Image Thresholding Segmentation on Quantum State Space
Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution,...
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| Format: | Article |
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MDPI AG
2018-09-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/10/728 |
| _version_ | 1817995159862771712 |
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| author | Xiangluo Wang Chunlei Yang Guo-Sen Xie Zhonghua Liu |
| author_facet | Xiangluo Wang Chunlei Yang Guo-Sen Xie Zhonghua Liu |
| author_sort | Xiangluo Wang |
| collection | DOAJ |
| description | Aiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searching for optimal thresholds, the method of quantum lossy-encoding-based entropy maximization (QLEEM) is presented, in which the eigenvalues of density matrices can give direct clues for thresholding, and then, the process of optimal searching can be avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the upper bound of the thresholding level can be implicitly determined according to the eigenvalues; and (2) the proposed approaches ensure that the local information in images is retained as much as possible, and simultaneously, the inter-class separability is maximized in the segmented images. Both of them contribute to the structural characteristics of images, which the human visual system is highly adapted to extract. Experimental results show that the proposed methods are able to achieve a competitive quality of thresholding and the fastest computation speed compared with the state-of-the-art methods. |
| first_indexed | 2024-04-14T02:01:58Z |
| format | Article |
| id | doaj.art-e9f590c0f403415a9e9fb8f7f42c28b0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T02:01:58Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e9f590c0f403415a9e9fb8f7f42c28b02022-12-22T02:18:48ZengMDPI AGEntropy1099-43002018-09-01201072810.3390/e20100728e20100728Image Thresholding Segmentation on Quantum State SpaceXiangluo Wang0Chunlei Yang1Guo-Sen Xie2Zhonghua Liu3School of Information Technology, Luoyang Normal University, Luoyang 471934, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaSchool of Information Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaAiming to implement image segmentation precisely and efficiently, we exploit new ways to encode images and achieve the optimal thresholding on quantum state space. Firstly, the state vector and density matrix are adopted for the representation of pixel intensities and their probability distribution, respectively. Then, the method based on global quantum entropy maximization (GQEM) is proposed, which has an equivalent object function to Otsu’s, but gives a more explicit physical interpretation of image thresholding in the language of quantum mechanics. To reduce the time consumption for searching for optimal thresholds, the method of quantum lossy-encoding-based entropy maximization (QLEEM) is presented, in which the eigenvalues of density matrices can give direct clues for thresholding, and then, the process of optimal searching can be avoided. Meanwhile, the QLEEM algorithm achieves two additional effects: (1) the upper bound of the thresholding level can be implicitly determined according to the eigenvalues; and (2) the proposed approaches ensure that the local information in images is retained as much as possible, and simultaneously, the inter-class separability is maximized in the segmented images. Both of them contribute to the structural characteristics of images, which the human visual system is highly adapted to extract. Experimental results show that the proposed methods are able to achieve a competitive quality of thresholding and the fastest computation speed compared with the state-of-the-art methods.http://www.mdpi.com/1099-4300/20/10/728image segmentationthresholdingvon Neumann entropydensity matrix |
| spellingShingle | Xiangluo Wang Chunlei Yang Guo-Sen Xie Zhonghua Liu Image Thresholding Segmentation on Quantum State Space Entropy image segmentation thresholding von Neumann entropy density matrix |
| title | Image Thresholding Segmentation on Quantum State Space |
| title_full | Image Thresholding Segmentation on Quantum State Space |
| title_fullStr | Image Thresholding Segmentation on Quantum State Space |
| title_full_unstemmed | Image Thresholding Segmentation on Quantum State Space |
| title_short | Image Thresholding Segmentation on Quantum State Space |
| title_sort | image thresholding segmentation on quantum state space |
| topic | image segmentation thresholding von Neumann entropy density matrix |
| url | http://www.mdpi.com/1099-4300/20/10/728 |
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