Multi-sensor Image Decision Level Fusion Detection Algorithm Based on D-S Evidence Theory

Wang, Aili; Jiang, Jinna; Zhang, Haoye

doi:10.1109/imccc.2014.132

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…The original image information collected by the camera belongs to the pixel coordinate system. Therefore, it is necessary to transform the 2D image into a 3D space under the camera coordinate system through an internal parameter transformation and set the initial depth information s, as shown in equation (5).…”

Section: Fuzzy Mappingmentioning

confidence: 99%

Three-dimensional contour detection method based on fusion of machine vision and laser radar

Jun,

Shuo,

Shaobo

et al. 2024

Meas. Sci. Technol.

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In the current methods of point cloud processing, there are still several limitations, particularly in achieving high precision and accuracy for large objects in complex environments. Existing techniques often struggle with incomplete or noisy data, leading to inaccurate contour extraction. In view of the challenges associated with the sparse and discrete nature of point clouds in complex environments, which lead to poor accuracy and stability in object contour extraction, this paper proposes a novel method for accurately extracting the contours of three-dimensional target point clouds. The method integrates high-resolution images with sparse point cloud information to address these issues. Firstly, the local characteristics of the point cloud are calculated, allowing for the selection of a contour point cloud. Next, depth information from two-dimensional images is obtained through a fuzzy mapping relationship. Finally, constraint conditions are established to derive a more accurate predicted value of the contour point cloud. Experiments demonstrate that the proposed method effectively improves the precision and accuracy of contour extraction for large objects, reducing measurement deviation by approximately 64.9% compared to using the original point cloud alone. Additionally, the method shows a more accurate completion effect on parts of the contour that are missing, underscoring its robustness and effectiveness in challenging scenarios.

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Section: Fuzzy Mappingmentioning

confidence: 99%

Three-dimensional contour detection method based on fusion of machine vision and laser radar

Jun,

Shuo,

Shaobo

et al. 2024

Meas. Sci. Technol.

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“…[61], evidence theory [62], fuzzy integrals, which include ICA and the support vector machine (SVM) [63], as well as various other specific methods [64][65][66], are some important examples of algorithms related to this level [57]. The DS evidence theory [67,68], with its advantages over other fusion methods [69] in capturing the uncertainty of evidence and without using prior conditional probability densities, has been widely used in multisource and multi-temporal data [70][71][72][73][74].…”

Section: Experimental Area and Datamentioning

confidence: 99%

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Qin,

Song,

Zhu

et al. 2023

Remote Sensing

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Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.

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“…In [20], the authors offer the method for target identification, using algorithm of Dempster belief theory function to fuse the uncertainties of IR and visible light images. The developed algorithm consists of the three stages:…”

Section: B) Acquisition Of the Uncertainty Estimatementioning

confidence: 99%

“…When both the sensor credibility weights and classification belief values are obtained, the resulting decision Res n about target identified in the image is made using (6). ( 6 ) In contradiction to the previously mentioned systems [14], [20] and [21], the proposed approach uses additional sensors to obtain the weight factors for the used image detection sensors.…”

Section: B the Fusion Of Sensor Beliefsmentioning

confidence: 99%

Target Identification Using Sensors of Different Nature

Vagale

Ņikitenko

Slava

et al. 2017

Applied Computer Systems

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In the present paper, a new and improved visual sensor data fusion method is proposed that uses visible and far-infrared light sensors. Additionally, lux meter data are used for decision level fusion of beliefs of recognised target classes. The database consisting of 4 ambient light condition images is created using Canon and FLIR cameras. The developed approach has been tested using database images, neural network training and classification, particularly for low light level conditions. Enhancements of target identification precision are proved by practical implementation and testing of the proposed method.

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Multi-sensor Image Decision Level Fusion Detection Algorithm Based on D-S Evidence Theory

Cited by 10 publications

References 3 publications

Three-dimensional contour detection method based on fusion of machine vision and laser radar

Three-dimensional contour detection method based on fusion of machine vision and laser radar

A Novel Shipyard Production State Monitoring Method Based on Satellite Remote Sensing Images

Target Identification Using Sensors of Different Nature

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