Enhancing image registration performance by incorporating distribution and spatial distance of local descriptors
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 103, no. (2018), p. 46-52
- Full Text: false
- Reviewed:
- Description: A data dependency similarity measure called mp-dissimilarity has been recently proposed. Unlike ℓp-norm distance which is widely used in calculating the similarity between vectors, mp-dissimilarity takes into account the relative positions of the two vectors with respect to the rest of the data. This paper investigates the potential of mp-dissimilarity in matching local image descriptors. Moreover, three new matching strategies are proposed by considering both ℓp-norm distance and mp-dissimilarity. Our proposed matching strategies are extensively evaluated against ℓp-norm distance and mp-dissimilarity on a few benchmark datasets. Experimental results show that mp-dissimilarity is a promising alternative to ℓp-norm distance in matching local descriptors. The proposed matching strategies outperform both ℓp-norm distance and mp-dissimilarity in matching accuracy. One of our proposed matching strategies is comparable to ℓp-norm distance in terms of recall vs 1-precision. © 2018 Elsevier B.V.
COREG : A corner based registration technique for multimodal images
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: Multimedia Tools and Applications Vol. 77, no. 10 (2018), p. 12607-12634
- Full Text: false
- Reviewed:
- Description: This paper presents a COrner based REGistration technique for multimodal images (referred to as COREG). The proposed technique focuses on addressing large content and scale differences in multimodal images. Unlike traditional multimodal image registration techniques that rely on intensities or gradients for feature representation, we propose to use contour-based corners. First, curvature similarity between corners are for the first time explored for the purpose of multimodal image registration. Second, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the edges in the neighborhood of corners. Third, a simple yet effective way of estimating scale difference is proposed by making use of geometric relationships between corner triplets from the reference and target images. Using a set of benchmark multimodal images and multimodal microscopic images, we will demonstrate that our proposed technique outperforms a state-of-the-art multimodal image registration technique. © 2017, Springer Science+Business Media, LLC.
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.
Integrated generalized zero-shot learning for fine-grained classification
- Authors: Shermin, Tasfia , Teng, Shyh , Sohel, Ferdous , Murshed, Manzur , Lu, Guojun
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 122, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine details in the absence of local features. On the other hand, EL or FS methods exploiting local features either neglect direct attribute guidance or global information. Consequently, neither method performs well. In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL. The proposed integrated network has an EL sub-network and a FS sub-network. Consequently, the proposed integrated network can be tested in two ways. We propose a novel two-step dense attention mechanism to discover attribute-guided local visual features. We introduce new mutual learning between the sub-networks to exploit mutually beneficial information for optimization. Moreover, we propose to compute source-target class similarity based on mutual information and transfer-learn the target classes to reduce bias towards the source domain during testing. We demonstrate that our proposed method outperforms contemporary methods on benchmark datasets. © 2021 Elsevier Ltd
Multimodal image registration technique based on improved local feature descriptors
- Authors: Teng, Shyh , Hossain, Tanvir , Lu, Guojun
- Date: 2015
- Type: Text , Journal article
- Relation: Journal of Electronic Imaging Vol. 24, no. 1 (2015), p.
- Full Text:
- Reviewed:
- Description: Multimodal image registration has received significant research attention over the past decade, and the majority of the techniques are global in nature. Although local techniques are widely used for general image registration, there are only limited studies on them for multimodal image registration. Scale invariant feature transform (SIFT) is a well-known general image registration technique. However, SIFT descriptors are not invariant to multimodality. We propose a SIFT-based technique that is modality invariant and still retains the strengths of local techniques. Moreover, our proposed histogram weighting strategies also improve the accuracy of descriptor matching, which is an important image registration step. As a result, our proposed strategies can not only improve the multimodal registration accuracy but also have the potential to improve the performance of all SIFT-based applications, e.g., general image registration and object recognition.
Effective and efficient contour-based corner detectors
- Authors: Teng, Shyh , Najmus Sadat, Rafi , Lu, Guojun
- Date: 2015
- Type: Text , Journal article
- Relation: Pattern Recognition Vol. 48, no. 7 (2015), p. 2185-2197
- Full Text: false
- Reviewed:
- Description: Corner detection is an essential operation in many computer vision applications. Among the contour-based corner detectors in the literature, the Chord-to-Point Distance Accumulation (CPDA) detector is reported to have one of the highest repeatability in detecting robust corners and the lowest localization error. However, based on our analysis, we found that the CPDA detector often fails to accurately detect the true corners when a curve has multiple corners but the sharpness of one or a few of them is much more prominent than the rest. This detector also might not perform well when the corners are closely located. Furthermore, the CPDA detector is also computationally very expensive. To overcome these weaknesses, we propose two effective and efficient corner detectors using simple triangular theory and distance calculation. Our experimental results show that our proposed detectors outperform CPDA and nine other existing corner detectors in terms of repeatability. Our proposed detectors also have a relatively low or comparable localization error and are computationally more efficient. © 2015 Elsevier Ltd.
Feature-subspace aggregating: ensembles for stable and unstable learners
- Authors: Ting, Kaiming , Wells, Jonathan , Tan, Swee , Teng, Shyh , Webb, Geoffrey
- Date: 2011
- Type: Text , Journal article
- Relation: Machine Learning Vol. 82, no. 3 (2011), p. 375-397
- Full Text: false
- Reviewed:
- Description: This paper introduces a new ensemble approach, Feature-Subspace Aggregating (Feating), which builds local models instead of global models. Feating is a generic ensemble approach that can enhance the predictive performance of both stable and unstable learners. In contrast, most existing ensemble approaches can improve the predictive performance of unstable learners only. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble through an increased level of localisation in Feating. Our empirical evaluation shows that Feating performs significantly better than Boosting, Random Subspace and Bagging in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by Feating makes feasible SVM ensembles that would otherwise be infeasible for large data sets. When SVM is the preferred base learner, we show that Feating SVM performs better than Boosting decision trees and Random Forests. We further demonstrate that Feating also substantially reduces the error of another stable learner, k-nearest neighbour, and an unstable learner, decision tree.