This paper presents an evaluation of two different 3D scanning devices. They can be put into operation depending on the monetary constraints of the user on one hand, and the accuracy needed for a specific application ...
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This paper presents an evaluation of two different 3D scanning devices. They can be put into operation depending on the monetary constraints of the user on one hand, and the accuracy needed for a specific application on the other hand. The setting up of the systems and the process of scanning with the devices are presented. Two setups were formed to evaluate the capabilities of a low costs scanning device together with industrial scanning devices. A comparison of the scans obtained was made by using a Konica Minolta VI-9i industrial scanning solution as a reference. The analysis comprises the estimation of surface flatness, the evaluation of measuring of geometric shapes, and the appraisal of the time required for obtaining a virtual 3D representation of a scanned object. The results show that both the curves as well as the shapes as a whole have been captured accurately.
A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D Quick Randomized Hough Transform (3DGHT), which is based on the 3D Randomized Hough Transform and coars...
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A single-mode background model based on blob analysis is proposed to segment foreground from image sequences in complex environment. Firstly, the symmetric difference is used to extract a rough moving object. Secondly...
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ISBN:
(纸本)9781424425020
A single-mode background model based on blob analysis is proposed to segment foreground from image sequences in complex environment. Firstly, the symmetric difference is used to extract a rough moving object. Secondly, blob analysis is utilized to update background model .Finally, classification strategy (block-level and frame-level) is used to extract foreground accurately and avoid the affect of noise and illumination variance. Experimental results show that the presented approach works well in the presence of complex environment and illumination variance.
To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrö...
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To overcome the main drawbacks of global minimal for active contour models (L. D. Cohen and Ron Kimmel) that the contour is only extracted partially for low SNR images, Method of boundary extraction based on Schrödinger Equation is proposed. Our Method is based on computing the numerical solutions of initial value problem for second order nonlinear Schrödinger equation by using discrete Fourier Transformation. Schrödinger transformation of image is first given. We compute the probability P(b,a) that a particle moves from a point a to another point b according to I-Type Schrödinger transformation of image and obtain boundary of object by using quantum contour model.
A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a...
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ISBN:
(纸本)9781424422944;9781424422951
A novel approach for walking people detection is proposed in this paper, which is inspired by the idea of gait energy image (GEI). Unlike most of common human detection methods where usually a trained detector scans a single image and then generates a detection result, the proposed method detects people on a sequence of silhouettes which contain both appearance characteristics and motion characteristics. Thus, our method is more robust. Encouraging experimental results are obtained based on CASIA gait database and the additional non-human objects data.
Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level prof...
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Finding a new position for each landmark is a crucial step in active shape model (ASM). Mahalanobis distance minimization is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark follow a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a new method support vector machine (SVM) based ASM (SVMBASM) is proposed. It approaches the finding task as a small sample size classification problem, and uses SVM classifier to deal with this problem. Moreover, considering imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is adopted. Performance evaluation on SJTU face database show that the proposed SVMBASM outperforms the original ASM in terms of the average error as well as the average frequency of convergence.
This paper presents a novel method for identity recognition based on the 2D gait representation: Gait Energy image (GEI) which is the averaged silhouette over one gait cycle. An ensemble of Gabor kernels is first conv...
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This paper presents a novel method for identity recognition based on the 2D gait representation: Gait Energy image (GEI) which is the averaged silhouette over one gait cycle. An ensemble of Gabor kernels is first convolved with GEI to extract discriminative feature. The obtained Gabor gait representation is then projected into lower dimensional subspace using discriminative common vectors (DCV) analysis. The final classification is performed in this subspace. The proposed method is tested on the USF HumanID Database. Experimental results show that Gabor-based method can improve recognition rate, and DCV is superior to other traditional dimensional reduction algorithm in the gait recognition application.
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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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 sequence of an action into a Motion History image (MHI) on which low-dimensional features are extracted using subspace analysis methods. Unlike other methods which use a sequence consisting of several frames for recognition, our method uses only a MHI per action sequence for recognition. Obviously, our method avoids the complexity as well as the large computation in sequence matching based methods. Encouraging experimental results on a widely used database demonstrate the effectiveness of the proposed method.
In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if we only consider the image's spectral feature or texture feature alone. In this paper,...
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In classification of a multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if we only consider the image's spectral feature or texture feature alone. In this paper, we present a new approach by applying the Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to classify the multispectral remote sense image. Using the combination of the spectral and texture features obtained by ACO in classification always produces a better accuracy.
Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video...
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Human key posture extraction will benefit for human action recognition, human action retrieval, human behaviour understanding and so on. This paper proposes an approach to select key postures from a human action video based on Radon transform. Cluster is used on the Radon transform to select the final key postures of human action video. The approach does not require motion extraction from the human action video. The experiments results show that the proposed approach is efficient.
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