Highly efficient neoteric histogram–entropy‐based rapid and automatic thresholding method for moving vehicles and pedestrians detection

Chandrasekar, K.; Geetha, P.

doi:10.1049/iet-ipr.2018.5555

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…The maximum entropy thresholding method divides the image into regions of interest and background, where the threshold that maximizes the sum of the target entropy and background entropy is considered the optimal segmentation threshold. The conventional method of thresholding using one-dimensional grayscale histograms does not consider spatial information of the image and is susceptible to noise interference [28]. In this paper, the two-dimensional maximum entropy thresholding method considering both the pixel grayscale value and its neighborhood average grayscale value is used as an example.…”

Section: Maximum Entropy Threshold Image Segmentation Methodsmentioning

confidence: 99%

“…Based on the analysis above, it is known that the maximum entropy segmentation algorithm, as a simple image post-processing method, requires significant computational time due to the entropy calculation. To reduce the optimization time for threshold determination, previous studies have utilized optimization algorithms such as particle swarm optimization [28]. To address this issue, this paper introduces the boundary array electrode measurement signal as a known condition in the threshold determination process.…”

Section: Soft-threshold Region Segmentation Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

Song,

Xiong,

Liu

et al. 2024

Applied Sciences

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The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

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Section: Maximum Entropy Threshold Image Segmentation Methodsmentioning

confidence: 99%

Section: Soft-threshold Region Segmentation Algorithmmentioning

confidence: 99%

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

Song,

Xiong,

Liu

et al. 2024

Applied Sciences

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show abstract

“…The algorithm has a good detection effect and real-time high sex has been widely used in practical engineering. Chandrasekar KS et al proposed a sample consistency algorithm, which determines whether a pixel is a front sight or a background by performing sample consistency judgment on each pixel [17]. Most of the background difference methods proposed later.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Spatial Segmentation of Difference Image Motion Changes

et al. 2020

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In this paper, real-time detection of moving targets is performed, and an improved moving target detection algorithm combining background modeling and three-frame difference method is studied. An improved anisotropic differential filtering algorithm is proposed. The algorithm mainly compares the gradient difference between the target and the background in eight nearby directions. Choose the average of the three directions with the smallest spread function value to filter the space, which will effectively highlight the difference between the targets. so that the target signal is well preserved in the differential space domain; then based on obtaining the difference map. The average gray level of the target and the local signal-to-noise ratio of the spatial domain obtained by the energy enhancement algorithm combining the spatial and temporal motion characteristics have reached 278 and 14.84 dB, which further improves the target signal. Strict registration of heterogeneous spatial domains is completed by affine transformation and bilinear interpolation, and in many cases, the invariance and robustness of the method are verified. Focus on the proposed color space fusion method based on spatial frequency and fuzzy transformation. The superiority of the methods is evaluated and compared from both subjective and objective aspects. Finally, it lays a good foundation for moving target detection and better dealing with complex scenes. The improved inter-frame difference method combined with background subtraction validates and compares the method in different scenarios. It shows the advanced nature of the method in both qualitative and quantitative aspects. And effectively overcome the shortcomings of the particle filter tracking algorithm based on a single feature.

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“…In the application research of PSO algorithm, Wang Y et al realized recursive network design by using two evolutionary learning algorithms of genetic algorithm and PSO algorithm [25]; Reily B et al once proposed a two-stage PSO algorithm to solve the random shop scheduling problem [26]; Chandrasekar KS once proposed a multi-objective feature selection method based on PSO algorithm and fusion of cross operation, mutation operation and crowded distance technology, and named CMDPSO, through this method can obtain a better feature subset set, etc. ; as more scholars join in the more in-depth research on the PSO algorithm, the PSO algorithm will be used in more fields the PSO algorithm has been applied to practical problems, and the research on practical problems will become more and more perfect and mature [27].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Simulation of Garden Image Visual Effect Based on Particle Swarm and Wavelet Threshold

Hua

Min

2020

IEEE Access

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This paper optimizes and simulates the visual effects of garden graphics based on particle swarm and wavelet threshold algorithm, and uses deep belief networks as the main body of the classifier. To make up for the shortcomings of the activation function of the deep belief network, this paper adopts the wavelet basis function as the activation function of the deep belief network to improve the recognition accuracy of the deep belief network, especially the recognition accuracy of small changes in the garden image information. It analyzes the learning process of deep belief network in detail, finds out the shortcomings of traditional algorithms, and optimizes them, combined with particle swarm optimization algorithm to construct a wavelet deep belief network model to further improve the recognition accuracy and recognition speed of garden image recognition. A hybrid optimization algorithm of genetic and particle swarm algorithms is proposed. The two algorithms complement each other and the idea of crossover and variation is introduced into the standard Particle Swarm Optimization (PSO) algorithm to avoid premature convergence of solutions obtained by the PSO algorithm and lead to local optimal solutions. Seven optimal solutions obtained by the algorithm are used as the weight of the sample features, which are multiplied by the sample features to obtain the input data of the weighted K-nearest neighbor algorithm. After that, a three-fold cross-validation method is utilized to train the data to ensure the classification effect of the data set. The weighted K-nearest neighbor algorithm has the best classification effect based on the hybrid optimization of genetic algorithms and particle swarm algorithm.

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Highly efficient neoteric histogram–entropy‐based rapid and automatic thresholding method for moving vehicles and pedestrians detection

Cited by 6 publications

References 53 publications

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

Real-Time Detection and Spatial Segmentation of Difference Image Motion Changes

Optimization and Simulation of Garden Image Visual Effect Based on Particle Swarm and Wavelet Threshold

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