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1,150 results on '"zhu, Jiahua"'

2. Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided Embedding

Author

Liu, Ziteng, Zhu, Jiahua, Liu, Bainan, Liu, Hao, Gao, Wenpeng, and Fu, Yili

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in providing vital information pertaining to smoke density. The reconstruction of smoke-free image is guided by a hybrid embedding, which combines the estimated smoke mask with the initial image. Experimental results demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that is $2.79\%$ higher than the state-of-the-art methods, while also exhibits a remarkable $38.2\%$ reduction in run-time. Overall, the proposed method offers comparable or even superior performance in terms of both smoke removal quality and computational efficiency when compared to existing state-of-the-art methods. This work will be publicly available on http://homepage.hit.edu.cn/wpgao, Comment: ISBI2024

Published
2024

30. Multilevel Image Thresholding Using a Fully Informed Cuckoo Search Algorithm

Author

Huang, Xiaotao, Shen, Liang, Fan, Chongyi, zhu, Jiahua, and Chen, Sixian

Subjects
Computer Science - Neural and Evolutionary Computing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Though effective in the segmentation, conventional multilevel thresholding methods are computationally expensive as exhaustive search are used for optimal thresholds to optimize the objective functions. To overcome this problem, population-based metaheuristic algorithms are widely used to improve the searching capacity. In this paper, we improve a popular metaheuristic called cuckoo search using a ring topology based fully informed strategy. In this strategy, each individual in the population learns from its neighborhoods to improve the cooperation of the population and the learning efficiency. Best solution or best fitness value can be obtained from the initial random threshold values, whose quality is evaluated by the correlation function. Experimental results have been examined on various numbers of thresholds. The results demonstrate that the proposed algorithm is more accurate and efficient than other four popular methods.

Published
2020

41. Novel Co-variant Feature Point Matching Based on Gaussian Mixture Model

Author

Shen, Liang, Zhu, Jiahua, Fan, Chongyi, Huang, Xiaotao, and Jin, Tian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The feature frame is a key idea of feature matching problem between two images. However, most of the traditional matching methods only simply employ the spatial location information (the coordinates), which ignores the shape and orientation information of the local feature. Such additional information can be obtained along with coordinates using general co-variant detectors such as DOG, Hessian, Harris-Affine and MSER. In this paper, we develop a novel method considering all the feature center position coordinates, the local feature shape and orientation information based on Gaussian Mixture Model for co-variant feature matching. We proposed three sub-versions in our method for solving the matching problem in different conditions: rigid, affine and non-rigid, respectively, which all optimized by expectation maximization algorithm. Due to the effective utilization of the additional shape and orientation information, the proposed model can significantly improve the performance in terms of convergence speed and recall. Besides, it is more robust to the outliers., Comment: arXiv admin note: text overlap with arXiv:0905.2635 by other authors

Published
2019

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