A Modified GrabCut Using a Clustering Technique to Reduce Image Noise

Lee, Gangseong; Lee, Sang-Hun; Kim, GaOn; Park, JongHun; Park, YoungSoo

doi:10.3390/sym8070064

Cited by 11 publications

(6 citation statements)

References 10 publications

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“…The min-cut/max-flow algorithm [ 33 ] is proposed to finish the segmentation more accurately. The idea of this algorithm is to regard one image as a net with nodes, and each node take the place of a corresponding pixel.…”

Section: Interactive Image Segmentationmentioning

confidence: 99%

An Interactive Image Segmentation Method in Hand Gesture Recognition

Chen

Sun

et al. 2017

Sensors

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Abstract:In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

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Section: Interactive Image Segmentationmentioning

confidence: 99%

An Interactive Image Segmentation Method in Hand Gesture Recognition

Chen

Sun

et al. 2017

Sensors

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“…The image processing layer has six image processing algorithms: Gaussian filter [15], Square filter [16], Median filter [17], Bilateral filter [18], Equalization histogram [19], Laplacian of Gaussian operator [20]. The differences among irises are suppressed from different angles by these six algorithms, stable feature points inside the iris texture are extracted as the iris recognition area, which can reduce the interference of defocus image on the feature.…”

Section: Introductionmentioning

confidence: 99%

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Liu

Zhu

et al. 2019

Algorithms

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Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

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“…These methods are realised in spatial and frequency domains (Han et al, 2015). The filter is the most basic operation of image processing and computer vision (Lee et al, 2016). One of the essential processes in the spatial domain is low or high pass filters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

Erkan

Gökrem

Enginoğlu

2019

Uluslararası Muhendislik Arastirma Ve Gelistirme Dergisi

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In image processing, nonlinear filters are commonly used as a pre-process for noise removal before applying any advanced processing such as classification and clustering to an image. The adaptive filters being a kind of the nonlinear filters mainly perform better than the others in salt-and-pepper noise. In this paper, we first define a new median method, i.e. right median (rm). We then define a new adaptive nonlinear filter developed via rm, namely Adaptive Right Median Filter (ARMF), for saltand-pepper noise removal. Afterwards, we compare the results of ARMF with some of the known filters by using 12 test images and two image quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that ARMF outperforms the other methods at all the noise density except 80% and 90% in the mean percentages. Finally, we discuss the need for further research.

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A Modified GrabCut Using a Clustering Technique to Reduce Image Noise

Cited by 11 publications

References 10 publications

An Interactive Image Segmentation Method in Hand Gesture Recognition

An Interactive Image Segmentation Method in Hand Gesture Recognition

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Adaptive Right Median Filter for Salt-and-Pepper Noise Removal

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