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Computer Science, Artificial Intelligence
Bo Lei, Jiulun Fan
Summary: This paper proposes a novel infrared pedestrian segmentation algorithm based on two-dimensional Kaniadakis entropy thresholding, which introduces spatial information of pixels and an intensity suppressed strategy to effectively address noise and complex background in infrared images. The experimental results demonstrate the effectiveness of the proposed method in comparison to state-of-the-art image segmentation methods.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Information Systems
Xin Fan, Junyan Wang, Haifeng Wang, Changgao Xia
Summary: A histogram-constrained and contrast-tunable HE technique for digital image enhancement is proposed in this paper, which partitions the input image histogram into two parts and redistributes them to achieve more accurate results in terms of information entropy and MS-SSIM compared to other algorithms.
Article
Computer Science, Information Systems
Haniza Yazid, Shafriza Nisha Basah, Saufiah Abdul Rahim, Muhammad Juhairi Aziz Safar, Khairul Salleh Basaruddin
Summary: This paper analyzes the success of image segmentation methods under different conditions. The main contribution is the analysis of entropy thresholding and the proposal of conditions for successful image segmentation.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Omar A. Kittaneh
Summary: This paper proposes a new multi-level entropy-based image thresholding method that relies on the minimum of the variance entropy. The method is fully automated and produces segmentation results comparable to the generalized Otsu's method, which requires human intervention. It also outperforms the generalized Kapur's method in benchmarking entropy-based thresholding techniques. The method is successfully applied to various scenarios and its performance is checked using classification measures and quality metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Andrea H. del Rio, Itzel Aranguren, Diego Oliva, Mohamed Abd Elaziz, Erik Cuevas
Summary: The paper introduces a new method for multilevel image thresholding segmentation based on iOSA and 2D histograms, which enhances performance by introducing new optimization strategies and applying opposition-based learning, while maintaining more image information to explore the search space better.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Mathematics
Jorge Munoz-Minjares, Osbaldo Vite-Chavez, Jorge Flores-Troncoso, Jorge M. Cruz-Duarte
Summary: This paper proposes a strategy for object segmentation based on CSA and GG distribution, and validates its advantages in both synthetic and practical scenarios through experiments. The results show that this strategy outperforms other algorithms in simulated environments and ranks among the best algorithms in real-world scenarios.
Article
Computer Science, Information Systems
Zheping Yan, Jinzhong Zhang, Zewen Yang, Jialing Tang
Summary: This paper proposes a whale optimization algorithm (WOA) based on Kapur's entropy method for image segmentation, and experimental results show that it outperforms other comparison algorithms with higher segmentation accuracy, better segmentation effect, and stronger robustness.
Article
Agriculture, Multidisciplinary
Arun Kumar, A. Kumar, Amit Vishwakarma, Girish Kumar Singh
Summary: This paper presents a crop image multilevel thresholding technique based on the recursive minimum cross entropy method and efficient cuckoo search algorithm. Experimental results demonstrate that the proposed method can accurately and efficiently segment crop images with complex backgrounds.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Computer Science, Information Systems
Simrandeep Singh, Nitin Mittal, Harbinder Singh, Diego Oliva
Summary: Image segmentation is a critical stage in image analysis and pre-processing, where pixels are divided into segments based on threshold values. Multi-level thresholding approaches are more effective than bi-level methods, and a new modified Otsu function is proposed that combines Otsu's between-class variance and Kapur's entropy. Experimental results demonstrate the high efficiency of the modified Otsu method in terms of performance metrics.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Mohammad Reza Naderi Boldaji, Samaneh Hosseini Semnani
Summary: This paper presents a new method for unsupervised image segmentation by combining different multi-objective swarm intelligence algorithms and histogram thresholding methods. It optimizes a vector objective function to simultaneously handle the segmentation of entire image color channels with the same thresholds. The proposed method requires fewer thresholds and less memory space compared to traditional thresholding methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Mathematical & Computational Biology
Wenqi Ji, Xiaoguang He
Summary: The paper presents a moth-flame optimization (MFO) method based on Kapur's entropy to address the issues of low segmentation accuracy and high computational complexity in multilevel thresholding image segmentation. Experimental results demonstrate that MFO has better calculation accuracy, segmentation effect, and stability.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
J. Anitha, S. Immanuel Alex Pandian, S. Akila Agnes
Summary: This paper proposes a modified whale optimization algorithm for optimizing the selection of multilevel color image thresholds. The algorithm achieves a proper balance between exploration and exploitation phases, thus avoiding the issue of local optima.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Caijie Shang, Dong Zhang, Yan Yang
Summary: A gradient-based method different from bionic algorithms is proposed in this paper for optimal multilevel thresholds search in image segmentation. Experiments show that this method is efficient in computation and has equal or better performance of segmentation compared to other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Instruments & Instrumentation
He Zhang, Weixian Qian, Minjie Wan, Kaimin Zhang
Summary: This paper proposes an infrared image enhancement method using local entropy mapping histogram adaptive segmentation, which effectively solves the problems of over-enhancement and noise amplification in traditional histogram equalization algorithm.
INFRARED PHYSICS & TECHNOLOGY
(2022)
Article
Computer Science, Hardware & Architecture
Fusong Xiong, Jian Zhang, Yun Ling, Zhiqiang Zhang
Summary: This paper proposed a new image thresholding method based on entropy and Parzen window estimation, which creates a new objective function by combining probability and entropy information. Experimental results show that the proposed method outperforms other classical thresholding methods in terms of accuracy, robustness, and visual effect.
Article
Engineering, Electrical & Electronic
Fangyan Nie
JOURNAL OF ELECTRONIC IMAGING
(2015)
Article
Engineering, Biomedical
Mei-Sen Pan, Fen Zhang, Qiu-Sheng Rong, Hui-Can Zhou, Fang-Yan Nie
BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS
(2011)
Article
Physics, Multidisciplinary
Peng Guang-Han, Nie Fang-Yan, Wang Sheng-Hui
COMMUNICATIONS IN THEORETICAL PHYSICS
(2013)
Article
Computer Science, Hardware & Architecture
Fangyan Nie, Chao Gao, Yongcai Guo, Min Gan
COMPUTERS & ELECTRICAL ENGINEERING
(2011)
Article
Computer Science, Theory & Methods
Fangyan Nie, Yonglin Wang, Meisen Pan, Guanghan Peng, Pingfeng Zhang
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING
(2013)
Article
Computer Science, Artificial Intelligence
Fangyan Nie, Jianqi Li, Tianyi Tu, Meisen Pan
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS
(2014)
Proceedings Paper
Automation & Control Systems
Fangyan Nie, Tianyi Tu, Meisen Pan, Qiusheng Rong, Huican Zhou
ADVANCES IN ELECTRICAL ENGINEERING AND AUTOMATION
(2012)
Article
Engineering, Electrical & Electronic
Alam Abbas Syed, Hassan Foroosh
Summary: This paper presents effective methods using spherical polar Fourier transform data for two different applications: 3D volumetric registration and machine learning classification network. The proposed method for registration offers unique and effective techniques, handling arbitrary large rotation angles and showing robustness. The modified classification network achieves robust classification results in processing spherical data.
Article
Engineering, Electrical & Electronic
Ruibo Fan, Mingli Jing, Jingang Shi, Lan Li, Zizhao Wang
Summary: In this study, a new low-rank sparse decomposition algorithm named TVRPCA+ is proposed for foreground-background separation. The algorithm combines spectral norm, structured sparse norm, and total variation regularization to suppress noise and obtain cleaner foregrounds. Experimental results demonstrate that TVRPCA+ achieves high performance in complex backgrounds and noise scenarios.
Article
Engineering, Electrical & Electronic
Omair Aldimashki, Ahmet Serbes
Summary: This paper proposes a coarse-to-fine FrFT-based algorithm for chirp-rate estimation of multi-component LFM signals, which achieves improved performance and a reduced signal-to-noise breakdown threshold by utilizing mathematical models for coarse estimation and a refined estimate-and-subtract strategy. Extensive simulation results demonstrate that the proposed algorithm performs very close to the Cramer-Rao lower bound, with the advantages of eliminating leakage effect, avoiding error propagation, and maintaining acceptable computational cost compared to other state-of-the-art methods.
Article
Engineering, Electrical & Electronic
Xinlei Shi, Xiaofei Zhang, Yuxin Sun, Yang Qian, Jinke Cao
Summary: In this paper, a low-complexity localization approach for multiple sources using two-dimensional discrete Fourier transform (2D-DFT) is proposed. The method computes the cross-covariance and utilizes phase offset method and total least square solution to obtain accurate position estimates.
Article
Engineering, Electrical & Electronic
Prabhanjan Mannari, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan
Summary: This paper discusses the problem of extended target tracking for a single 2D extended target with a known convex polytope shape and dynamics. It proposes a framework based on the existing point multitarget tracking framework to address the challenges of uncertainty in shape and kinematics, as well as self-occlusion. The algorithm developed using this framework is capable of dynamically changing the number of parameters used to describe the shape and estimating the whole target shape even when different parts of the target are visible at different frames.
Article
Engineering, Electrical & Electronic
Yongsong Li, Zhengzhou Li, Jie Li, Junchao Yang, Abubakar Siddique
Summary: This paper proposes a weighted adaptive ring top-hat transformation (WARTH) for extracting infrared small targets in complex backgrounds. The WARTH method effectively measures local and global feature information using an adaptive ring-shaped structural element and a target awareness indicator, resulting in accurate detection of small targets with minimized false alarms.
Article
Engineering, Electrical & Electronic
Yu Wang, Zhen Qin, Jun Tao, Yili Xia
Summary: In this paper, an enhanced sparsity-aware recursive least squares (RLS) algorithm is proposed, which combines the proportionate updating (PU) and zero-attracting (ZA) mechanisms, and introduces a general convex regularization (CR) function and variable step-size (VSS) technique to improve performance.
Article
Engineering, Electrical & Electronic
Neil J. Bershad, Jose C. M. Bermudez
Summary: This paper analyzes the impact of processing delay on the Least Mean Squares (LMS) algorithm in system identification, highlighting bias issues in the resulting weight vector.
Article
Engineering, Electrical & Electronic
Kanghui Jiang, Defu Jiang, Mingxing Fu, Yan Han, Song Wang, Chao Zhang, Jingyu Shi
Summary: In this paper, a novel method for velocity estimation using multicarrier signals in a single dwell is proposed, which effectively addresses the issue of Doppler ambiguity in pulse Doppler radars.
Article
Engineering, Electrical & Electronic
Xiao-Jun Zhang, Peng-Lang Shui, Yu-Fan Xue
Summary: This paper proposes a method for low-velocity small target detection in maritime surveillance radars. It models sea clutter sequences using the spherical invariant random vector (SIRV) model with block tridiagonal speckle covariance matrix and inverse Gamma distributed texture. The proposed detector, which is a long-time adaptive generalized likelihood ratio test with linear threshold detector (GLRT-LTD), shows competitive detection performance in experiments.
Article
Engineering, Electrical & Electronic
Aiyi Zhang, Fulai Liu, Ruiyan Du
Summary: This paper proposes an adaptive weighted robust data recovery method with total variation regularization for hyperspectral image. The method models the HSI recovery problem as a tensor robust principal component analysis optimization problem, decomposing the data into low-rank HSI data, outliers, and noise component. An adaptive weighted strategy is then defined to impose on the tensor nuclear norm and outliers, using the priori information of singular values and strengthening the sparsity of outliers.
Article
Engineering, Electrical & Electronic
Hamid Asadi, Babak Seyfe
Summary: This paper presents a novel approach for estimating the model order in the presence of observation errors. The proposed method is based on correntropy estimation of eigenvalues in the observation space, which is further enhanced by resampling the observations using the bootstrap method. The algorithm partitions the observation space into signal and noise subspaces using the covariance matrix of mixtures, and determines the model order based on a correntropy estimator with kernel functions. Theoretical analysis and comparative evaluations demonstrate the superiority of this information-theoretic approach.
Article
Engineering, Electrical & Electronic
Buket colak Guvenc, Engin Cemal Menguc
Summary: In this paper, a novel family of online censoring based complex-valued least mean kurtosis (CLMK) algorithms is proposed. The algorithms censor less informative complex-valued data streams and reduce the costs of data processing without affecting accuracy. Robust algorithms are also developed to handle outliers. The simulation results confirm the attractive features of the proposed algorithms in large-scale system identification and regression scenarios.
Article
Engineering, Electrical & Electronic
Yun Su, Weixian Tan, Yifan Dong, Wei Xu, Pingping Huang, Jianxin Zhang, Diankun Zhang
Summary: In this study, a novel method for detecting low-resolution and small targets in millimeter wave radar images is proposed. The Wavelet-Conv structure and Wavelet-Attention mechanism are introduced to overcome the limitations of existing detectors. Experimental results demonstrate that the proposed method improves recall and mean average precision while maintaining competitive inference speed.
Article
Engineering, Electrical & Electronic
Xin Wang, Xingxing Jiang, Qiuyu Song, Jie Liu, Jianfeng Guo, Zhongkui Zhu
Summary: This study proposes a variational mode extraction (VME) method for extracting specific modes from complicated signals. By exploring the convergence property of VME, strategies for identifying ICF and determining the balance parameter are designed, and a bandwidth estimation strategy is constructed. The effectiveness of the proposed method for bearings fault diagnosis is verified and compared with other methods.
