The Experts below are selected from a list of 153 Experts worldwide ranked by ideXlab platform
Yi Fang - One of the best experts on this subject based on the ideXlab platform.
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learning a discriminative Deformation Invariant 3d shape descriptor via many to one encoder
Pattern Recognition Letters, 2016Co-Authors: Guoxian Dai, Jin Xie, Fan Zhu, Yi FangAbstract:Developing a global shape descriptor using locality-constrained linear coding.Learning a discriminative 3D shape description.Proposed shape descriptor got high performance in the shape retrieval task. Display Omitted Recent advances in 3D acquisition techniques have led to a rapid increase in the size of database of three dimensional (3D) models across areas as diverse as engineering, medicine and biology, etc. Therefore, developing an efficient shape retrieval method has been attracting more and more attention in recent years. In this paper, we have developed a novel learning paradigm for extracting a concise data-driven shape descriptor to address challenging issues posed by structural Deformation variations and noise present in 3D models. First, we use the scale Invariant heat kernel signature (SIHKS) to describe the vertex of the shape. The locality-constrained linear coding (LLC) is employed to encode each vertex of the shape to form the global shape representation. Then we develop a discriminative shape descriptor for retrieval using many-to-one encoder. Our proposed shape descriptor is extensively evaluated on three well-known benchmark datasets including McGill, SHREC'10 ShapeGoogle and SHREC'14 human. Experimental results on 3D shape retrieval demonstrate the superior performance of our proposed method over the state-of-the-art methods and is robust to large Deformations.
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IDSS: Deformation Invariant signatures for molecular shape comparison
BMC bioinformatics, 2009Co-Authors: Yu-shen Liu, Yi Fang, Karthik RamaniAbstract:Many molecules of interest are flexible and undergo significant shape Deformation as part of their function, but most existing methods of molecular shape comparison (MSC) treat them as rigid bodies, which may lead to incorrect measure of the shape similarity of flexible molecules. To address the issue we introduce a new shape descriptor, called Inner Distance Shape Signature (IDSS), for describing the 3D shapes of flexible molecules. The inner distance is defined as the length of the shortest path between landmark points within the molecular shape, and it reflects well the molecular structure and Deformation without explicit decomposition. Our IDSS is stored as a histogram which is a probability distribution of inner distances between all sample point pairs on the molecular surface. We show that IDSS is insensitive to shape Deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. Our approach reduces the 3D shape comparison problem of flexible molecules to the comparison of IDSS histograms. The proposed algorithm is robust and does not require any prior knowledge of the flexible regions. We demonstrate the effectiveness of IDSS within a molecular search engine application for a benchmark containing abundant conformational changes of molecules. Such comparisons in several thousands per second can be carried out. The presented IDSS method can be considered as an alternative and complementary tool for the existing methods for rigid MSC. The binary executable program for Windows platform and database are available from https://engineering.purdue.edu/PRECISE/IDSS .
Tom Macgillivray - One of the best experts on this subject based on the ideXlab platform.
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dicyc gan based Deformation Invariant cross domain information fusion for medical image synthesis
Information Fusion, 2021Co-Authors: Chengjia Wang, Giorgos Papanastasiou, Sotirios A Tsaftaris, Guang Yang, Calum Gray, David E Newby, Gillian Macnaught, Tom MacgillivrayAbstract:Abstract Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative Deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a Deformation Invariant cycle-consistency model that can filter out these domain-specific Deformation. The Deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific Deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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tpsdicyc improved Deformation Invariant cross domain medical image synthesis
Second International Workshop MLMIR 2019 Held in Conjunction with MICCAI 2019, 2019Co-Authors: Chengjia Wang, Giorgos Papanastasiou, Sotirios A Tsaftaris, Guang Yang, Calum Gray, David E Newby, Gillian Macnaught, Tom MacgillivrayAbstract:Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative Deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a Deformation Invariant model based on the Deformation-Invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative Deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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unsupervised learning for cross domain medical image synthesis using Deformation Invariant cycle consistency networks
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Chengjia Wang, Giorgos Papanastasiou, Gillian Macnaught, Tom Macgillivray, David E NewbyAbstract:Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear Deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a Deformation-Invariant CycleGAN (DicycleGAN) method using deformable convolutional layers and new cycle-consistency losses. Its robustness dealing with data that suffer from domain-specific nonlinear Deformations has been evaluated through comparison experiments performed on a multi-sequence brain MR dataset and a multi-modality abdominal dataset. Our method has displayed its ability to generate synthesized data that is aligned with the source while maintaining a proper quality of signal compared to CycleGAN-generated data. The proposed model also obtained comparable performance with CycleGAN when data from the source and target domains are alignable through simple affine transformations.
Cheng Zhong - One of the best experts on this subject based on the ideXlab platform.
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3d face recognition by constructing Deformation Invariant image
Pattern Recognition Letters, 2008Co-Authors: Wei Tang, Cheng ZhongAbstract:Based on the observation that facial surfaces across different expressions can be modeled as similar isometric transformations, in this paper a novel Deformation Invariant image for robust 3D face recognition is proposed. First, we obtain the depth image and the intensity image from the original 3D facial data. Then, geodesic level curves are generated by constructing radial geodesic distance image from the depth image. Finally, Deformation Invariant image is constructed by evenly sampling points from the selected geodesic level curves in the intensity image. Our experiments are based on the 3D CASIA Face Database, which includes 123 individuals with complex expressions. Experimental results show that our proposed method substantially improves the recognition performance under various facial expressions.
Paul Suetens - One of the best experts on this subject based on the ideXlab platform.
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isometric Deformation Invariant 3d shape recognition
Pattern Recognition, 2012Co-Authors: Dirk Smeets, Jeroen Hermans, Dirk Vandermeulen, Paul SuetensAbstract:Intra-shape Deformations complicate 3D shape recognition and therefore need proper modeling. Thereto, an isometric Deformation model is used in this paper. The method proposed does not need explicit point correspondences for the comparison of 3D shapes. The geodesic distance matrix is used as an isometry-Invariant shape representation. Two approaches are described to arrive at a sampling order Invariant shape descriptor: the histogram of geodesic distance matrix values and the set of largest singular values of the geodesic distance matrix. Shape comparison is performed by comparison of the shape descriptors using the @g^2-distance as dissimilarity measure. For object recognition, the results obtained demonstrate the singular value approach to outperform the histogram-based approach, as well as the state-of-the-art multidimensional scaling technique, the ICP baseline algorithm and other isometric Deformation modeling methods found in literature. Using the TOSCA database, a rank-1 recognition rate of 100% is obtained for the identification scenario, while the verification experiments are characterized by a 1.58% equal error rate. External validation demonstrates that the singular value approach outperforms all other participants for the non-rigid object retrieval contests in SHREC 2010 as well as SHREC 2011. For 3D face recognition, the rank-1 recognition rate is 61.9% and the equal error rate is 11.8% on the BU-3DFE database. This decreased performance is attributed to the fact that the isometric Deformation assumption only holds to a limited extent for facial expressions. This is also demonstrated in this paper.
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inelastic Deformation Invariant modal representation for non rigid 3d object recognition
Articulated Motion and Deformable Objects, 2010Co-Authors: Dirk Smeets, Thomas Fabry, Jeroen Hermans, Dirk Vandermeulen, Paul SuetensAbstract:Intra-shape Deformations complicate 3D object recognition and retrieval and need therefore proper modeling. A method for inelastic Deformation Invariant object recognition is proposed, representing 3D objects by diffusion distance tensors (DDT), i.e. third order tensors containing the average diffusion distance for different diffusion times between each pair of points on the surface. In addition to the DDT, also geodesic distance matrices (GDM) are used to represent the objects independent of the reference frame. Transforming these distance tensors into modal representations provides a sampling order Invariant shape descriptor. Different dissimilarity measures can be used for comparing these shape descriptors. The final object pair dissimilarity is the sum or product of the dissimilarities obtained by modal representations of the GDM and DDT. The method is validated on the TOSCA non-rigid world database and the SHREC 2010 dataset of non-rigid 3D models indicating that our method combining these two representations provides a more noise robust but still inter-subject shape variation sensitive method for the identification and the verification scenario in object retrieval.
Chengjia Wang - One of the best experts on this subject based on the ideXlab platform.
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dicyc gan based Deformation Invariant cross domain information fusion for medical image synthesis
Information Fusion, 2021Co-Authors: Chengjia Wang, Giorgos Papanastasiou, Sotirios A Tsaftaris, Guang Yang, Calum Gray, David E Newby, Gillian Macnaught, Tom MacgillivrayAbstract:Abstract Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative Deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a Deformation Invariant cycle-consistency model that can filter out these domain-specific Deformation. The Deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific Deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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tpsdicyc improved Deformation Invariant cross domain medical image synthesis
Second International Workshop MLMIR 2019 Held in Conjunction with MICCAI 2019, 2019Co-Authors: Chengjia Wang, Giorgos Papanastasiou, Sotirios A Tsaftaris, Guang Yang, Calum Gray, David E Newby, Gillian Macnaught, Tom MacgillivrayAbstract:Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative Deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a Deformation Invariant model based on the Deformation-Invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative Deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
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unsupervised learning for cross domain medical image synthesis using Deformation Invariant cycle consistency networks
arXiv: Computer Vision and Pattern Recognition, 2018Co-Authors: Chengjia Wang, Giorgos Papanastasiou, Gillian Macnaught, Tom Macgillivray, David E NewbyAbstract:Recently, the cycle-consistent generative adversarial networks (CycleGAN) has been widely used for synthesis of multi-domain medical images. The domain-specific nonlinear Deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a Deformation-Invariant CycleGAN (DicycleGAN) method using deformable convolutional layers and new cycle-consistency losses. Its robustness dealing with data that suffer from domain-specific nonlinear Deformations has been evaluated through comparison experiments performed on a multi-sequence brain MR dataset and a multi-modality abdominal dataset. Our method has displayed its ability to generate synthesized data that is aligned with the source while maintaining a proper quality of signal compared to CycleGAN-generated data. The proposed model also obtained comparable performance with CycleGAN when data from the source and target domains are alignable through simple affine transformations.