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Multidimensional Systems and Signal Processing最新文献

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Seizure disorders recognition model from EEG signals using new probabilistic particle swarm optimizer and sequential differential evolution 基于概率粒子群优化器和序列微分进化的脑电癫痫识别模型
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-02-13 DOI: 10.1007/s11045-023-00870-2
Anuradha Thakare, A. Anter, A. Abraham
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引用次数: 3
Correction: Saturation-value based higher-order regularization for color image restoration 校正:基于饱和度值的高阶正则化用于彩色图像恢复
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-02-09 DOI: 10.1007/s11045-023-00872-0
Miyoun Jung
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引用次数: 0
LBCRN: lightweight bidirectional correction residual network for image super-resolution LBCRN:用于图像超分辨率的轻量级双向校正残差网络
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-02-09 DOI: 10.1007/s11045-023-00866-y
Shuying Huang, Jichao Wang, Yong Yang, Weiguo Wan, Guo-Chao Li
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引用次数: 0
Saturation-value based higher-order regularization for color image restoration 基于饱和度值的高阶正则化彩色图像恢复
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-02-09 DOI: 10.1007/s11045-023-00867-x
Miyoun Jung
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引用次数: 0
Coherent signal localization with multiple arrays: a successive perspective 多阵列相干信号定位的连续视角
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-01-23 DOI: 10.1007/s11045-022-00862-8
Haowei Zeng, Shao Shi, Heng Yue, Jianfeng Li
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引用次数: 0
Few-shot learning based blind parameter estimation for multiple frequency-hopping signals 基于少弹学习的多跳频信号盲参数估计
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-01-11 DOI: 10.1007/s11045-022-00865-5
Keyu Lu, Zhisheng Qian, Manxi Wang, Dewang Wang
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引用次数: 0
RTR-DPD: reweighted tikohonov regularization for blind deblurring via dual principles of discriminativeness RTR-DPD:基于双重判别原则的重加权tikohonov正则化盲去模糊
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-01-11 DOI: 10.1007/s11045-022-00863-7
W. Shao, H. Deng, Qi Ge, Liqian Wang
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引用次数: 0
A Robust edge detection technique for bone extraction from X-ray images based on image processing techniques 基于图像处理技术的x射线图像骨骼提取鲁棒边缘检测技术
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-01-09 DOI: 10.1007/s11045-022-00860-w
Nashaat M. Hussain Hassan, Mohamed Hassan M. Mahmoud
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引用次数: 0
Reduced-order modeling methods via bivariate discrete orthogonal polynomials for two-dimensional discrete state-delayed systems 二维离散状态延迟系统的二元离散正交多项式降阶建模方法
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-01-07 DOI: 10.1007/s11045-022-00864-6
Zhaoxi Wang, Yaolin Jiang, Kangli Xu
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引用次数: 0
Noisy iris smoothing and segmentation scheme based on improved Wildes method. 基于改进Wildes方法的噪声虹膜平滑与分割方案。
IF 2.5 4区 工程技术 Q2 COMPUTER SCIENCE, THEORY & METHODS
Multidimensional Systems and Signal Processing
Pub Date : 2023-01-01 DOI: 10.1007/s11045-022-00852-w
Anchal Kumawat, Sucheta Panda

In an automated iris recognition system, in order to get higher accuracy, we should have an efficient iris segmentation process. The reliability of accurate "iris recognition" system largely depends on the accuracy of segmentation process. Traditional "iris segmentation" methods are unable to detect the exact boundaries of iris and pupil, which is time consuming and also highly sensitive to noise. To overcome these problems, we have proposed an improved Wildes method (IWM) for segmentation in iris recognition system. The proposed algorithm consists of two major steps before applying Wildes method for segmentation: edge detection of iris and pupil from a noisy eye image with improved Canny with fuzzy logic (ICWFL) and removal of unwanted noise from above step with a hybrid restoration fusion filter (HRFF). A comparative study of various edge detection techniques is performed to prove the efficiency of ICWFL method. Similarly, the proposed method is tested with various noise densities from 10 to 95 dB. Also the working of the proposed HRFF is compared with some existing smoothing filters. Various experiments have been performed with the help of iris database of IIT_Delhi. Both visual and numerical results prove the efficiency of the proposed algorithm.

在自动虹膜识别系统中，为了获得更高的识别精度，必须有一个高效的虹膜分割过程。准确的“虹膜识别”系统的可靠性很大程度上取决于分割过程的准确性。传统的“虹膜分割”方法无法准确检测出虹膜和瞳孔的边界，耗时长，对噪声高度敏感。为了克服这些问题，我们提出了一种改进的Wildes分割方法(IWM)用于虹膜识别系统。该算法在应用Wildes方法进行分割之前，主要包括两个步骤:利用改进的模糊逻辑Canny (ICWFL)对带有噪声的眼睛图像进行虹膜和瞳孔边缘检测，并利用混合恢复融合滤波器(HRFF)去除上述步骤中不需要的噪声。通过对各种边缘检测技术的比较研究，证明了ICWFL方法的有效性。同样，该方法在10 ~ 95 dB的不同噪声密度下进行了测试。并与现有的几种平滑滤波器进行了比较。利用印度理工学院的鸢尾花数据库进行了各种实验。视觉和数值结果都证明了该算法的有效性。
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引用次数: 0
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