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 ...
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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.
作者：
Farahani, GholamrezaIROST
Elect & Informat Technol Inst Sh Ehsani Rad StEnqelab StParsa Sq Tehran *** Iran
There are various methods in the field of moving-object tracking in the video images that each of them implies on the specific features of object. Among tracking methods based on features, algorithms based on color ar...
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There are various methods in the field of moving-object tracking in the video images that each of them implies on the specific features of object. Among tracking methods based on features, algorithms based on color are able to provide a precise description of the object and track the object with high speed. One of the efficient methods in the field of object tracking based on color information is mean-shift algorithm. If the color of moving object approaches toward a background model or image background has a low contrast and brightness, then the color information is not enough for target tracking. In this paper, the new tracking method is proposed which with combination of moved object information with color information, the new proposed method will be capable to track object under condition that color information is not enough for tracking. With use of background subtraction method based on Gaussian combination, the binary image which includes moving information will use in the mean-shift algorithm. Usage of object movement information will compensate the lack of spatial information and will increase robustness of algorithm especially in the complicated conditions. Also in order to achieve the robust algorithm against changes in shapes, size, and rotation of object, extended mean-shift algorithm is used. Results show the robustness of proposed algorithm in object tracking especially under conditions which object color is same as background color and have better results in the low contrast condition in comparison to mean-shift and extended mean-shift algorithms.
In the context of regressing a response Y on a predictor X, we consider estimating the local modes of the distribution of Y given X = x when X is prone to measurement error. We propose two nonparametric estimation met...
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In the context of regressing a response Y on a predictor X, we consider estimating the local modes of the distribution of Y given X = x when X is prone to measurement error. We propose two nonparametric estimation methods, with one based on estimating the joint density of (X, Y) in the presence of measurement error, and the other built upon estimating the conditional density of Y given X = x using error-prone data. We study the asymptotic properties of each proposed mode estimator, and provide implementation details including the mean-shift algorithm for mode seeking and bandwidth selection. Numerical studies are presented to compare the proposed methods with an existing mode estimation method developed for error-free data naively applied to error-prone data.
In order to accurately track sea cucumber on the assembly line to realize automatic grabbing using mechanical arm, an object tracking method based on mean-shift algorithm was proposed. Firstly, the contours of the obj...
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ISBN:
(纸本)9781509041022
In order to accurately track sea cucumber on the assembly line to realize automatic grabbing using mechanical arm, an object tracking method based on mean-shift algorithm was proposed. Firstly, the contours of the objects was extracted from the original image to select tracking target, and then the local image was cropped at the same position and local area in the second frame, and mean-shift algorithm was used to search the center location until the searching process converged. Finally, tracking was realized by searching the center location in the next frames one by one. Compared with the color histogram mean-shift algorithm(CHMS) algorithm, - the proposed algorithm have good tracking performance and the average consuming time is only 11. 04% of the average consuming time of the CHMS algorithm, which prove that the proposed algorithm can satisfy the automatic grabbing requirement of real-time and robustness.
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 no...
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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.
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...
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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.
The bandwidth of traditional mean-shift tracking algorithm can not be adapted to size change of target. To overcome this problem, a new adaptive kernel-bandwidth selection method based on the comparison of Bhattachary...
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The bandwidth of traditional mean-shift tracking algorithm can not be adapted to size change of target. To overcome this problem, a new adaptive kernel-bandwidth selection method based on the comparison of Bhattacharyya coefficients is proposed in this paper. In this method, the Bhattacharyya coefficient is calculated by using histograms of target image and target background image, and then, a new Bhattacharyya coefficient is calculated according to the candidate image histogram of the current frame and the target background image during the process of tracking. Judging the changing trend of the target by comparing the two former coefficients and the kernel-bandwidth is expanded or shrunk by 10% according to the judgment. Finally, the target area is specified after the calculation above. The simulation results show that the algorithm proposed in this paper verifies the effectiveness of tracking of the targets with changing scales.
In this paper, we use image-based rendering (IBR) to develop a scene rotation mechanism. We shot several images in the same scene and computed the angles between images. A video is then composed, allowing users to sel...
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In this paper, we use image-based rendering (IBR) to develop a scene rotation mechanism. We shot several images in the same scene and computed the angles between images. A video is then composed, allowing users to select viewing angles when the video is playing. We made three kinds of assumptions that may affect the resulting video, and proved our assumptions by a series of experiments. Finally, we use video of realistic scenario and produce interactive video by the proposed method. The contribution also includes techniques to compute geometric parameters of the scene from one or more images.
In this work we present the Probabilistic Primitive Refinement (PPR) algorithm, an iterative method for accurately determining the inliers of an estimated primitive (such as planes and spheres) parametrization in an u...
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ISBN:
(纸本)9781467391634
In this work we present the Probabilistic Primitive Refinement (PPR) algorithm, an iterative method for accurately determining the inliers of an estimated primitive (such as planes and spheres) parametrization in an unorganized, noisy point cloud. The measurement noise of the points belonging to the proposed primitive surface are modelled using a Gaussian distribution and the measurements of extraneous points to the proposed surface are modelled as a histogram. Given these models, the probability that a measurement originated from the proposed surface model can be computed. Our novel technique to model the noisy surface from the measurement data does not require a priori given parameters for the sensor noise model. The absence of sensitive parameters selection is a strength of our method. Using the geometric information obtained from such an estimate the algorithm then builds a color-based model for the surface, further boosting the accuracy of the segmentation. If used iteratively the PPR algorithm can be seen as a variation of the popular mean-shift algorithm with an adaptive stochastic kernel function.
Tracking of any given object forms integral part in surveillance, control and analysis applications. The video tracker presented here works on the principle of meanshift. meanshift is an iterative algorithm which is...
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ISBN:
(纸本)9781467387866
Tracking of any given object forms integral part in surveillance, control and analysis applications. The video tracker presented here works on the principle of meanshift. meanshift is an iterative algorithm which is extended to the field of object tracking. However meanshift tracker losses track of the object when there are variations in illumination. In order to improve the performance of the tracker the poorly illuminated video frames are pre-processed and enhancement is provided to only those frames based on hue preservation algorithm. Low illumination in a frame leads to low value DC coefficient. Enhancement is provided to frames with low DC coefficient. The frames with corrected illumination are used to track object of interest using the meanshiftalgorithm. The proposed method is tried on a variety of video sequences. The results exhibit much improved tracking. The graph plotted for Bhattacharyya coefficient versus frame index exhibits significant increase in its values when the frames are pre-processed in comparison with the basic meanshiftalgorithm.
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