decomposition.
49. Different methods exist for homography computation.
50. The challenge lies in robust homography estimation under noisy conditions.