Aiming at adverse influence of the correlation between measurement and process noise for filtering precision, a new multiple model particle filtering algorithm with correlated measurement noise and process noise is pr...
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The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement a...
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The skeleton of an image object is a simplified representation, which is of great significance for the imagerecognition and matching. To obtain a smooth and accurate skeleton of a specified object in the gray image, ...
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A new algorithm for removing random-valued impulse noise is *** use a standardized version of the Rank Ordered Absolute Differences statistic of Garnett et al. [1] to attribute weights to noisy pixels. These weights a...
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Automatically assessment of photo quality in different categories is of great interest in the recent years. And there are many researches work on it. In this paper, we use several new global and regional features to d...
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Automatically assessment of photo quality in different categories is of great interest in the recent years. And there are many researches work on it. In this paper, we use several new global and regional features to describe the photo quality. In one image, there is an area where attract the most attention of humans eyes. We call this area subject area. We use two different methods to extract the subject area while dealing with different kinds of images. Then we extract the regional feature from the subject area and background separately. Photos taken by professional photographer have obvious difference between subject area and background. We combine global features and regional features, and our method performs quite well in the database.
In this paper, we propose a robust visual tracking algorithm based on online learning of a joint sparse dictionary. The joint sparse dictionary consists of positive and negative sub-dictionaries, which model foregroun...
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It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracki...
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image segmentation is a fundamental process in many image, video, and computer vision applications. It is very essential and critical to imageprocessing and patternrecognition, and determines the quality of final re...
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ISBN:
(纸本)9781479927654
image segmentation is a fundamental process in many image, video, and computer vision applications. It is very essential and critical to imageprocessing and patternrecognition, and determines the quality of final result of analysis and recognition. This paper presents a semi-supervised strategy to deal with the issue of image segmentation. Each image is first segmented coarsely, and represented as a graph model. Then, a semi-supervised algorithm is utilized to estimate the relevance between labeled nodes and unlabeled nodes to construct a relevance matrix. Finally, a normalized cut criterion is utilized to segment images into meaningful units. The experimental results conducted on Berkeley image databases and MSRC image databases demonstrate the effectiveness of the proposed strategy.
A contour segmentation algorithm is proposed based on GVF Snake model and Contourlet transform. Firstly, object contours of images can be obtained based on Contourlet Transform, and those contours will be identified a...
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Interior tomography is to reconstruct the interior region of interest (ROI) from the projection data just across the ROI. One kind of interior reconstruction methods is based on the inversion of truncated Hilbert tran...
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Interior tomography is to reconstruct the interior region of interest (ROI) from the projection data just across the ROI. One kind of interior reconstruction methods is based on the inversion of truncated Hilbert transform (THT) when there is a known sub-region inside ROI. However, the result via this method is usually to be degraded by noise in real data case. In this paper, we propose to incorporate the total variation (TV) minimization constraint into the THT-based interior tomography to improve the reconstruction quality. Therein, we first carry out projection-on-convex-sets (POCS) iteration on each chord, and then we perform a soft-threshold based TV minimization on the intermediate image. In order to validate the proposed method, we conduct both simulated and real data experiments. The results show that with TV constraint the proposed method can lead to better ROI with less noise.
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