In this article, we explain why and how to identify the projected sphere center, i.e. the projection of the sphere center, in passive-mode based optical tracking systems using infrared reflective spheres as markers. W...
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In this article, we explain why and how to identify the projected sphere center, i.e. the projection of the sphere center, in passive-mode based optical tracking systems using infrared reflective spheres as markers. We first present the algebraic representation of the 'deviation', defined by their Euclidian distance in the image coordinate system, between the projected sphere center and the center of the elliptical contour of the sphere's image, and show that the common approximation to substitute the later for the former is not always appropriate in terms of accuracy. Then, we give the projective equation of a sphere in matrix form, thus paving the way for the linear estimation of the projected sphere center. Sufficient experiments indicate that this proposed method enlarges the manipulating volume of the optical tracking system and improves the precision of locating surgical instruments.
Medical imaging techniques like computed/digital radiography (CR/DR) have introduced a formidably powerful tool in medicine. image enhancement takes an important roll in the CR/DR computerized analysis process. Much e...
Medical imaging techniques like computed/digital radiography (CR/DR) have introduced a formidably powerful tool in medicine. image enhancement takes an important roll in the CR/DR computerized analysis process. Much effort has been put into the area of image enhancement. However, conventional multi-scale methods have the drawback of the introduction of severe visible artifacts while large structures are enhanced strongly. This paper presents a nonlinear multi-scale medical image contrast enhancement method for the improvement of medical image quality. More specifically, a novel nonlinear enhancement function is proposed incorporated with human visual local perceptual contrast. The proposed work provides the advantages of enhancing or preserving image contrast while suppressing visible artifacts. To quantitatively compare the performance of the proposed method, the average local variances are used as comparison criteria. Results demonstrate the superiority of the proposed method. Our results show that the proposed method has the potential to become useful for improvement of image quality of medical images.
Recently, extensive research and application on contrast enhancement of radiographs based on multi-scale decomposition of the images have validated its higher performance than regular techniques. However, to some exte...
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Recently, extensive research and application on contrast enhancement of radiographs based on multi-scale decomposition of the images have validated its higher performance than regular techniques. However, to some extent, conventional multi-scale methods suffered from the introduction of visible artifacts. In this work, we present an algorithm for nonlinear chest radiograph contrast enhancement algorithm within the multi-scale decomposition architecture in spatial domain. In particular, one kind of nonlinear enhancement function is designed by exploiting local contrast information. The main contribution of this model is the local adaptive enhancement ability, which can avoid visible artifacts, while keeping the same detail enhancement ability. In the meantime, no excessive noise is amplified, comparing to conventional methods. Finally, an evaluation using a chest image is provided to demonstrate the effectiveness of the proposed algorithm.
In this paper, a universal full-reference (FR) image quality metric based on Edge structure similarity (QMESS) is proposed using spatial position displacement degree of wavelet transform modulus maxima between referen...
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In this paper, a universal full-reference (FR) image quality metric based on Edge structure similarity (QMESS) is proposed using spatial position displacement degree of wavelet transform modulus maxima between reference image and distorted image in multi-resolution domain. Firstly, we decompose images in wavelet domain. The structure error between reference images and distorted images is computed based on the statistics of spatial position error of local modulus maxima in wavelet domain. At the same time, peak signal to noise ratio (PSNR) is adopted to evaluate the stochastic noise in images. Finally, the low frequency resolution layer distortion is evaluated by means of the mutual information and the luminance distortion. The three components are combined for the whole visual distortion measurement. From the experiment results, the proposed metric is much better than conventional PSNR method and the state-of-the-art SSIM approach in terms of the performance relative to subjective judgment. Comparing to the excellent VIF method, the proposed method performs better in individual distortions and obtains similar results on cross-distortion type.
This paper presents a new feature extraction method for iris recognition. Since two dimensional complex wavelet transform (2D-CWT) does not only keep wavelet transform's properties of multiresolution decomposition...
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A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relat...
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A closed form solution to the problem of segmenting multiple 3D motion models was proposed from straight-line optical flow. It introduced the multibody line optical flow constraint (MLOFC), a polynomial equation relating motion models and line parameters. The motion models can be obtained analytically as the derivative of the MLOFC at the corresponding line measurement, without knowing the motion model associated with that line. Experiments on real and synthetic sequences were also presented.
Human matching is fundamental in human tracking over non-overlapping cameras. Fusing multiple features is an efficient way to increase the ratio of matching. In this paper, we present an algorithm of Iterative Widenin...
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This paper proposes a new human action recognition method which deals with recognition task in a quite different way when compared with traditional methods which use sequence matching scheme. Our method compresses a s...
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Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the...
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Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities.
With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint (GPU) shading functions recently introduced in graphic cards and is fast enough to interact oil the segmented area, which was not possible with previous methods.
Digitizing large-surface paintings at a high resolution in museums is necessary in the field of painting conservation to document the actual condition of paintings (e.g. colour measurements) and for analysis (e.g. to ...
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Digitizing large-surface paintings at a high resolution in museums is necessary in the field of painting conservation to document the actual condition of paintings (e.g. colour measurements) and for analysis (e.g. to analyse underdrawings in high-resolution infrared images). Hence, this paper presents a portable system that is able to scan large-surface paintings or images to provide high-resolution digital images. Additional a 3D-model of the surface of the scanned object is generated. The purpose of the 3D-model is to use the 3D-information to create a planar representation of a once planar painting or image that was deformed through environmental effects. Otherwise uncorrected images lead to blurring in the overlap region of the blended subimages. The resolution of the final image is up to 33 pixel/mm and the size of the scanned object is up to 1.5 m x 1.3 m.
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