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检索条件"任意字段=Conference on Visualization and Optimization Techniques"

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Perceptual quality metric applied to wavelet-based 2D region of interest image coding

Perceptual quality metric applied to wavelet-based 2D region...

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International conference on Intelligent Systems: Theories and Applications (SITA)

作者： Mohammed Nahid Abderrahim Bajit Abdennaceur Baghdad FSTM Faculty UHIIC University Morocco Department of Physics Mohamed V University Rabat Morocco

ISBN: (纸本)9781509057825

Region of interest ROI image and video compression techniques have been widely used in visual communication applications in an effort to deliver good quality images and videos at limited bandwidths. Foveated imaging exploits the fact that the spatial resolution of the human visual system (HVS) is highest around the point of fixation (foveation point) and decreases dramatically with increasing eccentricity. Exploiting this fact, we have developed an appropriate metric for the assessment of ROI coded images, adapted to foveation image coding based on psychovisual quality optimization tools, which objectively enable to assess the visual quality measurement with respect to the region of interest (ROI) of the human observer. The proposed metric yields a quality factor called foveation probability score (FPS) that correlates well with visual error perception, and demonstrating very good perceptual quality evaluation. Thus, we avoid the traditional subjective criteria called mean opinion score (MOS), which involves human observers, are inconvenient, time-consuming, and influenced by environmental conditions. Widely used pixelwise measures such as the mean square error (MSE) and the peak signal to noise ratio (PSNR) cannot capture the artifacts like blurriness or blockiness. The model therefore called the Foveated Wavelet Visible Difference Predictor FWVDP, is based both on traditional psychometric function and the Daly model visible difference predictor (VDP) and integrates various psychovisual models (foveation, luminance and contrast masking). Experiments show that FWVDP integrates foveation filtering with foveated image coding and demonstrates very good performance in terms of foveated image quality measurement.

关键词： Measurement Image coding Discrete wavelet transforms Predictive models Mathematical model visualization

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Application Research of Operation and Maintenance Scene Fusion and Intelligent Knowledge Assistant

Application Research of Operation and Maintenance Scene Fusi...

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Computers, Information Processing and Advanced Education (CIPAE), 2020 International conference on

作者： Feifeng Wang Zhengping Lin Xin Wang Dajian Li Chunzhi Meng Jian Qin Guangxi Power Grid Co.Ltd. Electric Power Science Research Institute Nanning China Electric Power Research Institute of China Southern Power Grid Company Guangzhou China Guangxi Power Grid Co. Ltd. Nanning China Digital Intelligence Operation Center of Guangxi Power Grid Co. Ltd. Nanning China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

In order to improve the intelligent level of power measuring equipment operation and maintenance, this paper introduces intelligent knowledge assistance technology into the reconstruction of power measuring equipment maintenance and operation scenario. The scene view of power measuring equipment operation is fused by vector library. A 3D vision imaging model based on machine vision was established. The feature of operation and maintenance scene is extracted by using fuzzy degree detection and visual reconstruction, and three-dimensional reconstruction is carried out by intelligent knowledge assistant technology. This paper uses pixel-based image fusion and virtual reality rendering techniques to enhance visual information in the operating environment. Multiple view images are encoded with the corresponding camera parameters and attitude information. According to the power metering device operation and maintenance scenario, the spatial distribution set is reorganized to achieve 3D VR vision reconstruction optimization. Simulation results show that the algorithm proposed in this paper can reproduce the running state of electric energy measuring equipment quickly and accurately.

关键词： visualization Solid modeling Power measurement Three-dimensional displays Heuristic algorithms Virtual reality Feature extraction Power grids Maintenance Image reconstruction

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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation

Enhancing the Transferability of Adversarial Attacks with St...

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IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Xinwei Zhang Tianyuan Zhang Yitong Zhang Shuangcheng Liu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks are susceptible to attacks from adversarial examples in recent years. Especially, the black-box attacks cause a more serious threat to practical applications. However, while most existing black-box attacks have achieved a high success rate in deceiving models, they have not focused on the stealthiness of adversarial examples, often exhibiting suspicious visual appearances. To address this issue, this paper proposes the Mask Momentum Iterative Attack (MMIA), which introduces a masking mechanism and adopts an optimal perturbation strategy to identify regions of an image most vulnerable to attacks. This approach effectively ensures the transferability and stealthiness of adversarial examples. Simultaneously, by integrating image enhancement techniques and temporal and spatial momentum terms into the iterative process of the attack, we prevent the attack from getting stuck in local optima, further improving the transferability of adversarial examples. To enhance the success rate of black-box attacks, we apply MMIA to a model ensemble using a joint optimization strategy. We demonstrate that adversarially trained models with a strong defense ability are also susceptible to our black-box attacks. We conduct extensive experiments on classification tasks using common vision models, and our results significantly demonstrate the superiority of our method over state-of-the-art approaches when considering both transferability and stealthiness.

关键词： visualization Fuses Computational modeling Perturbation methods Closed box Pattern recognition Iterative methods

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Machine Learning (ML) based Electricity Price Prediction with visualization

Machine Learning (ML) based Electricity Price Prediction wit...

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Automation, Computing and Renewable Systems (ICACRS), International conference on

作者： Abisha D Prasanna Kumar D Ida Christy J Navedha Evanjalin R Rajesh P Mohamed Haris K Department of Computer Science and Engineering National Engineering College Kovilpatti India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Electricity price prediction is crucial for businesses reliant on heavy machinery or computing services, as it aids in cost management and energy optimization. Accurate predictions allow these businesses to forecast annual electricity expenses, enhancing cost efficiency. This project aims to forecast electricity prices using machine learning techniques, specifically the Random Forest regression algorithm. The project utilizes historical data on electricity consumption and other influencing factors, including date and time, holiday indicators, day of the week, week of the year, forecasted and actual wind production, national load, temperature, wind speed, and CO2 intensity. The methodology involves converting string values to numerical values, handling missing data, and normalizing the dataset, identifying key features that significantly impact electricity prices, Splitting the data into training and testing sets, and training the model using the Random Forest regression algorithm and assessing the model’s performance with appropriate metrics to ensure accuracy and reliability. The results demonstrate that the Random Forest model effectively predicts electricity prices with high accuracy. Visualizing these predictions in Power BI provides intuitive insights into the factors influencing electricity prices. These findings enable businesses to better anticipate electricity expenses, optimize energy usage, and improve financial planning. The comprehensive approach outlined in this project offers a valuable tool for understanding and forecasting electricity prices, thereby enhancing resource management and cost efficiency.

关键词： Training Costs Accuracy Electricity Prediction algorithms Data models Numerical models Wind forecasting Random forests Business

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Towards Accurate Renal Micro-Structure Segmentation: An Assessment of YOLOv8 and Mask R-CNN Models

Towards Accurate Renal Micro-Structure Segmentation: An Asse...

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IEEE International WIE conference on Electrical and Computer Engineering (WIECON-ECE)

作者： Md. Estiak Ahmed Farhana Tazmim Pinki Mozdaher Abdul Quader Montaser Abdul Quader Fateha Jannat Ayrin Ahmed Selim Anwar Prosenjit Roy Computer Science and Engineering Discipline Khulna University (KU) Department of Computer Science and Engineering Green University of Bangladesh (GUB) Department of Computer Science and Engineering Chittagong University (CU) Department of EEE Islamic University of Technology (IUT) Department of Computer Science Prairie View A & M University Texas United States

ISBN: (数字)9798331535476

ISBN: (纸本)9798331535483

In the field of medical imaging, correct instance segmentation is essential. This work attempts to address the problems related to renal micro-structure segmentation by using the power of YOLOv8 and special MASK R-CNN models to facilitate the development of more rapid and accurate nephrology diagnosis, which will benefit physicians and other healthcare providers. Improved medical imaging analysis is made possible by the model's high precision rate of “glomerulus” and “blood vessel” structure detection. The application of the MASKRCNN framework, which successfully integrated pertinent annotations to boost visual understanding, increased the clarity of image interpretation. Additionally, this work uses the HuBMAP dataset to segregate renal microstructures using the YOLOv8 and Mask R-CNN techniques. Strong performance was shown by the YOLOv8 model, which received an IoU score of 84.55%. The Mask R-CNN model received an IoU score of 80.66%, indicating its potential. It is an impressive accomplishment to obtain a high IoU score for YOLOv8 in the context of medical photo segmentation, since this suggests that there is still room for further improvement and optimization of the model. For medical professionals, the IoU scores are crucial since they offer a technique to improve accuracy and reliability when it comes to nephrology diagnosis. In order to improve diagnosis accuracy and enable broader clinical adoption, future research aims to improve these IoU ratings. The observed disparity underscored the inherent complexities associated with differentiating between ambiguous kidney structures and also the pressing need for further advancement. Our preliminary work demonstrates the potential of MASK-RCNN and YOLOv8 in the challenging field of renal micro-structure segmentation.

关键词： visualization Accuracy Nephrology Medical services Pressing Reliability Microstructure Medical diagnostic imaging optimization Kidney

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Mitigating Garlic Insect Pests and Diseases Using Knowledge-Driven Decision Support System

Mitigating Garlic Insect Pests and Diseases Using Knowledge-...

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International conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： James Patrick A. Acang Gerry L. Contillo Dionisio S. Bucao Nathaniel R. Alibuyog Ryan J. Pascual Evangeline S. Galacgac Marcilyn B. Manuel Kyle Cedric A. Dadoy Department of Computer Science Mariano Marcos State University Garlic and Other Agri-Food Condiments R&D Center Mariano Marcos State University City of Batac Ilocos Norte Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

Modern farming practices are being transformed by innovative technological solutions like advanced garlic disease detection systems and cutting-edge weather forecasting applications. By providing garlic cultivators with access to precise and real-time data, these transformative tools empower them to make data-driven decisions that significantly enhance crop management techniques and increase yields. The garlic disease detection systems use state-of-the-art machine learning and image analysis methods to guarantee early identification and classification of potential diseases. The weather forecasting apps utilize sophisticated algorithms that incorporate historical weather data, soil conditions, and pest and disease occurrences to assist in the strategic allocation of resources and the meticulous management of irrigation schedules. The technological marvels have brought about a promising era for garlic agriculture, one that emphasizes sustainability and efficiency, ultimately bolstering global food security through the optimization of garlic cultivation practices.

关键词： Irrigation visualization Insects Weather forecasting Crops Food security Resource management

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LTM: Lightweight Textured Mesh Extraction and Refinement of Large Unbounded Scenes for Efficient Storage and Real-Time Rendering

LTM: Lightweight Textured Mesh Extraction and Refinement of ...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jaehoon Choi Rajvi Shah Qinbo Li Yipeng Wang Ayush Saraf Changil Kim Jia-Bin Huang Dinesh Manocha Suhib Alsisan Johannes Kopf University of Maryland Meta

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Advancements in neural signed distance fields (SDFs) have enabled modeling 3D surface geometry from a set of 2D images of real-world scenes. Baking neural SDFs can extract explicit mesh with appearance baked into texture maps as neural features. The baked meshes still have a large memory footprint and require a powerful GPU for real-time rendering. Neural optimization of such large meshes with differentiable rendering pose significant challenges. We propose a method to produce optimized meshes for large unbounded scenes with low triangle budget and high fidelity of geometry and appearance. We achieve this by combining advancements in baking neural SDFs with classical mesh simplification techniques and proposing a joint appearance-geometry refinement step. The visual quality is comparable to or better than state-of-the-art neural meshing and baking methods with high geometric accuracy despite significant reduction in triangle count, making the produced meshes efficient for storage, transmission, and rendering on mobile hardware. We validate the effectiveness of the proposed method on large unbounded scenes from mip-NeRF 360, Tanks & Temples, and Deep Blending datasets, achieving at-par rendering quality with 73 x reduced triangles and 11 x reduction in memory footprint.

关键词： Geometry visualization Solid modeling Three-dimensional displays Memory management Rendering (computer graphics) Feature extraction

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Adaptive Heuristic Pathfinding Algorithm (AHPA): A Dynamic Path Adjuster for Mobile Robots

Adaptive Heuristic Pathfinding Algorithm (AHPA): A Dynamic P...

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International conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Akash Aman Abir Debnath Saurabh Kumar Rahul Dev Sarkar Rapti Chaudhuri Suman Deb Department of CSE Indian Institute of Information Technology Agartala India Department of CSE National Institute of Technology Agartala India

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

The development of advanced algorithms in the field of autonomous systems has transformed navigation capabilities in both dynamic as well complex situations. The root algorithms like A* and Dynamic A*(D*) dynamically update Cost Function in case of obstructions blocking the path. In these cases of algorithms the reliable navigation is not guaranteed for sudden clearance of obstacles and availability of the shortest path. In order to cope with this situation, we have exploited Trajectory optimization using Adaptive Heuristic Pathfinding Algorithm (AHPA). It utilizes Self-Adjusting, re-planning, and Adaptive real-time path-planning techniques that combine the features of both progressive and eternal strategists to provide simplified solutions to complex environments. To efficiently handle states, it makes use of g-values, ACTIVE-SET, PROCESSED-SET, and FROZEN-SET along with an Expansion Factor(EF) $\delta$ which should be greater than 1. A lower value of $\delta$ indicates fewer cells need to be expanded in the grid and vice versa, thus optimizing the overall path planned. It estimates costs and provides visual representations of the navigation process through the use of heuristic functions like MD(Manhattan Distance) and ED(Euclidean Distance). In order to improve situational awareness, including dynamic obstacles the proposed method maneuvers thorough assessments and constantly re-calibrate routes to closely examine their effectiveness and potential to quickly adjust and react when it comes to shifting surroundings. With an Expansion Factor(EF) $\delta$ valued at 1.5, it expands fewer cells which enables it to meet real-time objectives in dynamic contexts with great efficiency (Execution time $=3.0254\text{ sec}$ , path planned $=62.468\text{cm}$ , and memory usage = 10MB). This optimized algorithm enables a wide range of applications across transportation, logistics, exploration, and beyond, from mobile robots to autonomous ground vehicles.

关键词： visualization Navigation Heuristic algorithms Memory management Transportation Real-time systems Path planning Mobile robots Vehicle dynamics Trajectory optimization

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Enhancing Insect Image Recognition with Sand Cat Swarm optimization with Deep Feature Extraction

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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International conference on Advances in Information Technology (ICAIT)

作者： C A Yogaraja C Priyanka Priyadharshini. SP J.Jagan Babu S. Rajagopal S. Poorani Computer Science and Engineering Ramco Institute of Technology Rajapalayam School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus Department of Artificial Intelligence and Machine Learning (AML) St. Joseph's College of Engineering OMR Chennai Department of ECE R.M.D Engineering college Kavaraipettai Department of IT National Engineering College Kovilppati Computer Technology-UG Kongu Engineering College Erode

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect Image Recognition with Sand Cat Swarm optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species.

关键词： visualization Image recognition Insects Biological system modeling Noise Feature extraction Image sequences

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A Novel Approach to Outlining Research Data Management Life Cycle: A Case Study 10th

A Novel Approach to Outlining Research Data Management Life...

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10th International conference on Information Management, ICIM 2024

作者： Yazdi, M. Amin Politze, Marius Müller, Matthias IT Center RWTH Aachen University Aachen Germany

ISBN: (纸本)9783031643583

In the increasingly complex domain of academic research, efficient and effective Research Data Management (RDM) emerges as a vital component. It offers a systematic approach to managing, preserving, and sharing data, fostering improved collaboration, and ensuring reproducibility while maintaining compliance with relevant funding and institutional regulations. Given the multifaceted nature of RDM, there is a pressing need for an accurate method to outline its phases and determine the exact stage of a research project within these phases to assist service managers in the early identification of user challenges and discover process-aware requirements. This paper addresses this demand by presenting an innovative methodology that leverages process mining techniques to discover and assess the various phases of RDM and to measure the current RDM activities of research projects. Moreover, this proposed methodology is evaluated by applying it to an extensive series of recorded user interactions within an active RDM platform. The approach has been found to provide valuable insights into the RDM life cycle, opening a window into monitoring and sustaining the progress of research projects and the activities of research groups, thereby significantly contributing to the understanding and optimization of the RDM process within the broader academic research community. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data visualization

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