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Image Segmentation Using Linked mean-shift Vectors and Global/Local Attributes

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2017年第10期27卷 2132-2140页

作者： Cho, Hanjoo Kang, Suk-Ju Kim, Young Hwan Pohang Univ Sci & Technol Dept Elect Engn Pohang 790784 South Korea Sogang Univ Dept Elect Engn Seoul 121742 South Korea

This paper proposes novel noniterative mean-shift-based image segmentation that uses global and local attributes. The existing mean-shift-based methods use a fixed range bandwidth, and hence their accuracy is dependent on the range spectrum of an image. To resolve this dependency, this paper proposes to modify the range kernel in the mean-shift process to be anisotropic. The modification is conducted using a global attribute defined as the range covariance matrix of the image. Further, to alleviate oversegmentation, the proposed method merges the segments having similar local attributes more aggressively than other segments. The local attribute for each segment is defined as the sum of the variances of the chromatic components. Finally, to expedite the processing, the proposed method uses a region adjacency graph (RAG) for the merging process, thus differing from the existing linked mean-shift-based methods. In the experiments on the Berkeley segmentation data set, the use of the global and local attributes improved segmentation accuracy;the proposed method outperformed the state-of-the-art linked mean-shift-based method by showing an improvement of 2.15%, 3.16%, 3.32%, and 1.90% in probability rand index, segmentation covering, variation of information, and F-measure, respectively. Further, compared with the benchmark method, which uses the dilating and merging scheme, the proposed method improved the speed of the merging process 42 times by applying the RAG.

关键词： Computer vision image analysis mean-shift algorithm

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Doppler Spectrum Segmentation of Radar Sea Clutter by mean-shift and Information Geometry Metric 17

Doppler Spectrum Segmentation of Radar Sea Clutter by Mean-S...

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17th International Radar Symposium (IRS)

作者： Barbaresco, Frederic Forget, Thibault Chevallier, Emmanuel Angulo, Jesus Thales Air Syst Adv Radar Concepts Business Unit Voie Pierre Gilles Gennes F-91470 Limours France Mines ParisTech Dept Math & Syst 35 Rue St Honore F-77300 Fontainebleau France

ISBN: (纸本)9781509025183

Radar sea clutter inhomogeneity in range is characterized by Doppler mean and spectrum width variations. We propose a new approach for robust statistical density estimation and segmentation of sea clutter Doppler spectrum. In each range cell, Doppler is characterized by a Toeplitz Hermitian Positive Definite covariance matrix that is coded in Poincare's unit poly-disk and we use adaptation of standard kernel methods to density estimation on this specific Riemannian manifold. Based on this non-parametric approach to estimate statistical density of Doppler Spectrum, we address the problem of sea clutter data mapping and segmentation by extending "mean-shift" tool for these densities on Poincare's unit poly-disk. This statistical segmentation is requested for robust detection of targets in sea clutter, especially in case of high sea state.

关键词： Terms Sea Clutter Fisher Metric Information Geometry Kernel Density Estimation mean-shift algorithm

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mean-shift Tracker with Face-adjusted Model

Mean-shift Tracker with Face-adjusted Model

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International Conference on Control, Automation and Systems

作者： Choe, Jeehyun Goya Seok, Joon-Hong Lee, Ju-Jang Korea Adv Inst Sci & Technol Robot Program Taejon 305701 South Korea Korea Adv Inst Sci & Technol Dept Elect Engn & Comp Sci Daejeon South Korea

ISBN: (纸本)9788995003893

mean-shift algorithm shows robust performances in various object-tracking technologies including face tracking. Due to its robustness and accuracy, mean-shift algorithm is regarded as one of the best ways to apply in object-tracking technology in computer vision fields. However, it has a drawback of getting into a bottleneck state when faced with a speedy object moving beyond its window size within one image frame interval time. The time required to calculate mean-shift vector could be much lessened with lesser memory when color model is adjusted to the previously known target information. This paper shows the building process of target-adjusted model with a non-uniform quantization. The target color model dealt in this paper is the one used for deriving mean-shift vector. It is a kernel model containing both the color and distance information. This paper gives scheme to efficiently deal with color information in the model. Through a proper selection of color bins, unimportant color values were reduced to a small amount. As a result, the computing time of the mean-shift vector in face-tracking was shortened while maintaining robustness and accuracy.

关键词： Face tracking mean-shift algorithm Face-adjusted Model LUT(Look Up Table)

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Efficiency Analysis of Preset Color Histogram in mean-shift-based Crow Tracking

Efficiency Analysis of Preset Color Histogram in Mean-shift-...

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International MultiConference of Engineers and Computer Scientists (IMECS 2012)

作者： Koga, Takanori Ochiai, Kouji Suetake, Noriaki Uchino, Eiji Tokuyama Coll Technol Dept Comp Sci & Elect Engn Gakuendai Yamaguchi 7458585 Japan Yamaguchi Univ Grad Sch Sci & Engn Yamaguchi 7538512 Japan Fuzzy Log Syst Inst Iizuka Fukuoka 8200067 Japan

ISBN: (纸本)9789881925114

Rice-ducks fed in rice paddies for farming are preyed frequently by injurious birds such as crows. The aims of this study are to detect crows' predation-motion by using time-varying image processing techniques, and to prevent the crows' predation by driving the pest control system adaptively. In this study, we employ a well-known mean-shift algorithm for motion detection. In order to drive the equipments adaptively and adequately, a quick detection response to the crow's motion in the mean-shift algorithm is required. For realization of its quick detection response, we modify the mean-shift algorithm by using the preset color histogram calculated from the previous scenes. By the actual experiments, it is verified that the detection response of the mean-shift algorithm is improved by the preset color histogram while keeping the fundamental tracking performance.

关键词： Rice-duck farming mean-shift algorithm Object tracking Crow Preset color histogram

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Weighted Sub-block mean-shift Tracking with Improved Level Set Target Extraction 8

Weighted Sub-block Mean-Shift Tracking with Improved Level S...

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2015 Eighth International Conference on Internet Computing for Science and Engineering (ICICSE)

作者： Wang, Xingmei Dong, Hongbin Chu, Yan Wang, Xiaowei Lie, Lin Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781509004546

mean-shift tracking algorithm is a widely-used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity of target and candidate sub-blocks, and by the ratio of the target sub-block and overall areas. The target sub-block area is calculated by the means of the narrow band level set combined with a compromise to improve extraction accuracy and operating efficiency. Both of RGB color information in the target region and the pixel's position information are taken into consideration while describing the feature model of target and candidate region inside each sub-block. Experimental results demonstrate the method's success for tracking of targets with background change and shade during the dynamic scene, where the basic mean-shift tracking algorithm fails. The proposed method has better tracking performance with higher tracking accuracy and adaptability.

关键词： target tracking mean-shift algorithm target extraction level set dynamic scene weighted sub-block

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A MOTION TRACKING METHOD BASED ON KALMAN FILTER COMBINED WITH mean-shift

A MOTION TRACKING METHOD BASED ON KALMAN FILTER COMBINED WIT...

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6th International Conference on Wavelet Analysis and Pattern Recognition

作者： Zhao, Jie Liu, Wei-Jing Sun, Hui-Jia Hebei Univ Coll Elect & Informat Engn Baoding 071002 Peoples R China

ISBN: (纸本)9781424422388

In this paper, it proposes an object tracking algorithm based-on the Kalman filter combined with the mean-shift algorithm. It can predict the object motion more accurate with Kalman filter, including position and velocity. And the adjacent locations of the predicted point are defined as the search window. In the search window, the position of object is fixed on by mean-shift. The experiment results show that this algorithm can make full use of the prediction function of Kalman filter, improve the search speed, and achieve a more accurate tracking even the color is similar, and also solve the problem of shelter to some extent.

关键词： Kalman filter mean-shift algorithm Tracking Prediction

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Auto-Segmentation using mean-shift and Entropy Analysis 3

Auto-Segmentation using Mean-Shift and Entropy Analysis

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Susan, Seba Kumar, Ankit Delhi Technol Univ Dept Comp Sci & Engn New Delhi India

ISBN: (纸本)9789380544199

In this paper we discuss the automization of the mean-shift clustering algorithm which is dependent on user defined parameters for its efficiency. We propose that the optimum solution of mean-shift corresponds to the maximum entropy of the mode-probabilities which assume the form of a uniform probability distribution for the meaningful segmentation of a scene. The experimentation on the benchmark Berkeley segmentation database shows high accuracy for the automated mean-shift as compared to the baseline method. The Non extensive entropy with Gaussian gain gives highly meaningful segmentation that agrees with human perception as compared to the extensive Shannon entropy.

关键词： mean-shift algorithm Maximum entropy framework Color Image Segmentation Non-extensive entropy with Gaussian gain

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Object Tracking using KLT aided mean-shift Object Tracker (ICCAS 2014) 14

Object Tracking using KLT aided Mean-shift Object Tracker (I...

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14th International Conference on Control, Automation and Systems (ICCAS)

作者： Kim, Sun-Ho Kim, Jungho Hwang, Youngbae Choi, Byoungho Yoon, Ju Hong Korea Elect Technol Inst Multimedia IP Ctr Pangyo 463400 South Korea

ISBN: (纸本)9788993215069

In this paper, we present a new object tracking algorithm which integrates color-based mean-shift and feature-based optical flow methods. To utilize two approaches in the complimentary manner, we iteratively compute the mean-shift vector based on color histograms and tracked features by KLT. In the experiments, we show the improved performance for partial occlusion and severe appearance changes in the representative benchmark sequences.

关键词： mean-shift algorithm Kanade-Lucas-Tomasi algorithm Object Tracking

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Efficient mean-shift tracking via a new similarity measure

Efficient mean-shift tracking via a new similarity measure

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Conference on Computer Vision and Pattern Recognition

作者： Yang, C Duraiswami, R Davis, L Univ Maryland Dept Comp Sci Perceptual Interfaces & Real Lab College Pk MD 20742 USA

ISBN: (纸本)0769523722

The mean shift algorithm 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 mean shift algorithm 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.

关键词： mean-shift algorithm improved fast Gauss transform similarity measure spatial-feature tracking

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Image Segmentation using Linked mean-shift Vectors with Region Attribution Analysis 11

Image Segmentation using Linked Mean-Shift Vectors with Regi...

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2015 11th Conference on PhD Res in Microelect & Elect PRIME

作者： Cho, Hanjoo Kim, Young Hwan POSTECH Dept Elect Engn Pohang South Korea

ISBN: (纸本)9781479982295

This paper proposes a novel linked mean-shift algorithm that considers region attribution in a cluster merging process. mean-shift based image segmentation suffers from its extreme computational complexity, despite of its outstanding segmentation accuracy. To resolve this problem, the linked mean-shift algorithm that removes the iterative process in the mean-shift process was introduced. However, the approximation in the linked-mean-shift algorithm gives rise to unwanted merging of the clusters that should not be merged. To prevent the unwanted merging, the proposed algorithm analyzes region attribution, then, in the merging process, applies strict condition to the clusters that have dissimilar attribution than the clusters that have similar attribution. In experiments, the proposed algorithm improved segmentation accuracy than the linked mean-shift algorithm, while retained twenty times faster speed than the mean-shift algorithm. Furthermore, the experimental results for variation of processing time showed the proposed algorithm can provide much settled throughput than the mean-shift algorithm.

关键词： image segmentation mean-shift algorithm

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