Wavelet-Based Image Matching Method
Date Issued
2014
Date
2014
Author(s)
Huang, Pei-Chi
Abstract
With the development of technology in both computer vision and image processing, a variety of image matching algorithms have been gradually proposed. The SIFT algorithm proposed by Lowe (2004) and the SURF algorithm proposed by Bay et al. (2008) belong to a kind of feature-based image matching and are widely used in both computer vision and photogrammetry field. In the stage of feature detection, no matter in SIFT or SURF, there is a great amount of calculation in finding extremes and the thresholds for keypoint localization are determined by experience. Besides, it is time-consuming when doing keypoint detection and producing keypoint descriptors in difference methods in both SIFT and SURF. Wavelet transform is one of the most popular analysis tools of the time-frequency transformation. The basic concept of wavelet multiresolution analysis is very similar to the Gaussian pyramid which is used in SIFT, but wavelet transform can detect the extreme points more accurately and has a better efficiency in searching extreme points. Generally, the wavelet transform is commonly used to detect the distinct features such as corners and edges. In this study, the wavelet transform is used to construct a feature-based image matching method for both detecting keypoints and calculating descriptors. In the experiment of this study, the proposed method is applied on both simulated images and a set of aerial images. The simulated image is used to investigate the characteristic of the keypoints in difference methods. And the aerial images are used to verify the performance of each step in the proposed method. The performance are illustrated comparing with the SIFT and SURF algorithm for least square matching (LSM), solving the relative orientation and epipolar geometry. The LSM results show that the error of conjugate point is about 1 pixel in our method and less then 1 pixel in both SIFT and SURF. But in the relative orientation and epipolar geometry result, our method performs better than SURF algorithm and is close to SIFT algorithm.
Subjects
影像匹配
小波轉換
小波轉換係數極值曲線
點特徵偵測
Type
thesis
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