Over the years, scene understanding has attracted a growing interest in computer vision, providing the semantic and physical scene information necessary for robots to complete some particular tasks autonomously. In 3D...
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Existing view planning systems either adopt an iterative paradigm using next-best views (NBV) or a one-shot pipeline relying on the set-covering view-planning (SCVP) network. However, neither of these methods can conc...
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At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and au...
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At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and autonomous behavioral patterns. They rely on algorithms with a global view, significantly differing from biological neural networks, in which, to simulate information storage and retrieval processes, the limitations of centralized algorithms must be overcome. This study introduces a directed graph model that equips each node with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information storage and modeling and simulation of the brain’s memory process. We abstract different storage instances as directed graph paths, transforming the storage of information into the assignment, discrimination, and extraction of different paths. To address writing and reading challenges, each node has a personalized adaptive learning ability. A storage algorithm without a "God’s eye" view is developed, where each node uses its limited neighborhood information to facilitate the extension, formation, solidification, and awakening of directed graph paths, achieving competitive, reciprocal, and sustainable utilization of limited resources. Storage behavior occurs in each node, with adaptive learning behaviors of nodes concretized in a microcircuit centered around a variable resistor, simulating the electrophysiological behavior of neurons. Based on Ohm’s and Kirchhoff’s laws, we simulated the dynamics of this directed graph network on a computer, where the network could store and retrieve uploaded instances, confirming the model’s effectiveness and exploring its storage capacity. Under the constraints of neurobiology on the anatomy and electrophysiology of biological neural networks, this model offers a plausible explanation for the mechanism of memory realization, providing a comprehensive, system-level experimental validation of
At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and au...
详细信息
At the intersection of computation and cognitive science, graph theory is utilized as a formalized description of complex relationships and structures. Traditional graph models are often static, lacking dynamic and autonomous behavioral patterns. They rely on algorithms with a global view, significantly differing from biological neural networks, in which, to simulate information storage and retrieval processes, the limitations of centralized algorithms must be overcome. This study introduces a directed graph model that equips each node with adaptive learning and decision-making capabilities, thereby facilitating decentralized dynamic information storage and modeling and simulation of the brain’s memory process. We abstract different storage instances as directed graph paths, transforming the storage of information into the assignment, discrimination, and extraction of different paths. To address writing and reading challenges, each node has a personalized adaptive learning ability. A storage algorithm without a "God’s eye" view is developed, where each node uses its limited neighborhood information to facilitate the extension, formation, solidification, and awakening of directed graph paths, achieving competitive, reciprocal, and sustainable utilization of limited resources. Storage behavior occurs in each node, with adaptive learning behaviors of nodes concretized in a microcircuit centered around a variable resistor, simulating the electrophysiological behavior of neurons. Based on Ohm’s and Kirchhoff’s laws, we simulated the dynamics of this directed graph network on a computer, where the network could store and retrieve uploaded instances, confirming the model’s effectiveness and exploring its storage capacity. Under the constraints of neurobiology on the anatomy and electrophysiology of biological neural networks, this model offers a plausible explanation for the mechanism of memory realization, providing a comprehensive, system-level experimental validation of
Autonomous robots for medical and emergency supplies are a potential way to avoid contact with people in quarantine and control the spread of contagious diseases in an indoor scene. However, scene understanding and re...
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Autonomous robots for medical and emergency supplies are a potential way to avoid contact with people in quarantine and control the spread of contagious diseases in an indoor scene. However, scene understanding and reconstruction through a single low-cost camera remains a challenge. It is known that absolute precise depth cannot be calculated accurately from a single image, but the relative pose of different planes, which can be inferred from geometric features in a 2-D image, are more likely to be used in understanding scenes and its reconstruction. In this article, we present an interpretable model to bridge the gap between 2-D scene understanding and three-dimensional (3-D) reconstruction without prior training or any precise depth data. Based on 2-D semantic information in our previous works, the 3-D relative pose of estimated planes can be estimated. At that point, indoor scenes are approximated in the reconstruction. The approach behaves as an interpretable characteristic and requires no prior training or knowledge of the camera's internal parameters. We compare the quantitative performance on the percentage of incorrectly reconstructed planes by relative pose estimation. The results demonstrated that the method can successfully understand and reconstruct indoor scenes including both Manhattan and curved non-Manhattan structures.
This study addresses the challenge of obtaining reliable dense depth results from outdoor stereo images captured in adverse weather conditions, whose impact is often overlooked by traditional methods, while deep learn...
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Deep-learning algorithms have an excellent nonlinear fitting ability. However, tiny objects in images do not approach the general properties of the fit, which causes the algorithms to have problems acquiring disparity...
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ISBN:
(数字)9798350356670
ISBN:
(纸本)9798350356687
Deep-learning algorithms have an excellent nonlinear fitting ability. However, tiny objects in images do not approach the general properties of the fit, which causes the algorithms to have problems acquiring disparity for tiny objects. In addition, the results suffer from problems such as disparity inflation and warping variation. To address the shortcomings of deep learning algorithms, we propose an improved deep learning algorithm based on traditional algorithms. First, the algorithm divides the image into segments based on edges. Second, the edge error disparity and disparity expansion is corrected. Third, it finds the wrong disparity in a small segment, and removes it. The adjacent pixels with disparity removed are grouped into clusters. The matching relationship is established by combining the cluster’s gray, gradient, and feature-point information into matching elements. Finally, removal of disparity warping variation and post-processing is performed. The algorithm can effectively correct error disparity, detect the disparity of tiny objects, and can be applied to practical scenarios where tiny objects are detected.
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