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Emergence Model of Perception with Global-contour Precedence Based on Gestalt Theory and Primary Visual Cortex

IEEE Transactions on Image Processing 2025年 34卷 2721-2736页

作者： Li, Jingmeng Wei, Hui Fudan University Laboratory of Algorithms for Cognitive Models School of Computer Science Shanghai200438 China Fudan University Laboratory of Algorithms for Cognitive Models Innovation Center of Calligraphy and Painting Creation Technology MCT School of Computer Science Shanghai200438 China

Perceptual edge grouping is a technique for organizing the cluttered edge pixels into meaningful structures and further serves high-level vision tasks, which has long been a basic and critical task in computer vision. Existing methods usually have a poor performance when coping with the junctions caused by occlusion and noise in natural images. In this paper, we present GPGrouper, a perceptual edge grouping model based on gestalt theory and the primary visual cortex (V1). Different from the existing methods, GPGrouper leverages the edge representation and grouping matrix (ERGM), a functional structure inspired by V1 mechanisms, to represent edges in a way that can effectively reduce grouping errors caused by occlusion between objects. ERGM is trained with natural image contours and further provides a priori guidance for the construction of the edge connection graph (ECG) that is useful to minimize the impact of noise on grouping. In the experiment, we compared GPGrouper and the state-of-the-art (SOTA) method of perceptual grouping on the visual psychology pathfinder challenge. The results demonstrate that GPGrouper outperforms the SOTA method in grouping performance. Furthermore, in the grouping experiments involving line segments with varying lengths detected by the Line Segment Detector (LSD), as well as those involving superpixel segmentation results with significant levels of interfering noise using the SLIC algorithm, GPGrouper was superior to the existing methods in terms of grouping effect and robustness. Moreover, the results of applying the grouping results to the vision tasks objectness demonstrate that GPGrouper can contribute significantly to high-level visual tasks. © 1992-2012 IEEE.

关键词： Image segmentation

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Leader-follower:Human-centered intention-guided controller for novel SuperLimb with application to load-carrying scenarios

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Science China(Technological Sciences) 2025年第1期68卷 321-334页

作者： Qinghua ZHANG Xianglong LI Hongwei JING Kerui SUN Yuancheng LI Sikai ZHAO Lele LI Haotian JU Ziqi WANG Rui WU Tianjiao ZHENG Jie ZHAO Yanhe ZHU The State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China The Learning Algorithms and Systems Laboratory School of EngineeringÉcole Polytechnique Fédérale de LausanneLausanne CH 101510587Switzerland

Supernumerary robotic arms(SuperLimb)are a new type of wearable robot that works closely with humans as a third hand to augment human operation *** conveyance of wearers'intentions,allocation of roles,and humancentered interaction considerations are the key points in the process of human-SuperLimb *** paper proposes a human-centered intention-guided leader-follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying *** proposed leader-follower controller takes the human as the leader and the SuperLimb as the follower,achieving effective information communication,autonomous coordination,and good force compliance between SuperLimb,humans,and the environment under human safety ***,the human-SuperLimb dynamic system is modeled to achieve force interaction with the environment and ***,to achieve the precise intention extraction of humans,pose data from five visual odometry sensors are fused to capture the human state,the generalized position,the velocity of hands,and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer ***,based on the real-time distance detection between human and mechanical limbs,the security assurance and force-compliant interaction of the human-SuperLimb system are ***,the human hand muscle intention recognition,human-robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb *** showed that the task parameters are well estimated to produce more reasonable planning trajectories,and SuperLimb could well understand the wearer's intentions to switch different SuperLimb *** proposed sensor-based human-robot communication framework motivates future studies of other collaboration scenes fo

关键词： SuperLimb leader-follower human intention estimation human-robot safety human-robot communication

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Enhanced genetic algorithm for indoor low-illumination stereo matching energy function optimization

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Alexandria Engineering Journal 2025年 121卷 1-17页

作者： Hongjin, Zhang Hui, Wei Laboratory of Algorithms for Cognitive Models School of Computer Science Fudan University Handan Road 220 ShangHai China College of Engineering Science and Technology Shanghai Ocean University Huchenghuan Road 999 ShangHai China

This study presents an enhanced genetic algorithm to generate high-quality, dense disparity maps in low-illumination indoor stereo matching scenarios. The algorithm utilizes a novel biomimetic mutation strategy, dynamically adjusted through common allelic theory, to effectively reduce the search space and improve optimization efficiency. A multi-objective genetic approach is employed, optimizing both data fidelity and smoothness to achieve balanced and accurate results. The Integration of biological evolution principles, including a hierarchical performance index system, ensures the algorithm balances local and global features during optimization. Experimental results demonstrate that the proposed algorithm consistently converges within 10 iterations, reducing average disparity errors by 33.7% compared to state-of-the-art methods and by 20% compared to a control group. Additionally, the algorithm achieves a lower computational complexity of O(H), offering significant improvements in resource efficiency compared to standard algorithms. These findings validate the algorithm's reliability, advancement, and effectiveness for stereo matching under challenging conditions. © 2025 The Authors

关键词： Optimization algorithms

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Temporal Modeling via TCN and Transformer for Audio-Visual Emotion Recognition

Temporal Modeling via TCN and Transformer for Audio-Visua...

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12th International Conference on Analysis of Images, Social Networks and Texts, AIST 2024

作者： Andreev, Aleksei A. Savchenko, Andrey V. Laboratory of Algorithms and Technologies for Network Analysis HSE University Nizhny Novgorod Russia Sber AI Lab Moscow Russia

ISBN: (纸本)9783031880353

Emotion recognition from multimodal sources is essential for advancing human-computer interaction. This paper introduces a comprehensive approach integrating robust methodologies to enhance multimodal emotion recognition. By harnessing both facial video and audio signals through Temporal Convolutional Networks (TCN) and Transformer models, alongside other advanced neural architectures, our ensemble effectively captures the complex dynamics of emotional expressions. Our approach has been rigorously evaluated on the Expression Classification challenge from the recent Affective Behavior Analysis in-the-Wild competition, demonstrating significant improvements compared to state-of-the-art methods. Specifically, our model achieved a 12% increase in F1 Score over the baseline, illustrating a substantial enhancement in performance and reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Multi-Scale Feature Convolutional Neural Network for Particle Image Velocimetry

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Gu, Shusheng Wang, Jingyu Li, Guanxiong Deng, Xiaogang Sichuan University College of Computer Science The National key laboratory of Fundamental Algorithms and Models for Engineering Simulation 610065 China Sichuan University School of Aeronautics and Astronautics The National key laboratory of Fundamental Algorithms and Models for Engineering Simulation 610065 China Sichuan University National key laboratory of Fundamental Algorithms and Models for Engineering Simulation 610065 China

Particle Image Velocimetry (PIV) is a crucial technique in experimental fluid dynamics for non-invasively measuring the velocity components of flow fields. Deep learning methods applied to PIV for velocity field estimation are primarily derived from computer vision. However, these methods often fail to account for the distinctive characteristics of fluid dynamics, such as the very small scales of objects and displacements relative to pixel resolution, high-density particle distributions, complex and irregular motion patterns. As demonstrated in this work, these approaches encounter three main challenges: (i) failure to accurately estimate the motion for very small particles and displacements;(ii) significant computational discrepancies with high-density particle distributions;and (iii) considerable inaccuracies in the analysis of complex flow fields. To address these issues, we propose a novel convolutional neural network, named Multi-Scale Feature Recurrent All-Pairs Field Transforms (MSF-RAFT). Our approach involves: (1) developing a highresolution multi-scale feature extraction network;(2) integrating an innovative residual block to improve the network;and (3) creating a multi-scale feature correction module that constructs a four-layer correlation pyramid. The proposed network has been validated on various datasets and experimental images. MSF-RAFT exhibits superior performance, particularly with high-density small particle images, achieving a significant reduction in estimation error and accurately capturing small vortex details. Comparative analyses with benchmark models demonstrate that MSF-RAFT achieves up to a 33% reduction in estimation error compared to previous best models on complex datasets and provides a flow field estimation that more accurately represents the fluid dynamics around the nacelle. Additionally, evaluations of model size, runtime, and memory consumption demonstrate that MSF-RAFT is highly efficient, making it well-suited to meet the indu

关键词： Flow fields

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Enhancing Wireless PHY With Adaptive OFDM and Multiarmed Bandit Learning on Zynq System-on-Chip

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2025年

作者： Singh, Neelam Darak, Sumit J. IIIT Delhi Algorithms to Architecture Laboratory Department of Electronics and Communications New Delhi110020 India

In this work, we present an intelligent and reconfigurable wireless physical layer (PHY) that dynamically adjusts the transmission parameters for a given radio frequency (RF) environment. The proposed PHY is based on orthogonal frequency division multiplexing (OFDM) and can dynamically augment OFDM with a finite impulse response (FIR) low-pass filter to improve the out-of-band emissions (OOBE). To make these adaptations intelligently, we employ multiarmed bandit (MAB)-based online learning algorithms, specifically upper confidence bound with control variate (UCB-CV). UCB-CV enhances traditional UCB by incorporating additional information such as interference level and transmit power, allowing it to manage interference more effectively. These algorithms are integrated into the PHY of an FPGA-based OFDM transceiver on the Zynq system-on-chip (SoC), facilitating real-time decision-making based on side-channel interference and other parameters. Our comparative analysis highlights the enhanced performance of the UCB-CV algorithm over the traditional UCB in terms of reducing the bit-error rate (BER) and managing interference more effectively. Unlike the traditional UCB, UCB-CV leverages side information through a control variate approach, incorporating the coefficient of variation (CV) into reward estimation to better handle interference. Additionally, we underline the advantages of filtered-OFDM (FOFDM) compared to standard OFDM. Notably, FOFDM significantly reduces OOBE by 20–75 dBW/Hz and improves BER. In environments with high interference, UCB-CV achieves a throughput improvement of 29.54% compared to UCB. © 1993-2012 IEEE.

关键词： System-on-chip

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On the Parallel Performance of a Novel Brick-Based Hash-Table CFD Library for Distributed Computing

On the Parallel Performance of a Novel Brick-Based Hash-Tabl...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Davis, Andrew Guzik, Stephen Gao, Xinfeng Computational Fluid Dynamics and Propulsion Laboratory University of Virginia CharlottesvilleVA22904 United States Applied Numerical Algorithms Group Lawrence Berkely National Lab BerkelyCA94720 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The performance of a brick-based and hash-table-based framework, HashBrick, supporting distributed structured-grid calculations on heterogeneous architectures, is evaluated with a fourth-order accurate finite-volume algorithm in space and time. HashBrick is implemented with brick data structures and hash tables to provide performance portability across CPUs and GPUsondistributed parallel architectures. It is optimized for CFD algorithms characterized by local clusters of structured-grid data that are otherwise sparsely distributed in the domain. Asimple acoustic Gaussian pulse problem is solved to primarily stress memory bandwidth and latency. A weak-scaling study was performed to demonstrate HashBrick’s performance on multiple computing nodes. The performance of HashBrick is assessed by comparing wall-clock time and energy against a CPU-only CFD solver, for the same power budget. Two-sided MPI communication with a bulk synchronization is compared against one-sided SHMEM and NVSHMEMcommunication. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Brick

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The Impact of Alternative Data on Default Probability: Analyzing the Italian E-commerce Sector with NLP and Network Structures

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Operations Research Forum 2025年第2期6卷 1-30页

作者： Bernhardt, Brian Daniel Marciano, Chiara Guarracino, Mario Rosario Department of Economics and Law University of Cassino and Southern Lazio Cassino Italy Indatis s.r.l. Cassino Italy Laboratory of Algorithms and Technologies for Networks Analysis Higher School of Economics Nizhny Novgorod Russian Federation

E-commerce is a key sector in the Italian economy, with online companies becoming some of the largest and most profitable businesses. However, this growth comes with increased risk exposure. This study aims to investigate the relationship between alternative data (contextual factors, Text-Driven Data Enrichment) and the probability of default for Italian e-commerce companies. To date, no studies have examined how these alternative data affect the default probability within this sector. To address this gap, the ongoing research analyzes a dataset of Italian companies, focusing on sector-specific indicators. In the Italian e-commerce market, companies are identified by a unique code that indicates the sales channel but not the types of products marketed. To overcome this limitation, a natural language processing (NLP) model is applied to the es of these companies, allowing us to identify the types of goods or services sold by each company. These enriched data provide a more comprehensive understanding and serve as the foundation for a classification model that uses standard interpretable algorithms for default prediction. Additionally, network structures identified by companies’ similarities are used to extract new insights, which can support decision-making processes for stakeholders at different levels of the supply chain. The model’s performance is evaluated in various scenarios, comparing results with and without the inclusion of alternative data. Key performance metrics are analyzed to demonstrate how integrating alternative data enhances default prediction models. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Alternative data E-commerce sector Probability of default Supervised learning Text embedding

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Using the Pilot Method as a Problem-Independent Metaheuristic for Multi-objective Beam Search

Using the Pilot Method as a Problem-Independent Metaheuri...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Bindreiter, Oliver Werth, Bernhard Wagner, Stefan Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031838873

This paper proposes a combination of multi-objective beam search and the pilot method. The idea is to use the pilot method as a problem-independent metaheuristic to guide the search process of multi-objective beam search. Through this combination, the newly created search algorithm is less dependent on problem specific heuristics, which would otherwise be required to guide the search process. Especially when dealing with problems where no well-known heuristic functions exist this approach can be feasible. It must be noted, that in this situation, the design of the required heuristic functions is a challenging task because a deep understanding of the underlying problem as well as the chosen search algorithm is required. The proposed methodology does not use multiple heuristic functions to evaluate nodes. Instead, a sub search process in the form of the pilot method is started to obtain an estimation for the node’s quality. This sub search process is a single target search process and can only provide an estimation for one of the problems target functions. Therefore, the target functions are now also used as heuristic functions. Applying this new search algorithm to well-known benchmark problems yielded promising results and generates high quality solutions compared with current state-of-the-art algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Multiobjective optimization

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Age-Layer-Population-Structure with Self-adaptation in Optimization

Age-Layer-Population-Structure with Self-adaptation in Opt...

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19th International Conference on Computer Aided Systems Theory, EUROCAST 2024

作者： Yang, Kaifeng Werth, Bernhard Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg4232 Austria

ISBN: (纸本)9783031838873

Dynamic optimization problems are a class of optimization problems where the objective function, constraints, or both can change over time. In this work, we address the dynamic travelling salesman problem (TSP) by using the age-layered population structure (ALPS). To enhance different layer’s behaviour, we introduce the self-adaptation strategies to adjust the mutation and crossover rate in each layers, which are stationary in the conventional ALPS. The proposed strategies are compared with the stationary strategy over 7 different dynamic TSPs. The experimental results shows that the proposed strategy, convex strategy, yields much better results than the stationary strategy on complex problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Evolutionary algorithms

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