A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion

Xiao, Gang; Xiao, Yun; Wu, Jianmin

doi:10.1007/s11432-012-4553-3

Cited by 16 publications

(7 citation statements)

References 11 publications

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“…Second, we noticed that many literatures only use one of traditional image algorithms [10][11][12][13][14][15] or deep learning [26,50,51], failing to fully combine the advantages of both. For the challenges existing in the lung segmentation of preschool children, we combined traditional image algorithms and deep learning as solutions.…”

Section: Discussionmentioning

confidence: 99%

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Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet

Shu

et al. 2022

Computational and Mathematical Methods in Medicine

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Lung segmentation using computed tomography (CT) images is important for diagnosing various lung diseases. Currently, no lung segmentation method has been developed for assessing the CT images of preschool children, which may differ from those of adults due to (1) presence of artifacts caused by the shaking of children, (2) loss of a localized lung area due to a failure to hold their breath, and (3) a smaller CT chest area, compared with adults. To solve these unique problems, this study developed an automatic lung segmentation method by combining traditional imaging methods with ResUnet using the CT images of 60 children, aged 0-6 years. First, the CT images were cropped and zoomed through ecological operations to concentrate the segmentation task on the chest area. Then, a ResUnet model was used to improve the loss for lung segmentation, and case-based connected domain operations were performed to filter the segmentation results and improve segmentation accuracy. The proposed method demonstrated promising segmentation results on a test set of 12 cases, with average accuracy, Dice, precision, and recall of 0.9479, 0.9678, 0.9711, and 0.9715, respectively, which achieved the best performance relative to the other six models. This study shows that the proposed method can achieve good segmentation results in CT of preschool children, laying a good foundation for the diagnosis of children’s lung diseases.

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Section: Discussionmentioning

confidence: 99%

“…However, it often has some defects and underperforms in the segmentation of pathological boundaries. (3) Clustering-based segmentation [ 12 – 14 ]. This method can aggregate pixels with small gray value differences into the same category and divide an image into different regions through clustering.…”

Section: Introductionmentioning

confidence: 99%

Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet

Shu

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Eqs. (16) and (17) are used to find the solutions. When an iteration terminates, the HSI of each habitat is updated and the habitat with the highest HSI is the solution.…”

Section: Proposed Cppbbo Algorithmmentioning

confidence: 99%

“…Moreover, scale and orientation changes in the target can also lead to poor tracking performance of the MS. Hence, many improved versions of MS tracker [14][15][16][17][18] have been proposed to enhance the robustness there.…”

Section: Introductionmentioning

confidence: 99%

Biologically adaptive robust mean shift algorithm with Cauchy predator-prey BBO and space variant resolution for unmanned helicopter formation

Wang

Duan

2014

Sci. China Inf. Sci.

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Visual tracking technology can provide measurement information for unmanned helicopter formation and thus, more attention is being paid to this research area. We propose a novel mean shift (MS) algorithm that is both adaptive and robust for unmanned helicopter formation and apply it to the leading unmanned helicopter tracking. The movement of an unmanned helicopter is very flexible and changeable, which makes the tracking there of more difficulty than for common targets. In creating an algorithm that can adapt to the acceleration of the unmanned helicopter and estimates both the scale and orientation of the movement changes, we combine the traditional MS with the bio-inspired Cauchy predator-prey biogeography-based optimization (CPPBBO) evolutionary algorithm, and also the space variant resolution (SVR) mechanism of the human visual system (MS-CPPBBO-SVR). To demonstrate the effectiveness and robustness of the proposed method and justify the importance of the CPPBBO algorithm and SVR mechanism at the same time, a series of comparative experiments were carried out. The experimental results of the proposed MS-CPPBBO-SVR method are compared with other competitive tracking methods, such as MS, MS with SVR (MS-SVR), MS-SVR with several other optimization algorithms, and the robust particle filter algorithm. The experimental results demonstrate that our proposed tracking approach, MS-CPPBBO-SVR, is more adaptive, robust and efficient in target tracking than the other methods.

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“…Multi-sensor image fusion [ 1 , 2 , 3 , 4 ], which can enhance the ability of target description and scene understanding, is widely used in target tracking [ 5 ], face recognition [ 6 ], night vision observation [ 7 ] and many other fields. Image registration, as an important procedure for image fusion, greatly determines the accuracy of target alignment in the scene, thus affecting the quality of infrared-visible image fusion.…”

Section: Introductionmentioning

confidence: 99%

Automatic and Robust Infrared-Visible Image Sequence Registration via Spatio-Temporal Association

Zhao

Chen

et al. 2019

Sensors

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To solve the problems of the large differences in gray value and inaccurate positioning of feature information during infrared-visible image registration, we propose an automatic and robust algorithm for registering planar infrared-visible image sequences through spatio-temporal association. In particular, we first create motion vector distribution descriptors which represent the temporal motion information of foreground contours in adjacent frames to complete coarse registration without feature extraction. Then, for precise registration, we extracted FAST corners of the foreground, which are described by the spatial location distribution of contour points based on connected blob detection, and match these corners using bidirectional optimal maximum strategy. Finally, a reservoir updated by Better-In, Worse-Out (BIWO) strategy is established to save matched point pairs and obtain the optimal global transformation matrix. Extensive evaluations on the LITIV dataset well demonstrate the effectiveness of the proposed algorithm. Particularly, our algorithm achieves lower registration overlapping errors than the other two state-of-the-arts.

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A multi-cue mean-shift target tracking approach based on fuzzified region dynamic image fusion

Cited by 16 publications

References 11 publications

Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet

Research on CT Lung Segmentation Method of Preschool Children based on Traditional Image Processing and ResUnet

Biologically adaptive robust mean shift algorithm with Cauchy predator-prey BBO and space variant resolution for unmanned helicopter formation

Automatic and Robust Infrared-Visible Image Sequence Registration via Spatio-Temporal Association

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