Traditional supervised learning methods achieve remarkable performance in high-resolution remote sensing image retrieval, but are limited by the dependence on large-scale annotated images. Contrastive learning can lev...
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The development of algorithms to solve Many-objective optimization problems(MaOPs) has attracted significant research interest in recent *** various types of Pareto front(PF) is a daunting challenge for evolutionary a...
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The development of algorithms to solve Many-objective optimization problems(MaOPs) has attracted significant research interest in recent *** various types of Pareto front(PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm(RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector *** concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.
Recent studies on simultaneous localization and mapping (SLAM) have tended to employ implicit neural representation, which can improve the efficiency and robustness of SLAM system. However, these methodologies still f...
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Recent studies on simultaneous localization and mapping (SLAM) have tended to employ implicit neural representation, which can improve the efficiency and robustness of SLAM system. However, these methodologies still face challenges, such as tracking failures and low-precision mapping. In this paper, we propose a dense reconstruction visual SLAM system enhanced with closed-loop threading and local map optimization, named TNIE-SLAM. First, we propose a tracking module that utilizes the similarity of ORB feature descriptors and the feature overlap rate of the current frame to model key frames, and then we define a complete and accurate initial map based on full bundle adjustment, which addresses the issue of tracking failure due to undermapped areas. Second, we add the 2D features of the initial map to the spatiotemporal encoding module to obtain the 3D features, enabling real-time prediction and tracking of unknown areas. Finally, considering the low-precision mapping issue arising from the complex geometric shapes of objects within the scene, we propose a local map optimization module that utilizes truncated signed distance fields to model 3D features and update the spatial occupancy of boundary and contour features of objects. We test our method on the synthetic Replica dataset and the real-world ScanNet and TUM RGB-D datasets to compare with some state-of-the-art RGB-D SLAM methods, and the experimental results indicate our method performs well in both tracking and mapping accuracy, surpassing the existing dense neural RGB-D SLAM methods.
The presence of large-scale heterogeneous IoT devices and AI-based applications has brought significant transmission and computation pressure on the wireless communication system. In this paper, we propose a novel mul...
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Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements....
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Recent studies have pointed out that the boundary of the extracted ventricle membranes is unsmooth, and the segmentation of the cardiac papillary muscle and trabecular muscle do inconformity the clinical requirements. To address these issues, this paper proposes an automatic segment algorithm for continuously extracting ventricle membranes boundary, which adopts optical flow field information and sequential images information. The images are cropped by frame difference method, which according to the continuity of adjacent slices of cardiac MRI images. The roughly boundary of epicardium is extracted by the Double level set region evolution(DLSRE) model, which combines image global information, local information and edge information. The ventricle endocardium and epicardial contours are tracked according to the optical flow field information between image sequences. The segmentation results are optimized by Delaunay triangulation algorithm. The experimental results demonstrate that the proposed method can improve the accuracy of segmenting the ventricle endocardium and epicardium contours, and segment the contour of the smooth ventricle membrane edge that meets the clinical definition.
Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as t...
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Real-time object detection has been a critical component of onboard instrumentation for next-generation autonomous driving. To enable safety and reliability in autonomous driving systems, we must continue advancing it...
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Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and dete...
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Recently, deep convolutional learning has been applied to image deblurring, which greatly improves the performance of single-image blind deblurring algorithms. However, most deep image deblurring models based on convo...
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The aim of quantum secret sharing,as one of most promising components of quantum cryptograph,is one-tomultiparty secret communication based on the principles of quantum *** this paper,an efficient multiparty quantum s...
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The aim of quantum secret sharing,as one of most promising components of quantum cryptograph,is one-tomultiparty secret communication based on the principles of quantum *** this paper,an efficient multiparty quantum secret sharing protocol in a high-dimensional quantum system using a single qudit is *** participant's shadow is encoded on a single qudit via a measuring basis encryption method,which avoids the waste of qudits caused by basis *** analysis indicates that the proposed protocol is immune to general attacks,such as the measure-resend attack,entangle-and-measure attack and Trojan horse *** to former protocols,the proposed protocol only needs to perform the single-qudit measurement operation,and can share the predetermined dits instead of random bits or dits.
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