Interactive image segmentation based on the appearance model and orientation energy

Qu, Shaobo; Li, Qiaoliang; Peng, Zili

doi:10.1016/j.cviu.2022.103371

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…To check the performance of the proposed algorithm, three datasets were considered: the FVC2000 dataset, 35 the coronary artery dataset, 36 and real images (collected from the Internet). The results of the proposed method are compared with 11 kernel graph cut methods: RBF, 11 entropy-based, 19 Krawtchouk, 24 L2S, 37 LoGRSF, 38 LocalPrefitting, 39 AMOE, 28 edge reg, 32 curvature reg, 33 spatial reg, 34 and customized RBF 27 . It should be mentioned that, to make a fair comparison, the coefficient of the regularization term in the RBF and entropy algorithm is considered to be the middle limit and is equal to 0.5.…”

Section: Laboratory Resultsmentioning

confidence: 99%

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Setting the regularization coefficient based on image energy in image segmentation using kernel graph cut algorithm

Niazi,

Rahbar

2024

J. Electron. Imag.

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The kernel graph cut method provides good performance in image segmentation. However, its efficiency strongly depends on the data term and regularization term in the objective function. The data term maps the standard deviation of the data of each region to the transformation space. The regularization term is responsible for smoothing the boundaries. The regularization term, based on a constant coefficient, is added to the data term. Allocating a fixed coefficient for all images leads to inappropriate image segmentation. In this regard, we deal with the automatic adjustment of the regularization term coefficient in the kernel graph cut method based on image energy. This approach is useful in image segmentation with thin structures. The laboratory results were taken on three datasets: fingerprint, coronary artery, and real. The efficiency evaluation of the algorithm, compared with other methods in the field of energy-based algorithms, shows a maximum score of 88.27 in Dice, 79.67 in Jaccard, 75.67 in accuracy, 61.06 in peak-signal-to noise ratio, and a minimum score of 0.06 in mean sum of squared distance on the mentioned datasets. The proposed method also takes advantage of appropriate computing load in the methods based on a graph cut.

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Section: Laboratory Resultsmentioning

confidence: 99%

“…In this regard, in Ref. 28, contour orientation and spatial distance between pixels are used in the graph energy function. The image segmentation is improved according to the similar appearance features of the foreground and background.…”

Section: Introductionmentioning

confidence: 99%