A novel multi-modal image registration method based on corners
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, Wollongong, New South Wales, 25th-27th November 2014
- Full Text: false
- Description: This paper presents a novel method for registering multi-modal images, based on corners. The proposed method is motivated by the fact that large content differences are likely to occur in multi-modal images. Unlike traditional multi-modal image registration methods that utilize intensities or gradients for feature representation, we propose to use curvatures of corners. Moreover, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the neighborhood of corners. Curvature and DEPAC similarities are combined in our method to improve the registration accuracy. Using a set of benchmark multi-modal images and multi-modal microscopic images, we demonstrate that our proposed method outperforms an existing state-of-the-art image registration method.
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
- Reviewed:
- 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].
Enhancing the effectiveness of local descriptor based image matching
- Authors: Hossain, Md Tahmid , Teng, Shyh , Zhang, Dengsheng , Lim, Suryani , Lu, Guojun
- Date: 2018
- Type: Text , Conference proceedings , Conference paper
- Relation: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018; Canberra, Australia; 10th-13th December 2018 p. 1-8
- Full Text: false
- Reviewed:
- Description: Image registration has received great attention from researchers over the last few decades. SIFT (Scale Invariant Feature Transform), a local descriptor-based technique is widely used for registering and matching images. To establish correspondences between images, SIFT uses a Euclidean Distance ratio metric. However, this approach leads to a lot of incorrect matches and eliminating these inaccurate matches has been a challenge. Various methods have been proposed attempting to mitigate this problem. In this paper, we propose a scale and orientation harmony-based pruning method that improves image matching process by successfully eliminating incorrect SIFT descriptor matches. Moreover, our technique can predict the image transformation parameters based on a novel adaptive clustering method with much higher matching accuracy. Our experimental results have shown that the proposed method has achieved averages of approximately 16% and 10% higher matching accuracy compared to the traditional SIFT and a contemporary method respectively.
- Description: 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
A detector of structural similarity for multi-modal microscopic image registration
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: Multimedia Tools and Applications Vol. 77, no. 6 (2018), p. 7675-7701
- Full Text: false
- Reviewed:
- Description: This paper presents a Detector of Structural Similarity (DSS) to minimize the visual differences between brightfield and confocal microscopic images. The context of this work is that it is very challenging to effectively register such images due to a low structural similarity in image contents. To address this issue, DSS aims to maximize the structural similarity by utilizing the intensity relationships among red-green-blue (RGB) channels in images. Technically, DSS can be combined with any multi-modal image registration technique in registering brightfield and confocal microscopic images. Our experimental results show that DSS significantly increases the visual similarity in such images, thereby improving the registration performance of an existing state-of-the-art multi-modal image registration technique by up to approximately 27%. © 2017, Springer Science+Business Media New York.
Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2016
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 84, no. (2016), p. 156-162
- Full Text: false
- Reviewed:
- Description: In this paper we will investigate the gradient utilization in building SIFT (Scale Invariant Feature Transform)-like descriptors for image registration. There are generally two types of gradient information, i.e. gradient magnitude and gradient occurrence, which can be used for building SIFT-like descriptors. We will provide a theoretical analysis on the effectiveness of each of the two types of gradient information when used individually. Based on our analysis, we will propose a novel technique which systematically uses both types of gradient information together for image registration. Moreover, we will propose a strategy to select keypoint matches with a higher discrimination. The proposed technique can be used for both mono-modal and multi-modal image registration. Our experimental results show that the proposed technique improves registration accuracy over existing SIFT-like descriptors. © 2016 Elsevier B.V.