Fusion Between Shape Prior and Graph Cut for Vehicle Image Segmentation

Wu, Hao; Sun, Xiaoyan; Liu, Yanan; Wang, Dagang; Wei, Bing

doi:10.18280/ts.370211

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The effectiveness of the proposed is analyzed in more detail in the form of evaluation metrics. For that, the sensitivity, selectivity, precision [15], and misclassification error (MCE) [16] are calculated between the binary image segmentation result and the ground truth image. The sensitivity value indicates the fraction of object region pixels that are correctly identified.…”

Section: Effectiveness Of the Proposed Methodsmentioning

confidence: 99%

Double Thresholding with Sine Entropy for Thermal Image Segmentation

Manda¹,

Hyun²

2021

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Traditional thresholding methods are often used for image segmentation of real images. However, due to distinct characteristics of infrared thermal images, it is difficult to ensure an optimal image segmentation using the traditional thresholding algorithms, and therefore, sometimes this can lead to over-segmentation, missing object information, and/or spurious responses in the output. To overcome these issues, we propose a new thresholding technique that makes use of the sine entropy-based criterion. Moreover, we build a double thresholding technique that makes use of two thresholds to get the final image thresholding result. Besides, we introduce the sine entropy concept as a supplement of the Shannon entropy in creating threshold-dependent criterion derived from the grayscale histogram. We found that the sine entropy is more robust in interpreting the strength of the long-range correlation in the gray levels compared to the Shannon entropy. We have experimented with our method on several infrared thermal images collected from standard image databases to describe the performance. On comparing with the state-of-art methods, the qualitative results from the experiments show that the proposed method achieves the best performance with an average sensitivity of 0.98 and an average misclassification error of 0.01, and second-best performance with an average sensitivity of 0.99 and an average specificity of 0.93.

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Section: Effectiveness Of the Proposed Methodsmentioning

confidence: 99%

Double Thresholding with Sine Entropy for Thermal Image Segmentation

Manda¹,

Hyun²

2021

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“…Graph cut methods treat the image segmentation as a label assignment problem. Image I will be divided into homogeneous 𝑁𝑟𝑒𝑔 regions [34,35] that respect some of the image characteristics (intensity, color…). Each region Rl is represented by a group of pixels, whose have a similar border and one label l. In this case, the segmentation is performed via minimizing an energy function [36] expressed by Eq.…”

Section: Image Segmentation By Graph Cutmentioning

confidence: 99%

Synthesis and Comparison of Improved Edge Detection Technique Based on Metaheuristic and Intelligent Algorithm Optimization

Moussa¹,

Douik²

2021

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The edge is the most significant information in image processing applications. Moreover, and the accurate and continue edge commonly leads to accurate related steps like object tracking and region clustering. In fact, it is the first step of image analysis and understanding. The accuracy edge detection results have an impact on the comprehension machine system. In this paper we present various improved edge detection techniques by our research team, of similar color and grey level images, using the information theory approach based on other energy information inspired from Shannon entropy and utilizing as well the metaheuristic and intelligent method combined with multilevel thresholding approach in various color spaces, and like the ant colony optimization with the graph cut approach for indexing images before the segmentation step. In addition, particular swarm optimization is done, and finally the fuzzy technique is used. The effectiveness and accuracy of these approaches are evaluated by many metric measurements and compared with the common operators. The PR metric, has a significant mean value (about 20) than PR of Canny operator (about 9). And also, we can denote that all improved techniques achieve significant results with ameliorated running time.

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“…However, all these methods suffer from low accuracy and poor robustness of segmentation results [16]. To overcome these problems, more and more modern mathematical knowledge and tools, such as statistical theory, graph theory, partial differential equations, and variational methods, have been used in the field of image segmentation, resulting in many excellent methods such as Markov random field models [17,18], graph-based segmentation methods [19][20][21][22], and variational model-based methods [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Multi-Phase Image Segmentation Based on Low-Rank Prior Decomposition

Guo

Huo

2022

IEEE Access

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Natural images usually contain structure component and texture component. The existing image segmentation models based on piecewise smooth cannot handle such natural images containing texture well. For this reason, in this paper, a multi-region image segmentation model based on low-rank prior decomposition is proposed. We use the low-rank prior to characterize the texture component, and the total variational norm to characterize the piecewise smooth structure component so that the proposed method can perform image decomposition and image segmentation tasks simultaneously. Then, the decomposed structure image can be used for image segmentation, and thus we can improve the accuracy of segmentation. To solve the new model, the alternating direction multiplier method is designed. The experimental results show that compared with the related classical models, the new model can significantly improve the subjective visual effect of image segmentation and the mean values of the precision rate, F1measure, and Jaccard similarity coefficient for the new model on the test images are at least 3.29%, 1.74%, and 3.13% higher, respectively..

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Fusion Between Shape Prior and Graph Cut for Vehicle Image Segmentation

Cited by 3 publications

References 16 publications

Double Thresholding with Sine Entropy for Thermal Image Segmentation

Double Thresholding with Sine Entropy for Thermal Image Segmentation

Synthesis and Comparison of Improved Edge Detection Technique Based on Metaheuristic and Intelligent Algorithm Optimization

Multi-Phase Image Segmentation Based on Low-Rank Prior Decomposition

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