This paper proposes a multiple facial feature interface that allows disabled users with various disabilities to implement different mouse operations. Using a regular PC camera, the proposed system detects the user'...
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This paper proposes a multiple facial feature interface that allows disabled users with various disabilities to implement different mouse operations. Using a regular PC camera, the proposed system detects the user's eye and mouth movements, and then interprets the communication intent to control the computer. Here, mouse movements are implemented based on the user's eye movements, while clicking events are implemented based on the user's mouth shapes, such as opening/closing. The proposed system is composed of three modules: facial feature detector, facial feature tracker, and mouse controller. The facial region is initially identified using a skin-color model and connected-component (CC) analysis. Thereafter, the eye regions are localized using a neural network (NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, then the mouth region is localized using an edge detector. Once the eye and mouth regions are localized, they are continuously and accurately tracking using a mean-shift algorithm and template matching. respectively. Based on the tracking results, the mouse movements and clicks are then implemented. To assess the validity of the proposed method, it was applied to three applications: a web browser, 'spelling board', and the game 'catching-a-bird'. The two test groups involved 34 users, and the results showed that the proposed system could be efficiently and effectively applied as a user-friendly and convenient communication device. Crown Copyright (C) 2008 Published by Elsevier B.V. All rights reserved.
The tracking of storm centres in radar data is of particular importance for short term weather prediction and specifically thunderstorm prediction. This paper presents a method to track storm centres in terrestrial ra...
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ISBN:
(纸本)9781424415380
The tracking of storm centres in radar data is of particular importance for short term weather prediction and specifically thunderstorm prediction. This paper presents a method to track storm centres in terrestrial radar images. meanshift segmentation is used to outline storm centres and meanshift tracking to locate the storm in the consecutive images. Re sults demonstrate the ability of the method to deal with deformable objects such as storm centres. Moreover the method is able to handle the splitting and merging of the convective cells.
This paper describes a new color image segmentation method based on low-level features including color, texture and spatial information. The mean-shift algorithm with color and spatial information in color image segme...
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This paper describes a new color image segmentation method based on low-level features including color, texture and spatial information. The mean-shift algorithm with color and spatial information in color image segmentation is in general successful, however, in some cases, the color and spatial information are not sufficient for superior segmentation. The proposed method addresses this problem and employs texture descriptors as an additional feature. The method uses wavelet frames that provide translation invariant texture analysis. The method integrates additional texture feature to the color and spatial space of standard mean-shift segmentation algorithm. The new algorithm with high dimensional extended feature space provides better results than standard mean-shift segmentation algorithm as shown in experimental results. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Areal-time image-tracking algorithm is proposed,which gives small weights to pixels farther from the object center and uses the quantized image gray scales as a *** identifies the target’s location by the mean-shift ...
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Areal-time image-tracking algorithm is proposed,which gives small weights to pixels farther from the object center and uses the quantized image gray scales as a *** identifies the target’s location by the mean-shift iteration method and arrives at the target’s scale by using image feature *** improves the kernel-based algorithm in tracking scale-changing targets.A decimation method is proposed to track large-sized targets and real-time experimental results verify the effectiveness of the proposed algorithm.
This paper presents an object tracking framework based on the mean-shift algorithm, which is a nonparametric technique that uses statistical color distribution of objects. Tracking objects through highly similar-color...
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This paper presents an object tracking framework based on the mean-shift algorithm, which is a nonparametric technique that uses statistical color distribution of objects. Tracking objects through highly similar-colored background is one of the problems that need to be addressed. In various cases where object and background color distributions are very similar, the color distribution obtained from single frame alone is not sufficient to track objects reliably. To deal with this problem, the proposed algorithm utilizes an adaptive statistical background and foreground modeling to detect the change due to motion using kernel density estimation techniques based on multiple recent frames. The use of multiple frames supplies more information than single frame and thus it provides more accurate modeling of both background and foreground. In addition to color distribution, this statistical multiple frame-based motion representation is integrated into a modified mean-shift algorithm to create more robust object tracking framework. The use of motion distribution provides additional discriminative power to the framework. The superior performance with quantitative results of the framework has been validated using experiments on synthetic and real sequence of images.
We introduce a nonparametric time-dynamic kernel type density estimate for the situation where an underlying multivariate distribution evolves with time. Based on this time-dynamic density estimate, we propose nonpara...
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We introduce a nonparametric time-dynamic kernel type density estimate for the situation where an underlying multivariate distribution evolves with time. Based on this time-dynamic density estimate, we propose nonparametric estimates for the time-dynamic mode of the underlying distribution. Our estimators involve boundary kernels for the time dimension so that the estimator is always centered at current time, and multivariate kernels for the spatial dimension of the time-evolving distribution. Under certain mild conditions, the asymptotic behavior of density and mode estimators, especially their uniform convergence in both time and space, is derived. A time-dynamic algorithm for mode tracking is proposed, including automatic bandwidth choices, and is implemented via a mean update algorithm. Simulation studies and real data illustrations demonstrate that the proposed methods work well in practice.
The meanshiftalgorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimate...
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ISBN:
(纸本)0769523722
The meanshiftalgorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the meanshiftalgorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.
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