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Academic literature on the topic 'GrabCut algorithm'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "GrabCut algorithm"

1

Pang, Shangzhen, Tzer Hwai Gilbert Thio, Fei Lu Siaw, Mingju Chen, and Yule Xia. "Research on Improved Image Segmentation Algorithm Based on GrabCut." Electronics 13, no. 20 (2024): 4068. http://dx.doi.org/10.3390/electronics13204068.

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The classic interactive image segmentation algorithm GrabCut achieves segmentation through iterative optimization. However, GrabCut requires multiple iterations, resulting in slower performance. Moreover, relying solely on a rectangular bounding box can sometimes lead to inaccuracies, especially when dealing with complex shapes or intricate object boundaries. To address these issues in GrabCut, an improvement is introduced by incorporating appearance overlap terms to optimize segmentation energy function, thereby achieving optimal segmentation results in a single iteration. This enhancement significantly reduces computational costs while improving the overall segmentation speed without compromising accuracy. Additionally, users can directly provide seed points on the image to more accurately indicate foreground and background regions, rather than relying solely on a bounding box. This interactive approach not only enhances the algorithm’s ability to accurately segment complex objects but also simplifies the user experience. We evaluate the experimental results through qualitative and quantitative analysis. In qualitative analysis, improvements in segmentation accuracy are visibly demonstrated through segmented images and residual segmentation results. In quantitative analysis, the improved algorithm outperforms GrabCut and min_cut algorithms in processing speed. When dealing with scenes where complex objects or foreground objects are very similar to the background, the improved algorithm will display more stable segmentation results.

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Rui, Zhang, Na Ding, Xin-pin Lu, Ying-qi Xu, and Bin-jie Xin. "Fiber Identification in Cross Section of Blended Yarn on Back Propagation Neural Network." AATCC Journal of Research 8, no. 2_suppl (2021): 95–99. http://dx.doi.org/10.14504/ajr.8.s2.19.

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An intelligent recognition algorithm was developed to identify fibers in the cross sections of blended yarn containing meta-aramid 1313 (Nomex), poly(phenylene-1,3,4-oxadiazole) (POD), flame resistant viscose, and flame-resistant vinylon. The yarn cross section image was obtained at x400 magnification. Drawing software was used to manually isolate single fiber images for training the back propagation (BP) neural network model in Matlab language image processing software. The GrabCut algorithm was used to de-noise the image and separate the target from the background. Finally, single fiber images and fiber distributions were obtained through the program. The result showed that the BP neural network model with the GrabCut algorithm can identify fiber type in a complex background more easily and more accurately than traditional algorithms.

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Subaran, Tiara Lestari, Transmissia Semiawan, and Nurjannah Syakrani. "Mask R-CNN and GrabCut Algorithm for an Image-based Calorie Estimation System." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 1–10. http://dx.doi.org/10.20473/jisebi.8.1.1-10.

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Background: A calorie estimation system based on food images uses computer vision technology to recognize and count calories. There are two key processes required in the system: detection and segmentation. Many algorithms can undertake both processes, each algorithm with different levels of accuracy. Objective: This study aims to improve the accuracy of calorie calculation and segmentation processes using a combination of Mask R-CNN and GrabCut algorithms. Methods: The segmentation mask generated from Mask R-CNN and GrabCut were combined to create a new mask, then used to calculate the calorie. By considering the image augmentation technique, the accuracy of the calorie calculation and segmentation processes were observed to evaluate the method’s performance. Results: The proposed method could achieve a satisfying result, with an average calculation error value of less than 10% and an F1 score above 90% in all scenarios. Conclusion: Compared to earlier studies, the combination of Mask R-CNN and GrabCut could obtain a more satisfying result in calculating food calories with different shapes. Keywords: Augmentation, Calorie Calculation, Detection

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ZHOU, Liangfen, and Jiannong HE. "Improved image segmentation algorithm based on GrabCut." Journal of Computer Applications 33, no. 1 (2013): 49–52. http://dx.doi.org/10.3724/sp.j.1087.2013.00049.

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Duan, Fuzhou, Yanyan Wu, Hongliang Guan, and Chenbo Wu. "Saliency Detection of Light Field Images by Fusing Focus Degree and GrabCut." Sensors 22, no. 19 (2022): 7411. http://dx.doi.org/10.3390/s22197411.

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In the light field image saliency detection task, redundant cues are introduced due to computational methods. Inevitably, it leads to the inaccurate boundary segmentation of detection results and the problem of the chain block effect. To tackle this issue, we propose a method for salient object detection (SOD) in light field images that fuses focus and GrabCut. The method improves the light field focus calculation based on the spatial domain by performing secondary blurring processing on the focus image and effectively suppresses the focus information of out-of-focus areas in different focus images. Aiming at the redundancy of focus cues generated by multiple foreground images, we use the optimal single foreground image to generate focus cues. In addition, aiming at the fusion of various cues in the light field in complex scenes, the GrabCut algorithm is combined with the focus cue to guide the generation of color cues, which realizes the automatic saliency target segmentation of the image foreground. Extensive experiments are conducted on the light field dataset to demonstrate that our algorithm can effectively segment the salient target area and background area under the light field image, and the outline of the salient object is clear. Compared with the traditional GrabCut algorithm, the focus degree is used instead of artificial Interactively initialize GrabCut to achieve automatic saliency segmentation.

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Xu, Jianyun, Hui Wang, Shuang Lin, and Li He. "Design of an Image Integrated Processing System for Improving Efficiency of Electric Vehicle Supporting Products." Mobile Information Systems 2022 (August 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/1949962.

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This study analyzes a solution that requires efficient and comprehensive processing of images of a large number of vehicles and their related parts, such as batteries, plastic fastening components, and brake discs, during the design investigation of electric vehicle accessories. The problem involves the extraction of the outer contours of different components, which is important to build a comprehensive image processing system that can handle different vehicle accessories. In this study, a comprehensive image processing system is proposed, which introduces an improved GrabCut and computer vision methods. It can complete the positioning of vehicle batteries, the fastening of automobile components, and the identification of brake discs, which improves the efficiency of inspection and design work. The improved GrabCut uses adaptive median filtering on the electric car accessory to reduce noise from the surface in variable degrees. The image is then sharpened using the Laplacian operator, followed by a contrast-limited histogram equalization (CLAHE) algorithm to boost the image brightness. We have compared our proposed work against existing techniques, i.e., the GrabCut algorithm, region growing algorithm, and K-means algorithm. The comparison clearly shows that our proposed work achieves a much better peak signal-to-noise ratio value as compared to the existing techniques.

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Yang, Chongyi, Wanyu Huang, Ruoqi Zhang, and Rui Kong. "Portrait Extraction Algorithm Based on Face Detection and Image Segmentation." Computer and Information Science 12, no. 2 (2019): 1. http://dx.doi.org/10.5539/cis.v12n2p1.

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Aiming to solve a series of problems in photo collection over citizen&rsquo;s license, this paper proposes Portrait Extraction Algorithm over our face based on facial detection technology and state-of-the-art image segmentation algorithm. Considering an input image where the foreground stands a man with unfixed size and its background is all sorts of complicated background, firstly we use Haar&amp;Adaboost facial detection algorithm as a preprocessing method so as to divide the image into different sub-systems, and we get a fix-sized image of human face. Then we use GrabCut and closed-form algorithm to segment the preprocessed image and output an image which satisfies our requirements (i.e. the fixed size and fixed background). Up to now the GrabCut and closed-form algorithm has been realized, both of which have its own advantages and shortages.

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Zhang Cuijun, 张翠军, and 赵娜 Zhao Na. "Improved GrabCut Algorithm Based on Probabilistic Neural Network." Laser & Optoelectronics Progress 58, no. 2 (2021): 0210024. http://dx.doi.org/10.3788/lop202158.0210024.

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Yong, Zhang, Yuan Jiazheng, Liu Hongzhe, and Li Qing. "GrabCut image segmentation algorithm based on structure tensor." Journal of China Universities of Posts and Telecommunications 24, no. 2 (2017): 38–47. http://dx.doi.org/10.1016/s1005-8885(17)60197-3.

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Rui, Wang, Jin Ye Peng, Li Ping Che, and Yu Ting Hou. "Improved Color Image Segmentation Algorithm Based on GrabCut." Applied Mechanics and Materials 373-375 (August 2013): 464–67. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.464.

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In realistic image processing, it is a problem of image foreground extraction. For a large number of color image processing, an important requirement is the automation of the extraction process. In this paper, by automatically setting foreground seed, we improve the image existing segmentation algorithm; by automatically searching image segmentation region, we accomplish image segmentation with the GrabCut algorithm, which is based on Gaussian Mixture Model and boundary computing. The improved algorithm in this paper can achieve the automation of image segmentation, without user participation in the implementation process, at the same time, it improves the efficiency of image segmentation, and gets a good result of image segmentation in complex background.

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Book chapters on the topic "GrabCut algorithm"

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Ždímalová, Mária, and Kristína Boratková. "Optimized GrabCut Algorithm in Medical Image Analyses." In Third Congress on Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9225-4_9.

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Li, Shuai, Xiaohui Zheng, Xianjun Chen, and Yongsong Zhan. "Portrait Image Segmentation Based on Improved Grabcut Algorithm." In Advances in Swarm and Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20469-7_36.

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Yamasaki, Yuuki, Masahiro Migita, Go Koutaki, Masashi Toda, and Tsuyoshi Kishigami. "ISHIGAKI Region Extraction Using Grabcut Algorithm for Support of Kumamoto Castle Reconstruction." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81638-4_9.

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Rygał, Janusz, Patryk Najgebauer, Jakub Romanowski, and Rafał Scherer. "Extraction of Objects from Images Using Density of Edges as Basis for GrabCut Algorithm." In Artificial Intelligence and Soft Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38658-9_56.

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Pareek, Anshul, Poonam Dahiya, and Shaifali M. Arora. "SURF-Based Algorithm to Deal with Pose Change Challenge in Human Tracking." In New Frontiers in Communication and Intelligent Systems. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-26.

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Most of the existing interest point-based methods do not deal with tracking drift owing to out-of-plane rotations based pose change. It continues to remain a big challenge for researchers. To address the issue, a SURF-based algorithm is developed in which the object model is upgraded during the course of tracking, for this new templates are selected whenever pose change is encountered. In this process, the fresh projecting points are added to the template pool extracted from previously generated templates employing affine transformation by calculating its aspect ratio. These works propose a novel implementation of the GRABCUT algorithm on interest point-based methods. This region growing algorithm eliminates the background descriptors from the object model and this information is used by a SURF-based tracker. Later to differentiate between pose change and occlusion situation an Autotuned classifier is implemented. The human tracking algorithm developed in this paper are computable in real-time and real-time experiments are conducted in indoor as well as in outdoor environments.

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Conference papers on the topic "GrabCut algorithm"

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Jeebaratnam, N., Chinmayee Dora, and Dhawaleswar Rao CH. "A Study on Banana Leaf Disease Detection Based on Grabcut Algorithm Using Multispectral Camera." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10991072.

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Li, Chao, Xiaozhen Zhao, and Huiying Ru. "GrabCut Algorithm Fusion of Extreme Point Features." In 2021 International Conference on Intelligent Computing, Automation and Applications (ICAA). IEEE, 2021. http://dx.doi.org/10.1109/icaa53760.2021.00014.

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Yiran He and Yongqi Sun. "An automatic image segmentation algorithm based on GrabCut." In 6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015). Institution of Engineering and Technology, 2015. http://dx.doi.org/10.1049/cp.2015.0937.

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Yang, Lei, Xiaoyu Wu, Yiming Guo, and Shaobin Li. "An interactive video segmentation approach based on GrabCut algorithm." In 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6100014.

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Hao, Lili, Jianrong Cao, and Chengdong Li. "Research of GrabCut algorithm for single camera video synopsis." In 2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP). IEEE, 2013. http://dx.doi.org/10.1109/icicip.2013.6568151.

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Li, XiaoQi, YeLi Li, and YaLi Qi. "Automatic GrabCut color Image Segmentation Based on EM Algorithm." In 4th International Conference on Computer, Mechatronics, Control and Electronic Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/iccmcee-15.2015.2.

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Yeh, Jui-Feng, Kuei-Mei Lin, Chen-Yu Lin, and Jen-Chun Kang. "Intelligent Mango Fruit Glade Classification Using AlexNet with GrabCut Algorithm." In 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 2021. http://dx.doi.org/10.1109/icce-tw52618.2021.9603074.

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Wu, Zeju, Liang Chen, and Cuijuan Jiao. "Improved GrabCut Algorithm Based On RANSAC For Tire Belt Image Segmentation." In ICBDT 2020: 2020 3rd International Conference on Big Data Technologies. ACM, 2020. http://dx.doi.org/10.1145/3422713.3422736.

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Yao, Huan, Zhikun Jia, Afen Zhou, et al. "Lightning Arrester Target Segmentation Algorithm Based on Improved DeepLabv3+ and GrabCut." In 2022 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2022. http://dx.doi.org/10.1109/icma54519.2022.9856256.

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Jiang, Shuihan, Yunqi Gao, Changying Wang, Junting Qi, Li Cheng, and Xiaojuan Zhang. "Background Subtraction Algorithm Based on Combination of Grabcut and Improved ViBe." In CCRIS 2020: 2020 International Conference on Control, Robotics and Intelligent System. ACM, 2020. http://dx.doi.org/10.1145/3437802.3437811.

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