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Visual attention modeling for 3D video using neural networks

Visual attention modeling for 3D video using neural networks

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International conference on 3D Imaging (IC3D)

作者： Iana Iatsun Mohamed-Chaker Larabi Christine Fernandez-Maloigne Universite de Poitiers Poitiers Poitou-Charentes FR XLIM-SIC lab University of Poitiers France

Visual attention is one of the most important mechanisms in the human visual perception. Recently, its modeling becomes a principal requirement for the optimization of the image processing systems. Numerous algorithms have already been designed for 2D saliency prediction. However, only few works can be found for 3D content. In this study, we propose a saliency model for stereoscopic 3D video. This algorithm extracts information from three dimensions of content, i.e. spatial, temporal and depth. This model benefits from the properties of interest points to be close to human fixations in order to build spatial salient features. Besides, as the perception of depth relies strongly on monocular cues, our model extracts the depth salient features using the pictorial depth sources. Since weights for fusion strategy are often selected in ad-hoc manner, in this work, we suggest to use a machine learning approach. The used artificial Neural Network allows to define adaptive weights based on the eye-tracking data. The results of the proposed algorithm are tested versus ground-truth information using the state-of-the-art techniques.

关键词： Three-dimensional displays Feature extraction visualization Computational modeling Mathematical model Solid modeling Equations

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Prefiltering and reconstruction filters in the volume rendering

Prefiltering and reconstruction filters in the volume render...

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Mediterranean Electrotechnical conference (MELECON)

作者： A. Goluban I. Mihajlovic L. Budin Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

This paper deals with reconstruction filters and appropriate prefiltering techniques in the volume rendering. Sampling, resampling and reconstruction of continuous functions are used in many different fields. To satisfy conditions of Shannon's theorem, prefiltering of images or data volumes is usually required before sampling. The usual approach is to use a Gaussian prefilter, where the cutoff frequency is determined by the sampling rate, and one of the different reconstruction schemes is then applied. The problem is to determine a prefilter so that the reconstruction will reproduce the original object as accurately as possible. If the combination of prefiltering and reconstruction in the volumetric space is not adequate, aliasing artifacts will appear. Aliasing artifacts project from the three-dimensional space onto the projection plane and superpose to the artifacts caused by sampling and reconstruction in two dimensions. We present a methodology for designing filters based on optimization of combined operation of prefiltering and reconstruction postfiltering. This methodology offers a framework for comparing different reconstruction schemes in equal circumstances. The correct choice of reconstruction will also influence the derivative reconstruction which is used for shading. We demonstrate our results on examples in two- and three-dimensional space.

关键词： Filters Image reconstruction Sampling methods Rendering (computer graphics) Image sampling Interpolation Spline Design methodology Data visualization Lighting

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Robust Visual Object Tracking in Clustered Environment

Robust Visual Object Tracking in Clustered Environment

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International conference on Contemporary Computing and Informatics (IC3I)

作者： L.Krishna Kumari K. Ramalakshmi V. SrinivasaRaghavan Ramco Institute of Technology Rajapalayam India PSR Engineering College Sivakasi India Theni Kammavar Sangam College of Technology Koduvilarpatti Theni

To perform visual object tracking in clustered environment, kernel methods based Support Vector Machines (SVMs) can be used. A Scale adaptive Kernel support correlation filter Algorithm (SKSCF) is employed here for visual object tracking. In this method, the SVM models with circulant matrix formulation have been used. For visual tracking and its related applications it enhances the process of optimization. SVM models with circulant matrix formulation use the discrete Fourier transform operation to accomplish visual object tracking. The issue of visual objects is illustrated as a recursive command of Support Correlation Filters (SCFs). A Scale Adaptive Kernel Support Correlation Filter (SKSCF) has an O(n2 log n) computational complexity. The SVM-based techniques’ computational complexity is at O (n4). Along with the Scale Adaptive Kernel Support Correlation Filter Algorithm, factors like multi-channel features, scale adaptive approach, and kernel methods are applied. For a sizable standard dataset, the Scale Adaptive Kernel Support Correlation Filter Algorithm provides finer outcomes in terms of reliability and speed.

关键词： Support vector machines visualization Adaptation models Correlation Clustering algorithms Filtering algorithms Object tracking

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Object Classification with Adaptable Regions

Object Classification with Adaptable Regions

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IEEE conference on Computer Vision and Pattern Recognition

作者： Hakan Bilen Marco Pedersoli Vinay P. Namboodiri Tinne Tuytelaars Luc Van Gool ESAT-PSI-VISICS/iMinds KU Leuven Dept. of Comp. Sci. & Eng. IIT Kanpur

ISBN: (纸本)9781479951192

In classification of objects substantial work has gone into improving the low level representation of an image by considering various aspects such as different features, a number of feature pooling and coding techniques and considering different kernels. Unlike these works, in this paper, we propose to enhance the semantic representation of an image. We aim to learn the most important visual components of an image and how they interact in order to classify the objects correctly. To achieve our objective, we propose a new latent SVM model for category level object classification. Starting from image-level annotations, we jointly learn the object class and its context in terms of spatial location (where) and appearance (what). Furthermore, to regularize the complexity of the model we learn the spatial and co-occurrence relations between adjacent regions, such that unlikely configurations are penalized. Experimental results demonstrate that the proposed method can consistently enhance results on the challenging Pascal VOC dataset in terms of classification and weakly supervised detection. We also show how semantic representation can be exploited for finding similar content.

关键词： Training Support vector machines Vectors visualization optimization Context Encoding

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Optimal AI Models for Process Data Analysis in Industry 4.0 Regarding Digital Twin Implementation

Optimal AI Models for Process Data Analysis in Industry 4.0 ...

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International conference on Information Technology (INCIT)

作者： Tanat Kanangnanon Chalermpan Fongsamut Prajaks Jitngernmadan Faculty of Informatics Burapha University Chon Buri Thailand Faculty of Engineering Burapha University Chon Buri Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

This study compares YOLOv8 and Mask R-CNN object detection algorithms for defect detection in digital twin applications for smart manufacturing. With the ongoing transformation of manufacturing processes in Industry 4.0 and 5.0, integrating digital twin technology and advanced visual inspection techniques has become crucial for improving quality control and process optimization. Using the expanded MVTec Metal Nut Dataset, we evaluated performance based on mean average precision (mAP), precision, recall, F1-score, and inference time. Results show YOLOv8 consistently outperformed Mask R-CNN in accuracy and computational efficiency, achieving higher scores across all metrics and quicker inference times. This suggests YOLOv8 is more suitable for real-time industrial applications, particularly in digital twin implementations. However, the study emphasizes the importance of considering specific manufacturing contexts when selecting an object detection model. The integration of AI-powered visual inspection with digital twin technology has significant potential to enhance product quality, reduce downtime, and optimize production processes, contributing valuable empirical evidence to guide the implementation of advanced defect detection systems in industry.

关键词： visualization Object detection Quality control Inspection Real-time systems Digital twins Fourth Industrial Revolution Quality assessment Defect detection Smart manufacturing

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Real Time Crop Harvest Time Prediction Model Using Raspberry Pi and Image Processing

Real Time Crop Harvest Time Prediction Model Using Raspberry...

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Advancement in Technology (ICONAT), International conference for

作者： Mrunalini S. Bhandarkar Basudha Dewan Payal Bansal Poornima University Jaipur India Poornima College of Engineering Jaipur India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Agriculture is a vital sector for ensuring global food security and promoting sustainable development in every country. Additionally, accurate prediction of crop yield and best harvest time is vital as it will help the farmers to maximize their profit with right utilization of the available resources. The proposed system enables prediction of crop harvest time using Raspberry Pi and image processing technique. The Raspberry Pi, which has a camera module installed, takes pictures of the crops over time. Image analysis techniques are applied to assess the visual indicators of crop maturity, such as color, size, and texture. This technology forecasts the best time to harvest by comparing these visual cues with past data. This approach eliminates the need for manual inspection and enables timely decision-making. The system incorporates a user-friendly interface accessible through a web or mobile application, presenting real-time and predicted harvest data. Notifications alert users to optimal harvest conditions. Continuous improvement is achieved through periodic updates and refinement of the image analysis algorithms, ensuring accurate predictions and adaptability to different crop types. This technique offers a practical and efficient solution for determining the ideal harvest time, facilitating resource optimization, and maximizing crop yields.

关键词： visualization Image analysis Image color analysis Decision making Crops Predictive models Real-time systems Sustainable development Crop yield Farming

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Human-centric Quality Management of Immersive Multimedia Applications

Human-centric Quality Management of Immersive Multimedia App...

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IEEE conference on Network Softwarization (NetSoft)

作者： Sam Van Damme Maria Torres Vega Filip De Turck IDLab Ghent University - imec

ISBN: (数字)9781728156842

ISBN: (纸本)9781728156859

Augmented Reality (AR) and Virtual Reality (VR) multimodal systems are the latest trend within the field of multimedia. As they emulate the senses by means of omnidirectional visuals, 360° sound, motion tracking and touch simulation, they are able to create a strong feeling of presence and interaction with the virtual environment. These experiences can be applied for virtual training (Industry 4.0), tele-surgery (healthcare) or remote learning (education). However, given the strong time and task sensitiveness of these applications, it is of great importance to sustain the end-user quality, i.e. the Quality-of-Experience (QoE), at all times. Lack of synchronization and quality degradation need to be reduced to a minimum to avoid feelings of cybersickness or loss of immersiveness and concentration. This means that there is a need to shift the quality management from system-centered performance metrics towards a more human, QoE-centered approach. However, this requires for novel techniques in the three areas of the QoE-management loop (monitoring, modelling and control). This position paper identifies open areas of research to fully enable human-centric driven management of immersive multimedia. To this extent, four main dimensions are put forward: (1) Task and well-being driven subjective assessment; (2) Real-time QoE modelling; (3) Accurate viewport prediction; (4) Machine Learning (ML)-based quality optimization and content recreation. This paper discusses the state-of-the-art, and provides with possible solutions to tackle the open challenges.

关键词： Quality of experience Measurement visualization Task analysis Quality of service Feeds Real-time systems

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Warehouse Management and Performance Measurement- A Case Study for an Automotive Wiring Industry

Warehouse Management and Performance Measurement- A Case Stu...

引用

International conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Abdelaziz Mrabti Zeineb Boumallessa Sana Bouajaja Khaled Nouri Advanced System Laboratory (LSA) Carthage university LR11ES27 Polytechnic School of Tunisia Tunis Tunisia Laboratory of Electromechanical Systems (LASEM) National Engineering School of Sfax Sfax Tunisia Optimization and Analysis of Industrial and Service Systems Laboratory National Engineering School of Tunis Higher Institut of Industrial Management of Sfax Sfax Tunisia

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

Effective warehouse management and performance measurement are critical for enhancing operational efficiency and competitiveness in industries, particularly in the automotive sector where just-in-time manufacturing practices are prevalent. This case study explores the implementation of warehouse management strategies and performance measurement techniques within an automotive wiring industry context. By utilizing a visual management, the study investigates the challenges faced by the company in managing its warehouse operations and evaluates the impact of various performance metrics on overall productivity and profitability. After the warehouse management, the scrap rate was reduced from 0.98 g/m to 0.3 g/m. The findings highlight the importance of aligning warehouse management practices with organizational goals and leveraging key performance indicators to drive continuous improvement initiatives. Additionally, the case study identifies best practices and recommendations for optimizing warehouse operations and enhancing performance measurement frameworks in the automotive wiring industry, offering valuable insights for practitioners and researchers alike.

关键词： Industries Wiring Productivity visualization Profitability Key performance indicator Companies

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Explainable Machine Learning for Regime-Based Asset Allocation

Explainable Machine Learning for Regime-Based Asset Allocati...

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IEEE International conference on Big Data

作者： Ruoyun Zhang Chao Yi Yixin Chen Data Center China Asset Management Co. Ltd Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

This paper explores an explainable AI model in the financial industry. Macroeconomic and market data serve as inputs of Hierarchical Clustering to distinguish among different economic regimes. Compared with traditional models such as Investment Clock, this method can adjust the classification standard in time according to recent market sentiment. The regime, therefore, can be interpreted by not only macro indicators but also investors' mood swings using Artificial Intelligence. When we compute the statistical characteristics of returns of each asset, we find that they can be well distinguished among regimes. This method can also identify the abnormally large wave of the stock market from 2015 to 2016 by separating it as an unusual regime, which cannot be realized by traditional methods. The clustering technique enables us to explain and understand the current market status and predict different assets' performances. Therefore, thanks to the superior interpretability of AI, the mean and variance of returns in each regime are estimated and viewed as viewpoints of the Black-Litterman asset allocation model to construct portfolios. To simulate the real situation, a dynamic backtesting method is used and asset weights change because of the rolling time windows. The results show that equipped with a simple timing strategy, the clustering technique can improve the results and yield excess returns. Some other machine learning techniques are also applied in an attempt to improve the model.

关键词： Data visualization Macroeconomics Numerical models Resource management Stock markets optimization Portfolios

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Smart Finance: An overview of Artificial Intelligence Integration in Fintech

Smart Finance: An overview of Artificial Intelligence Integr...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Parth Dhananjay Akre Utkarsha Pacharaney Warsha Siraskar Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The role of FinTech in advancing contemporary economies, societies, and technologies is growing. Smart FinTech is the next wave of FinTech that leverages artificial intelligence and data science methodologies to drive major advancements in fields including blockchain, cryptocurrencies, InsurTech, PayTech, BankingTech, TradeTech, LendTech, InsurTech, WealthTech, and RiskTech. An overview of these domains, as well as the artificial intelligence and data science techniques applied, is given in this paper. These techniques include augmentation, optimization, privacy-preserving processing, deep learning, federated learning, and intelligent interactions, complex system methods, mathematical approaches, knowledgeable communication, recognition and responses, and data analytics. This study offers a thorough analysis of smart financial companies, the obstacles they face, the smart FinTech environment, the artificial intelligence and data science methods that make smart FinTech possible, and potential future research areas for the artificial intelligence and data science community. To improve comprehension and analysis, the work makes use of visual aids such as figures and tables. The relevance of data quality, including data augmentation and manipulation, as well as enhancing fairness and decision-support, is emphasized in the study for financial firms. It also covers ethical and explicable FinTech, with a focus on privacy preservation, accountability, openness, and objectivity in FinTech and business designs. The potential for more research and innovation at the heart of analytics, data science, and FinTech is highlighted in the paper's conclusion.

关键词： Ethics visualization Technological innovation Fintech Education Companies Data science Regulation Risk management Artificial intelligence

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