Novel mean-shift based histogram equalization using textured regions

Lai, Yu-Ren; Chung, Kuo-Liang; Chen, Chyouhwa; Lin, Guei-Yin; Wang, Chao-Hsin

doi:10.1016/j.eswa.2011.08.134

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Mean-Shift algorithm can be used to detect and track moving objects. It determines moving objects' locations by sample density distribution maximum value [21]. If a set {x } =1,..., belongs to space, the corresponding density estimation function (x) will be computed by density kernel (X) and window radius ℎ:̂(…”

Section: Feature Scale Based On Mean-shiftmentioning

confidence: 99%

A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

Dou

Hao

Ding

et al. 2015

Mathematical Problems in Engineering

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We propose a novel Mean-Shift-based building approach in wide baseline. Initially, scale-invariance feature transform (SIFT) approach is used to extract relatively stable feature points. As to each matching SIFT feature point, it needs a reasonable neighborhood range so as to choose feature points set. Subsequently, in view of selecting repeatable and high robust feature points, Mean-Shift controls corresponding feature scale. At last, our approach is employed to depth image acquirement in wide baseline and Graph Cut algorithm optimizes disparity information. Compared with the existing methods such as SIFT, speeded up robust feature (SURF), and normalized cross-correlation (NCC), the presented approach has the advantages of higher robustness and accuracy rate. Experimental results on low resolution image and weak feature description in wide baseline confirm the validity of our approach.

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Section: Feature Scale Based On Mean-shiftmentioning

confidence: 99%

A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

Dou

Hao

Ding

et al. 2015

Mathematical Problems in Engineering

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“…Huiyu Zhou [5] has introduced SIFT features to the procedures of mean-shift 1 algorithm and has used this novel method to track object in real scenarios. Yu-Ren Lai, et al [6] …”

Section: Introductionmentioning

confidence: 98%

Parallel manipulator tracking with occlusion based on improved mean-shift

Zhang

Yu²

2013

2013 IEEE International Conference on Information and Automation (ICIA)

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An improved mean-shift algorithm is proposed and applied to track a moving parallel manipulator with partial occluded. At first, an improved mean-shift algorithm based on color-texture features is introduced. Then a factor reflecting the severity of occlusion is defined to judge whether the end-effector of parallel manipulator is occluded. At last, the end-effector is divided into several sub-blocks and each sub-block is tracked independently. The sub-block with the largest confidence decides the location of the end-effector in next frame. The experimental results demonstrate that it is effective and efficient.

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“…Ai et al [22] examined incorporating the temporal characteristics of acquired functional magnetic resonance imaging data with mean shift clustering for functional magnetic resonance imaging analysis to enhance activation detections. Lai et al [23] used MS for histogram equalization and determined a set of textured regions by using the density of edge concentration by using the MS abased approach. Ozden et al [24] proposed an approach using low-level features incorporating color, spatial information and texture features in MS algorithm for segmentation.…”

Section: Introductionmentioning

confidence: 99%

An effective color image segmentation approach using neutrosophic adaptive mean shift clustering

et al. 2018

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Color image segmentation can be defined as dividing a color image into several disjoint, homogeneous, and meaningful regions based on the color information. This paper proposes an efficient segmentation algorithm for color images based on neutrosophic adaptive mean shift (NAMS) clustering. Firstly, an image is transformed in neutrosophic set and interpreted by three subsets: true, indeterminate, and false memberships. Then a filter is designed using indeterminacy membership value, and neighbors' features are employed to alleviate indeterminacy degree of image. A new mean shift clustering, improved by neutrosophic set, is employed to categorize the pixels into different groups whose bandwidth is determined by the indeterminacy values adaptively. At last, the segmentation is achieved using the clustering results. Various experiments have been conducted to verify the performance of the proposed approach. A published method was then employed to take comparison with the NAMS on clean, low contrast, and noisy images, respectively. The results demonstrate the NAMS method achieves better performances on both clean image and low contrast and noisy images.

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Novel mean-shift based histogram equalization using textured regions

Cited by 10 publications

References 22 publications

A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

A Mean-Shift-Based Feature Descriptor for Wide Baseline Stereo Matching

Parallel manipulator tracking with occlusion based on improved mean-shift

An effective color image segmentation approach using neutrosophic adaptive mean shift clustering

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