Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location ...Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.展开更多
The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natu...The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.展开更多
Social perception refers to how individuals interpret and understand the social world.It is a foundational area of theory and measurement within the social sciences,particularly in communication,political science,psyc...Social perception refers to how individuals interpret and understand the social world.It is a foundational area of theory and measurement within the social sciences,particularly in communication,political science,psychology,and sociology.Classical models include the Stereotype Content Model(SCM),Dual Perspective Model(DPM),and Semantic Differential(SD).Extensive research has been conducted on these models.However,their interrelationships are still difficult to define using conventional comparison methods,which often lack efficiency,validity,and scalability.To tackle this challenge,we employ a text-based computational approach to quantitatively represent each theoretical dimension of the models.Specifically,we map key content dimensions into a shared semantic space using word embeddings and automate the selection of over 500 contrasting word pairs based on semantic differential theory.The results suggest that social perception can be organized around two fundamental components:subjective evaluation(e.g.,how good or likable someone is)and objective attributes(e.g.,power or competence).Furthermore,we validate this computational approach with the widely used Rosenberg’s 64 personality traits,demonstrating improvements in predictive performance over previous methods,with increases of 19%,13%,and 4%for the SD,DPM,and SCM dimensions,respectively.By enabling scalable and interpretable comparisons across these models,our findings would facilitate both theoretical integration and practical applications.展开更多
With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration ...With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration unlocks the value of data and computational power,presenting significant opportunities for large-scale 3D scene modeling and XR presentation.In this paper,we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture.We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.展开更多
文摘Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.
文摘The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.
文摘Social perception refers to how individuals interpret and understand the social world.It is a foundational area of theory and measurement within the social sciences,particularly in communication,political science,psychology,and sociology.Classical models include the Stereotype Content Model(SCM),Dual Perspective Model(DPM),and Semantic Differential(SD).Extensive research has been conducted on these models.However,their interrelationships are still difficult to define using conventional comparison methods,which often lack efficiency,validity,and scalability.To tackle this challenge,we employ a text-based computational approach to quantitatively represent each theoretical dimension of the models.Specifically,we map key content dimensions into a shared semantic space using word embeddings and automate the selection of over 500 contrasting word pairs based on semantic differential theory.The results suggest that social perception can be organized around two fundamental components:subjective evaluation(e.g.,how good or likable someone is)and objective attributes(e.g.,power or competence).Furthermore,we validate this computational approach with the widely used Rosenberg’s 64 personality traits,demonstrating improvements in predictive performance over previous methods,with increases of 19%,13%,and 4%for the SD,DPM,and SCM dimensions,respectively.By enabling scalable and interpretable comparisons across these models,our findings would facilitate both theoretical integration and practical applications.
基金the National Natural Science Foundation of China(U22B2034)the Fundamental Research Funds for the Central Universities(226-2022-00064).
文摘With the support of edge computing,the synergy and collaboration among central cloud,edge cloud,and terminal devices form an integrated computing ecosystem known as the cloud-edge-client architecture.This integration unlocks the value of data and computational power,presenting significant opportunities for large-scale 3D scene modeling and XR presentation.In this paper,we explore the perspectives and highlight new challenges in 3D scene modeling and XR presentation based on point cloud within the cloud-edge-client integrated architecture.We also propose a novel cloud-edge-client integrated technology framework and a demonstration of municipal governance application to address these challenges.
