scholarly journals Generalized methods and solvers for noise removal from piecewise constant signals. I. Background theory

2011 ◽  
Vol 467 (2135) ◽  
pp. 3088-3114 ◽  
Author(s):  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Signal Processing ◽  
Mean Shift ◽  
Path Following ◽  
Drill Hole ◽  
Noise Removal ◽  
Total Variation Regularization ◽  
Piecewise Constant ◽  
Least Angle Regression ◽  
Background Theory ◽  
Mean Shift Clustering

Removing noise from piecewise constant (PWC) signals is a challenging signal processing problem arising in many practical contexts. For example, in exploration geosciences, noisy drill hole records need to be separated into stratigraphic zones, and in biophysics, jumps between molecular dwell states have to be extracted from noisy fluorescence microscopy signals. Many PWC denoising methods exist, including total variation regularization, mean shift clustering, stepwise jump placement, running medians, convex clustering shrinkage and bilateral filtering; conventional linear signal processing methods are fundamentally unsuited. This paper (part I, the first of two) shows that most of these methods are associated with a special case of a generalized functional, minimized to achieve PWC denoising. The minimizer can be obtained by diverse solver algorithms, including stepwise jump placement, convex programming, finite differences, iterated running medians, least angle regression, regularization path following and coordinate descent. In the second paper, part II, we introduce novel PWC denoising methods, and comparisons between these methods performed on synthetic and real signals, showing that the new understanding of the problem gained in part I leads to new methods that have a useful role to play.

scholarly journals Generalized methods and solvers for noise removal from piecewise constant signals. II. New methods

2011 ◽  
Vol 467 (2135) ◽  
pp. 3115-3140 ◽  
Author(s):  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Markov Models ◽  
Mean Shift ◽  
Noise Removal ◽  
Local Smoothing ◽  
Wavelet Shrinkage ◽  
Piecewise Constant ◽  
New Methods ◽  
Background Theory ◽  
Mean Shift Clustering ◽  
Special Case

Removing noise from signals which are piecewise constant (PWC) is a challenging signal processing problem that arises in many practical scientific and engineering contexts. In the first paper (part I) of this series of two, we presented background theory building on results from the image processing community to show that the majority of these algorithms, and more proposed in the wider literature, are each associated with a special case of a generalized functional, that, when minimized, solves the PWC denoising problem. It shows how the minimizer can be obtained by a range of computational solver algorithms. In this second paper (part II), using this understanding developed in part I, we introduce several novel PWC denoising methods, which, for example, combine the global behaviour of mean shift clustering with the local smoothing of total variation diffusion, and show example solver algorithms for these new methods. Comparisons between these methods are performed on synthetic and real signals, revealing that our new methods have a useful role to play. Finally, overlaps between the generalized methods of these two papers and others such as wavelet shrinkage, hidden Markov models, and piecewise smooth filtering are touched on.

Video Vehicle Tracking Based on Improved Mean-Shift Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 1408-1411
Author(s):  
Wei Bin Chen ◽  
Xin Zhang ◽  
Su Qin Luo
Keyword(s):  
Clustering Algorithm ◽  
Mean Shift ◽  
Vehicle Tracking ◽  
Absolute Difference ◽  
Mean Shift Algorithm ◽  
Invariant Moments ◽  
Mean Shift Clustering ◽  
Video Frames ◽  
Detection Technology ◽  
Background Area

An improved Mean-Shift-based Video vehicle tracking algorithm was proposed and which can improve the real-time and accuracy of the vehicle detection technology in the application. First, it eliminates the disturbance from unrelated background by mathematical morphology operation between a traffic image and the mask of fixed background area .Then the image sequences are simulated by absolute difference of adaptive threshold for detecting latent target. At last, clusters video frames with similar characteristics which are regarded of the invariant moments vectors by Mean Shift clustering algorithm. Experimental results shown that the improved algorithm has advantages of reducing king region of vehicle matching and vehicle complete occlusion.

Efficient video segmentation using temporally updated mean shift clustering

2008 ◽  
Author(s):  
Nemanja Petrović ◽  
Ljubomir Jovanov ◽  
Aleksandra Pižurica ◽  
Wilfried Philips
Keyword(s):  
Video Segmentation ◽  
Mean Shift ◽  
Mean Shift Clustering ◽  
Efficient Video

scholarly journals Segmentation of Multi-Tumor from PET/CT Images

2019 ◽  
Vol 8 (12S2) ◽  
pp. 170-176
Keyword(s):  
Automatic Segmentation ◽  
Mean Shift ◽  
Ground Truth ◽  
Histogram Equalization ◽  
Ct Images ◽  
Svm Classifier ◽  
Segmentation Evaluation ◽  
Multiple Tumor ◽  
Mean Shift Clustering ◽  
Pet Ct

The aim of the project is to develop a methodology for automatic segmentation of multiple tumor from PET/CT images. Image pre-processing methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE), image sharpening and Wiener filtering were performed to remove the artifacts due to contrast variations and noise. The image was segmented using K-means, Threshold segmentation, watershed segmentation, FCM clustering Segmentation, Mean shift Clustering Segmentation, Graph Cut Segmentation. Evaluation was made for the segmentation against the Ground Truth. Various Features was selected and extracted. Classification was made using SVM classifier and KNN classifier to classify the tumor as benign or malignant. The proposed method was carried out using PET/CT images of lung cancer patients and implemented using MATLAB.

scholarly journals A Balanced Strategy for the FFBS Operator Integrating Dispatch Area, Route, and Depot Based on Multimodel Technologies

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Qingfeng Zhou ◽  
Jun Zhou ◽  
Chun Janice Wong
Keyword(s):  
Traffic Management ◽  
Evaluation Method ◽  
Time Window ◽  
Mean Shift ◽  
City Management ◽  
Optimal Dispatch ◽  
Scheduling Strategy ◽  
Mean Shift Clustering ◽  
Load Action ◽  
Shared Bicycle

Bicycle scheduling is the essential strategy for balancing the demand for the public bicycle system (PBS). Existing literature pays more attention to bike scheduling models and their solutions, but seldom discusses the dispatch area and depot center. Reasonable dockless public bicycle dispatch area and optimal dockless bike dispatch depot location in the service area were discussed from the existing shared bicycle operation data in this paper. We proposed a feasible framework including bike trip network segmentation, mean-shift clustering based on the point position, VRP model, genetic algorithm, and TOPSIS evaluation method. The effectiveness and superiority of the division of the dispatch area are verified. The main evidence for this article is (1) although the cycling networks of bicycles are different at different times of the day, the results of community division are relatively stable and have great similarities. (2) The plan of the dispatch area has impacted on the operation efficiency of the PBS. For a scheduling area, the target value of the optimal scheduling strategy corresponding to different dispatch centers is obviously different. Therefore, the location of the dispatch center has a great impact on the quality of the scheduling strategy. The dispatch area determined by bike trip OD community detection has stable characteristics of scheduling costs. (3) This work is an attempt to combine big data and model technology to assist city management. We build a feasible framework to serve a balanced strategy for FFBS which can provide reasonable dispatch area, optimal dispatch depot location, dispatch truck’s route length, load action, and time window. Our proposed framework provides new ideas for regional traffic dispatching for the traffic management department and FFBS operator, which has certain practical reference significance.

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