For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and line...
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In order to overcome the limitations of using a single sensor to locate and map the sterile mobile robot in complex environment, this paper combines the advantages of lidar and depth camera, presents a method to fuse ...
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ISBN:
(纸本)9781665464697
In order to overcome the limitations of using a single sensor to locate and map the sterile mobile robot in complex environment, this paper combines the advantages of lidar and depth camera, presents a method to fuse the depth information of the depth camera with the lidar data to get point cloud data, uses gampping algorithm to construct two-dimensional indoor map, establishes the robot and simulation environment in gazebo, and tests the algorithm. Map information is more comprehensive than building maps directly using lidar.
Upright position CT scans make it possible for full-length-body imaging at conditions more relevant to daily situations, but the substantial weight of the upright CT scanners increases the risks to floor’s stability ...
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The Local Binary Pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-u...
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The Local Binary Pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-uniform patterns with only one bin in the feature histogram, thus losing their discrimination capability. (2) When center pixel is contaminated by noise, a uniform pattern may be transformed into a non-uniform pattern, which can seriously affect the obtained LBP, thus degrading the classification results. To overcome these drawbacks, an edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy (SFB-OCPS) is proposed in this paper. To extract the correct edge pixels, we divide the whole texture image into 16 = 4 x 4 sub-images and propose an edge pixel selection strategy (EPSS) based on adaptive quantization with local threshold on each sub-image. Then 3 candidate center pixels constructed by statistical features of the local sampling neighborhood are generated for each edge-located center pixel. After the steps above, the SFB-OCPS strategy is introduced into the LBP-based algorithms. It is possible to recover some important edge-located non-uniform patterns to uniform patterns with an optimal center pixel selection, thus improving feature discrimination capability of the LBP-based algorithms. It should be emphasized that any LBP variants can introduce the proposed SFB-OCPS strategy to achieve the recovery of the edge-located uniform patterns. To validate the effectiveness of the proposed SFB-OCPS strategy, we introduce the SFB-OCPS strategy into the original LBP and 5 representative LBP-based algorithms. Experiments are conducted on 6 representative texture databases. Classification comparison reveals that the introduction of SFB-OCPS strategy can significantly improve the texture classification performance of LBP-based algorithms. Additionally, the noise-robustness of the proposed SFB-OCPS st
In the process of imageprocessing, the higher the resolution of the image, the richer the data information contained in the image. Aiming at the problem of low image resolution, through the research of generating cou...
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Vehicle license plate recognition is a crucial task in intelligent traffic management systems. However, the challenge of achieving accurate recognition persists due to motion blur from fast-moving vehicles. Despite th...
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With the development of technology, the technological informationization of the security network video surveillance service industry has become the demand of the times. How to improve the functions of the video survei...
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With the development of technology, the technological informationization of the security network video surveillance service industry has become the demand of the times. How to improve the functions of the video surveillance system and build an open security integrated monitoring management platform has become the research point of this article. This article intends to build a video surveillance system based on database technology to meet the multi-functional requirements of the surveillance system. This article mainly uses experimental methods to test the data of the monitoring system designed in this article, and then uses the comparative method to compare the speed of the three methods to calculate the data, and the data results are obtained. According to the experiment, the data processing time of the binary algorithm in the video surveillance system is within 15s. image detection in database technology uses binary algorithms to operate and analyze it more quickly.
Long-range active detection is widely demanded in various fields. Currently, it is still difficult to obtain high-resolution images in long-range while ensuring miniaturization of the detection system, because the res...
Long-range active detection is widely demanded in various fields. Currently, it is still difficult to obtain high-resolution images in long-range while ensuring miniaturization of the detection system, because the resolution of photodetector-based detection systems is limited by the aperture of the optic system and the sampling frequency of the photodetector. Furthermore, the existing super-resolution algorithms are limited by the Nyquist sampling theorem. Alternatively, super-resolution computational ghost imaging is utilized to overcome the above problems and to meet the requirements of imaging resolution in the long-range active detection system. This paper aims to verify the feasibility of super-resolution computational ghost imaging applied to long-range active detection. First, we establish the light transmission model based on scalar diffraction theory. Then, we investigate the resolution of the imaging system and finally verify the proposed computational ghost imaging method by simulation and equivalent experiments. It is concluded from the above study that super-resolution computational ghost imaging can be applied to long-range active detection. It provides a feasible technical means for the miniaturization of long-range active detection systems. Furthermore, in the imageprocessing of the equivalence experiment, it was found that the image reconstructed by the ghost imaging (GI) algorithm can suppress the background noise of the image reconstructed by the compressive sensing ghost imaging (CSGI) algorithm.
This study aims to address two challenging problems that affect the accurate and reliable recognition of ship infrared (IR) images in various scenarios: the interference of radiation highlights from specular reflectio...
This study aims to address two challenging problems that affect the accurate and reliable recognition of ship infrared (IR) images in various scenarios: the interference of radiation highlights from specular reflections of target background sunlight and the difficulty of distinguishing ship targets from background confusion under low light conditions. We propose a data optimisation method for ship IR deep learning recognition, which uses processing tools such as median noise reduction and local histogram equalisation image enhancement to improve the performance and stability of ship IR recognition under the above unfavourable conditions. And the effectiveness of the method is verified on high fidelity mid-wave infrared (MWIR) ship simulation data using deep learning image classification algorithms such as AlexNet, VggNet and EfficientNet. To further demonstrate the effectiveness of the method, research has also adopted model feature gradient visualization technique, which indirectly verifies that our method can effectively improve the predictive reliability of ship recognition models. Overall, the data optimization method proposed in this study provides a feasible solution to address the challenges in ship IR image recognition and can provide a valuable reference for military mission-related research.
Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice. For example, a large dialog LLM like ChatGPT has successfully passed p...
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