발간년도 : [2014]
| 논문정보 |
|
| 논문명(한글) |
[Vol.9, No.6] Noise-Insenstive Wavelet Transformation-based Harris Corner Detection Algorithm |
|
| 논문투고자 |
Nam-Oh Kang, Jae-Ho Kim |
|
| 논문내용 |
The massive amounts of data and the irregularity of images are big obstacles in constructing image-based applications. To solve the problems, feature points of images are widely used. The extraction of robust feature points from images is one of the fundamental operations required in image processing. Corners in images represent a lot of important information. Hence, extracting corners accurately is significant to image processing as well as reducing much of the calculations. Harris corner detection algorithm, among many corner detection algorithms, is well known for extracting robust corners. It is used to improve feature description algorithms such as Scale-invariant feature transform(SIFT) and Speeded Up Robust Feature(SURF). It is also employed in the development of various image processing applications such as object segmentation, object tracking, image recognition, and image registration etc. Therefore, a lot of research has been conducted to improve the Harris corner detection algorithm. In this paper, we proposed a noise-insenstive wavelet-based Harris corner detection algorithm which can extract more resilient corners and more rapidly than the Harris corner detection algorithm, by combining Wavelet transformation and Harris corner detection algorithm and making them complementary. Experiments were performed using Lena image and they showed that the proposed technique produces more robust salient points and more rapidly than Harris corner detection algorithm does. |
|
| 첨부논문 |
|
|
|
|
|
자료실 > 논문지 
