• Title, Summary, Keyword: Mean Shift

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Modified Mean Shift for Color Image Processing (컬러 영상 처리를 위한 Mean Shift 기법 개선)

  • Hwang, Young-chul;Bae, Jung-ho;Cha, Eui-young
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.407-410
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    • 2009
  • 본 논문에서는 개선된 mean shift를 이용한 컬러 영상 분할을 소개한다. Mean shift는 Yizong Cheng에 의해 재조명되고 Dorin Comaniciu 등에 의해 정리되어 영상 필터링(image filtering), 영상 분할(image segmentation), 물체 추적(object tracking) 등 여러 응용 분야에 널리 활용되고 있다. 커널을 이용해 밀도를 추정하고 밀도가 가장 높은 점으로 커널을 연속적으로 이동함으로써 지역적으로 주요한 위치로 데이터 값을 갱신시킨다. 그러나 영상에 포함된 모든 화소에 대해 mean shift를 수행해야하기 때문에 연산 시간이 많이 소요되는 단점이 있다. 본 논문에서는 mean shift 필터링 과정을 분석하고 참조수렴방법과 강제수렴방법을 이용해 소요 시간을 단축시켰다. 모든 점에 대해 mean shift를 수행하는 대신 특정 조건을 만족하는 픽셀은 이웃 픽셀의 수렴 값을 참조하고, mean shift 과정에 진동 또는 미미한 이동을 계속하는 픽셀은 강제 수렴을 실시하였다. 개선된 방법과 기존의 mean shift 방식을 적용하여 영상 필터링과 영상 분할에 적용한 실험에서 결과 영상에는 차이가 적고 기존의 방법에 비해 수행 시간이 24% 정도 소요됨을 확인하였다.

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Robust Mean-Shift Tracking Using Adoptive Selection of Hue/Saturation (Hue/Saturation 영상의 적응적 선택을 이용한 강인한 Mean-Shift Tracking)

  • Park, Han-dong;Oh, Jeong-su
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.579-582
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    • 2015
  • The Mean-Shift is a robustness algorithm that can be used for tracking the object using the similarity of histogram distributions of target model and target candidate. However, Mean-shift using hue information has disadvantage of tracking a wrong target when the target and background has similar hue distributions. We then propose a robust Mean-Shift tracking algorithm using new image that combined upper 4bit-planes in hue and saturation, respectively.

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Integration of Condensation and Mean-shift algorithms for real-time object tracking (실시간 객체 추적을 위한 Condensation 알고리즘과 Mean-shift 알고리즘의 결합)

  • Cho Sang-Hyun;Kang Hang-Bong
    • The KIPS Transactions:PartB
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    • v.12B no.3
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    • pp.273-282
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    • 2005
  • Real-time Object tracking is an important field in developing vision applications such as surveillance systems and vision based navigation. mean-shift algerian and Condensation algorithm are widely used in robust object tracking systems. Since the mean-shift algorithm is easy to implement and is effective in object tracking computation, it is widely used, especially in real-time tracking systems. One of the drawbacks is that it always converges to a local maximum which may not be a global maximum. Therefore, in a cluttered environment, the Mean-shift algorithm does not perform well. On the other hand, since it uses multiple hypotheses, the Condensation algorithm is useful in tracking in a cluttered background. Since it requires a complex object model and many hypotheses, it contains a high computational complexity. Therefore, it is not easy to apply a Condensation algorithm in real-time systems. In this paper, by combining the merits of the Condensation algorithm and the mean-shift algorithm we propose a new model which is suitable for real-time tracking. Although it uses only a few hypotheses, the proposed method use a high-likelihood hypotheses using mean-shift algorithm. As a result, we can obtain a better result than either the result produced by the Condensation algorithm or the result produced by the mean-shift algorithm.

Improvement of Edge Detection Using Mean Shift Algorithm (Mean Shift 알고리즘을 활용한 경계선 검출의 향상)

  • Shin, Seong-Yoon;Lee, Chang-Woo;Rhee, Yang-Won
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.6
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    • pp.59-64
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    • 2009
  • Edge detection always influenced by the noise of original image, therefore need some methods to eliminate them in advance, and the Mean Shift algorithm has the smooth function which suit for this purpose, so adopt it to fade out the unimportant information and the sensitive noise portions. Above all, we use the Canny algorithm to pick up the contour of the objects we focus on. And, take tests and get better result than the former sole Canny algorithm. This combination method of Mean Shift algorithm and Canny algorithm is suitable for the edge detection processing.

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Improved Real-Time Mean-Shift Face Tracking by Readjusting Detected Face Region Histogram (검출된 얼굴 영역 히스토그램 재조정을 통한 개선된 실시간 평균이동 얼굴 추적 방식)

  • Kim, Gui-sik;Lee, Jae-sung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.195-198
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    • 2013
  • Recognition and Tracking of interesting object is the significant field in Computer Vision. Mean-Shift algorithm have chronic problems that some errors are occurred when histogram of tracking area is similar to another area. in this paper, we propose to solve the problem. Each algorithm blocks skin color filtering, face detect and Mean-Shift started consecutive order assists better operation of the next algorithm. Avoid to operations of the overhead of tracking area similar to a histogram distribution areas overlap only consider the number of white pixels by running the Viola-Jones algorithm, simple arithmetic increases the convergence of the Mean-Shift. The experimental results, it comes to 78% or more of white pixels in the Mean-Shift search area, only if the recognition of the face area when it is configured to perform a Viola-Jones algorithm is tracking the object, was 100 percent successful.

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Smartphone Based Retouching Method for Watercolor Painting Effect Using Mean Shift Segmentation (Mean Shift Segmentation을 이용한 스마트폰 기반의 수채화 효과 변환 기법)

  • Lee, Sang-Geol;Kim, Cheol-Ki;Cha, Eui-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.11
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    • pp.2413-2418
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    • 2010
  • We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

Graph Cut-based Automatic Color Image Segmentation using Mean Shift Analysis (Mean Shift 분석을 이용한 그래프 컷 기반의 자동 칼라 영상 분할)

  • Park, An-Jin;Kim, Jung-Whan;Jung, Kee-Chul
    • Journal of KIISE:Software and Applications
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    • v.36 no.11
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    • pp.936-946
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    • 2009
  • A graph cuts method has recently attracted a lot of attentions for image segmentation, as it can globally minimize energy functions composed of data term that reflects how each pixel fits into prior information for each class and smoothness term that penalizes discontinuities between neighboring pixels. In previous approaches to graph cuts-based automatic image segmentation, GMM(Gaussian mixture models) is generally used, and means and covariance matrixes calculated by EM algorithm were used as prior information for each cluster. However, it is practicable only for clusters with a hyper-spherical or hyper-ellipsoidal shape, as the cluster was represented based on the covariance matrix centered on the mean. For arbitrary-shaped clusters, this paper proposes graph cuts-based image segmentation using mean shift analysis. As a prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in $L^*u^*{\upsilon}^*$ color space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigate the problems of mean shift-based and normalized cuts-based image segmentation methods that are recently popular methods, and the proposed method showed better performance than previous two methods and graph cuts-based automatic image segmentation using GMM on Berkeley segmentation dataset.

Multi-mode Kernel Weight-based Object Tracking (멀티모드 커널 가중치 기반 객체 추적)

  • Kim, Eun-Sub;Kim, Yong-Goo;Choi, Yoo-Joo
    • Journal of the Korea Computer Graphics Society
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    • v.21 no.4
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    • pp.11-17
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    • 2015
  • As the needs of real-time visual object tracking are increasing in various kinds of application fields such as surveillance, entertainment, etc., kernel-based mean-shift tracking has received more interests. One of major issues in kernel-based mean-shift tracking is to be robust under partial or full occlusion status. This paper presents a real-time mean-shift tracking which is robust in partial occlusion by applying multi-mode local kernel weight. In the proposed method, a kernel is divided into multiple sub-kernels and each sub-kernel has a kernel weight to be determined according to the location of the sub-kernel. The experimental results show that the proposed method is more stable than the previous methods with multi-mode kernels in partial occlusion circumstance.

Tracking Object with Radical Color Changes Using Rectified Mean Shift (개선된 Mean Shift를 이용한 급격한 컬러 변화 물체 추적)

  • Whang, In-Teck;Choi, Kwang-Nam
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.137-140
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    • 2006
  • 본 논문은 급격한 컬러 변화를 보이는 물체를 추적하기 위해 새로운 알고리즘에 대해서 기술하였다. 이를 수행하기 위해 컬러기반의 추적 알고리즘인 Mean Shift를 개선하여 적용한다. 지존의 Mean Shift 알고리즘은 물체 추적을 위해 컬러 분포 정보를 설정한다. 하지만 초기의 컬러 분포 정보가 사라질 경우 물체 추적을 정확히 수행하기 힘들다는 문제점을 안고 있다. 본 논문에서는 이를 해결하기 위해 Mean Shift를 개선하여, 추적 대상의 컬러 정보를 반복적으로 업데이트하여 초기의 컬러 정보가 사라지더라도 추적이 가능하도록 개선하였다. 개선된 추적 알고리즘은 시간에 따라 초기의 컬러 분포 정보가 완전히 사라지더라도 실시간 추적이 가능하도록 구현하였다. 이를 입증하기 위해 본 논문의 실험에서는 실험적인 환경에서 급격한 컬러 변화를 보이는 간단한 문제의 추적과 실생활에서의 예를 함께 보여준다.

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Mean-Shift Object Tracking with Discrete and Real AdaBoost Techniques

  • Baskoro, Hendro;Kim, Jun-Seong;Kim, Chang-Su
    • ETRI Journal
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    • v.31 no.3
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    • pp.282-291
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    • 2009
  • An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.

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