An enhancement to SIFT-based techniques for image registration
- Authors: Hossain, Tanvir , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of the 2010 Digital Image Computing: Techniques and Applications p. 166-171
- Full Text: false
- Reviewed:
- Description: Symmetric-SIFT is a recently proposed local technique used for registering multimodal images. It is based on a well-known general image registration technique named Scale Invariant Feature Transform (SIFT). Symmetric SIFT makes use of the gradient magnitude information at the image's key regions to build the descriptors. In this paper, we highlight an issue with how the magnitude information is used in this process. This issue may result in similar descriptors being built to represent regions in images that are visually different. To address this issue, we have proposed two new strategies for weighting the descriptors. Our experimental results show that Symmetric-SIFT descriptors built using our proposed strategies can lead to better registration accuracy than descriptors built using the original Symmetric-SIFT technique. The issue highlighted and the two strategies proposed are also applicable to the general SIFT technique.
Improving SIFT's performance by incorporating appropriate gradient information
- Authors: Lv, Guohua , Hossain, Md. Tanvir , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2011
- Type: Text , Conference paper
- Relation: 26th Image and Vision Computing New Zealand Conference (IVCNZ 2011) p. 381 - 386
- Full Text: false
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- Description: Scale Invariant Feature Transform (SIFT) has been applied in numerous applications especially in the domain of computer vision. In these applications, image information used for building the SIFT descriptor can have a significant impact on its performance. When building orientation histograms for descriptors, a critical step is how to increment the values in the orientation bins. The original scheme for this step in SIFT was improved in [6]. Two different types of gradient information are used for building orientation histograms. The limitations of the two schemes are identified in this paper and we then propose three new schemes which use both types of gradient information in the feature description and matching stages. Our experimental results show that the proposed schemes can achieve better registration performances than the schemes proposed in SIFT and [6].
Improved symmetric-SIFT for Multi-modal image registration
- Authors: Hossain, Md. Tanvir , Lv, Guohua , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2011
- Type: Text , Conference paper
- Relation: 2011 International Conference on Digital Image Computing: Techniques and Applications p. 197-202
- Full Text: false
- Reviewed:
- Description: Multi-modal image registration has received significant research attention over the past decade. SymmetricSIFT is a recently proposed local description technique that can be used for registering multi-modal images. It is based on a well-known general image registration technique named Scale Invariant Feature Transform (SIFT). Symmetric-SIFT, however, achieves this invariance to multi-modality at the cost of losing important information. In this paper, we show how this loss may adversely affect the accuracy of registration results. We then propose an improvement to Symmetric-SIFT to overcome the problem. Our experimental results show that the proposed technique can improve the number of true matches by up to 10 times and overall matching accuracy by up to 30%.
Maximizing structural similarity in multimodal biomedical microscopic images for effective registration
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 IEEE International Conference on Multimedia and Expo (ICME)
- Full Text: false
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- Description: Multimodal image registration (MMIR) is the alignment of contents in images captured from different sensors or instruments. MMIR is important in medical applications as it enables the visualization of the complementary contents in biomedical microscopic images. The registration for such images can be challenging as the structures of their contents are usually only partially similar. Thus in this paper, we propose a new method to maximize the structural similarity of the contents in such images by utilizing intensity relationships among Red-Green-Blue color channels. Our experimental results will demonstrate that our proposed method substantially improves the accuracy of registering such images as compared to the state-of-the-art methods.