A robust and geometric invariant digital image watermarking scheme based on robust feature points detector and local Zernike transform is proposed in this paper. The robust feature points detector is proposed based on...
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4D flow MRI is a powerful technique for quantitative flow assessment and visualization of complex flow patterns and hemodynamics of cardiovascular flows. This technique results in more anatomical information and compr...
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ISBN:
(纸本)9781457702150
4D flow MRI is a powerful technique for quantitative flow assessment and visualization of complex flow patterns and hemodynamics of cardiovascular flows. This technique results in more anatomical information and comprehensive assessment of blood flow. However, conventional 4D PC MRI suffers from a few obstacles for clinical applications. The total scan time is long, especially in large volumes with high spatial resolutions. Inaccuracy of conventional Cartesian PC MRI in the setting of atherosclerosis and in general, disturbed and turbulent blood flow is another important challenge. This inaccuracy is the consequence of signal loss, intravoxel dephasing and flow-related artifact in the presence of disturbed and turbulent flow. Spiral k-space trajectory has valuable attributes which can help overcome some of the problems with 4D flow Cartesian acquisitions. Spiral trajectory benefits from shorter TE and reduces the flow-related artifacts. In addition, short spiral readouts with spiral interleaves can significantly reduce the total scan time, reducing the chances of patient motion which may also corrupt the data in the form of motion artifacts. In this paper, the accuracy of flow assessment and flow visualization with reduced TE 4D Spiral PC was investigated and good agreement was observed between the spiral and conventional technique. The systolic mean velocity, peak flow and the average flow in CCA and ICA of normal volunteers using 4D spiral PC MRI showed errors less than 10% compared to conventional 4D PC MRI. In addition, the scan time using spiral sequence was 3:31 min which is half of the time using conventional sequence.
Diffusion Tensor imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for ...
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ISBN:
(纸本)9781479904945
Diffusion Tensor imaging (DTI) is a non-invasive magnetic resonance technique that produces in vivo images of biological tissues with local microstructural characteristics such as water diffusion. It can be used, for example, to localize white matter lesions, or in neuronavigation surgery of brain tumors. Diffusion tensor maps are usually computed on a voxel-by-voxel basis by fitting the signal intensities of diffusion weighted images as a function of their corresponding data acquisition parameters. This processing is highly computation-intensive and can be time-consuming which constraints the clinical use of DTI. This study presents the application of using high performance GPU clusters in diffusion tensor estimation by accelerating the multivariate non-linear regression. The results are tested in simulated DTI brain datasets and show significant performance gain in tensor fitting in addition to favorable scalability characteristics. The proposed GPU implementation framework can further promote the clinical use of DTI, and can be used to accelerate statistical analysis of DTI where Monte Carlo simulations are employed, or readily applied to quantitative assessment of DTI using bootstrap analysis.
This paper presents a Thai font type recognition on Thai document by using Scale-invariant feature transform (SIFT). The features are extracted by Scale-invariant feature transform (SIFT) that is widely used in image ...
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The paper proposes techniques for retrieval and visualization of multimodal data, i.e. documents that contain multiple modalities, such as image, sound etc. A novel cross-modal retrieval framework is proposed, which f...
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ISBN:
(纸本)9780889869219
The paper proposes techniques for retrieval and visualization of multimodal data, i.e. documents that contain multiple modalities, such as image, sound etc. A novel cross-modal retrieval framework is proposed, which fuses the results of unimodal retrieval methods into a multimodal retrieval list, by introducing an estimated cross-modal distance. For the visualization task, a framework for extending existing similarity-based visualization methods for multimodal data is proposed. The similarity between two multimodal objects is calculated as a weighted sum of single modality similarities. The values for the weights of the sum are determined through a semi-supervised user feedback mechanism. Experimental tests of the cross-modal retrieval method show improved performance compared to unimodal approaches and other multimodal ones. Additionally, the results of testing the visualization framework on two existing visualization methods indicate an improvement in the resulting visual data organization when user feedback is allowed.
3D Television enhances viewing experience by adding visual impact to any scene. Generating stereoscopic content from the vast collection of already existing 2D material is less expensive and less time consuming than c...
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3D Television enhances viewing experience by adding visual impact to any scene. Generating stereoscopic content from the vast collection of already existing 2D material is less expensive and less time consuming than creating 3D material using stereoscopic cameras. Depth Image Based Rendering (DIBR) is one of the various approaches for 2D to 3D video conversion. The constraints in selection of depth cues for conversion of existing videos are more restrictive than for images. This paper explains the preference of DIBR over other methods for 3D television. It also provides an in-depth analysis of monocular depth cue constraints and how they can be overcome for generating depth maps which is the primary and crucial step in monoscopic to stereo video conversion for 3D Television.
Ray Casting is a direct volume rendering technique for visualizing 3D arrays of sampled data. It has vital applications in medical and biological imaging. Nevertheless, it is inherently open to cluttered classificatio...
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The ubiquitous visual graphics are valuable because of their brief design that enables the audience to easily access the information they symbolize. As to this phenomenon, we probe into computer image processing and g...
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ISBN:
(纸本)9781467325455
The ubiquitous visual graphics are valuable because of their brief design that enables the audience to easily access the information they symbolize. As to this phenomenon, we probe into computer image processing and graphics recognition from the difference between pre-image and after-image of information field. We analyzed several methods in imagery processing so that we may contribute to theories of image transmission and information visualization.
3D point cloud registration is an essential problem in 3D object and scene understanding. In many realistic circumstances, however, because of noise during data acquisition and large motion between two point clouds, m...
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3D point cloud registration is an essential problem in 3D object and scene understanding. In many realistic circumstances, however, because of noise during data acquisition and large motion between two point clouds, most existing approaches can hardly work satisfactorily without good initial alignment or manually marked correspondences. Inspired by the popular kernel methods in machine learning community, this paper puts forward a general point cloud registration framework by constructing kernel functions over 3D point clouds. More specifically, Gaussian mixtures Based on the point clouds are established and probability product kernel functions are exploited for the registration. To enhance the generality of the framework, SE(3) on-manifold optimization scheme is employed to compute the optimal motion. Experimental results show that our registration framework works robustly when many outliers are presented and motion between point clouds is relatively large, and compares favorably to related methods.
Reconstruction of 3D objects Based on images is useful in many applications. One of the methods Based on multi-image data is the Photometric Stereo technique relying on several photographs of the observed object from ...
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Reconstruction of 3D objects Based on images is useful in many applications. One of the methods Based on multi-image data is the Photometric Stereo technique relying on several photographs of the observed object from the same point of view, each one taken under a different illumination condition. The common approach is to estimate the gradient field of the surface by minimizing a functional, integrating the distance from the camera and thereby obtaining the geometry of the observed object. We propose an alternative method that consists of a novel differential approach for multi-image Photometric Stereo and permits a direct solution of a novel PDE Based model without going through the gradient field while naturally dealing with shadowed regions. The mathematical well-posed ness of the problem in terms of numerical stability yields a fast algorithm that efficiently converges, even for pictures of sizes in the order of several mega pixels affected by noise.
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