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visual Verse 2.0: image Generation with image Inpaiting 1st

Visual Verse 2.0: Image Generation with Image Inpaiting

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1st International Research conference on Computing Technologies for Sustainable Development, IRCCTSD 2024

作者： Dharanidharan, R. Abinav Satya, S. Gokula Prasath, S. Muthurasu, N. SRM Institute of Science and Technology Tamil Nadu Vadapalani India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Vadapalani India

ISBN: (纸本)9783031823855

This research introduces a novel method for image enhancement and repair using Generative AI - Stable diffusion model. Unlike traditional techniques, it selectively masks and replaces areas to adjust aspect ratio while preserving details. Inspired by digital artists, it advances visual creation. Primary focus is on image inpainting, crucial for restoration, editing, and synthesis. Our approach merges deep learning in Generative AI with Stable diffusion, enhancing quality and stability of inpainted images. Stable diffusion models simulate gradual information spread across missing areas, yielding coherent outcomes. Integration of generative AI enables understanding of image structures, aiding realistic content generation. Inpainting guided by stable diffusion ensures seamless transitions and contextual coherence preservation. Adaptive contextual understanding fills missing regions based on global and local context. Evaluation criteria assess image quality, coherence, and similarity to the original. Results advance inpainting techniques for real-world applications, addressing challenges with improved visual quality and contextual consistency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

1st International Conference on Pattern Analysis and Machine...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

ISBN: (纸本)9789819633487

The proceedings contain 28 papers. The special focus in this conference is on Pattern Analysis and Machine Intelligence. The topics include: Development of a Low Cost 3D LiDAR Using 2D LiDAR and Servo Motor;the Design of Machine Vision-Based Waste Sorting System;ECLNet: Efficient Convolution with Lite Transformer for 3D Medical image Segmentation;exploring High-Performance 3D Object Detection with Partial Depth Completion;full-Scale Network for Remote Sensing Object Detection;Detection of Pedestrian Movement Poses in High-Speed Autonomous Driving Environments Using DVS;city-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model;an Efficient Transformer-Based Network for Remote Sensing image Change Detection;the Method for Three-Dimensional visual Measurement of Circular Markers Based on Active Fusion Technology;intelligent image Recognition and Classification Technology in Digital Media;Indoor Visible Light Positioning System Based on the image Sensor and CNN-GRU Fusion Neural Network;Stock Investor Sentiment Analysis Based on NLP;Novel Audiobook System Based on BERT;student Enrollment Consultation Q&A Robot Based on Large Language Model;family Doctor Model Training Based on Large Language Model Tuning;composite Awareness-Based Knowledge Distillation for Medical Anomaly Detection;Improved CNN-GRU RF Fingerprint Feature Recognition Method Based on Comb Filter;emotional State Recognition of English Learners Based on Deep Learning;Application of Classification Framework Based on CDR and CNN in Ophthalmic Prediagnosis;visual Recognition and Recommendation System for Cultural Tourism Attractions Based on Deep Learning;quadruped Robot System Based on Proprioceptive Vision and Complex Ground Mobility Capabilities;a Simulated Dataset to Evaluate the visual-Inertial Odometry Algorithms.

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Exploring Diverse Techniques in image and Video Forgery 1st

Exploring Diverse Techniques in Image and Video Forgery

引用

1st International conference on Machine Learning Algorithms, ICMLA 2024

作者： Dhiman, Neha Singh, Hakam Thakur, Abhishek School of Engineering and Technology Chitkara University Himachal Pradesh Baddi India

ISBN: (纸本)9783031758607

The versatility of leading editing tools, access to the internet and technological advancement have led to a sudden rise in visual forgery, resulting in manipulation, tampering and fabrication of content or information. The digital divide in affordable access and infrastructure led to prominent innovation and challenges. The visual forgery gives rise to challenges or threats to multimedia data's trustworthiness and integrity, which requires robust detection and prevention methods. This paper reviews existing approaches and techniques to authenticate and identify fabricated images and videos. In addition to this, technological advancements in deep learning and computer vision have led to the development of better algorithms. Mitigating visual forgery problems requires a multidisciplinary approach combining traditional forensics technologies, machine learning and ethical consideration. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Design of AXI Bus-Based IP Core for image processing 12th

Design of AXI Bus-Based IP Core for Image Processing

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12th CCF conference on BigData, BigData 2024

作者： Chen, Yutong Yi, Zhongchao Li, Xuqiang Chen, Xingyan Chen, Yanjiang School of Software Engineering University of Science and Technology Hefei China School of Computer Science and Technology University of Science and Technology Hefei China

ISBN: (纸本)9789819610235

This study addresses the significant issue of image blur through image restoration techniques that reduce or remove blur and detect blurry regions, improving image quality and analysis potential. Utilizing FPGA-based digital hardware systems, the research leverages FPGA-supported systems like ZYNQ and ARM, using the Advanced eXtensible Interface (AXI) and Advanced Microcontroller Bus Architecture (AMBA) for streamlined device communication. The design incorporates specialized Intellectual Property (IP) cores such as Square Root (Sqrt) and Shift RAM, tailored to overcome the limitations of development cycles and processing algorithms, focusing on portability across various platforms including domestic FPGAs. The practical implementation features modules for axis-stream transmission, RGB to YUV conversion, image smoothing using Gaussian, mean, and median filters, advanced image processing with Sobel edge detection, and erosion-dilation modules. A ZYNQ verification system with an OV5640 camera and HDMI output substantiates the functionality and efficiency of the designed IP core, which is further analyzed for resource and power consumption using a High-Level Synthesis (HLS) tool. The study successfully demonstrates a robust IP core for blurry image processing, significantly enhancing the field of image processing and embedded systems, laying a strong groundwork for future advancements and porting endeavors, and highlighting the efficacy of FPGA-based image processing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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VIPNet: Combining Viewpoint Information and Shape Priors for Instant Multi-view 3D Reconstruction 17th

VIPNet: Combining Viewpoint Information and Shape Priors fo...

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17th Asian conference on Computer Vision, ACCV 2024

作者： Ye, Weining Li, Zhixuan Jiang, Tingting School of Computer Science Peking University Beijing100871 China College of Computing and Data Science Nanyang Technological University Singapore Singapore National Engineering Research Center of Visual Technology National Key Laboratory for Multimedia Information Processing School of Computer Science National Biomedical Imaging Center Peking University Beijing100871 China

ISBN: (纸本)9789819609680

While the multi-view 3D reconstruction task has made significant progress, existing methods simply fuse multi-view image features without effectively leveraging available auxiliary information, especially the viewpoint information for guiding and associating features of different views. To this end, we propose to enhance multi-view 3D reconstruction with the power of viewpoint information. Specifically, a simple-yet-effective viewpoint estimator is designed to learn and provide comprehensive viewpoint knowledge for locating and associating learned features from different views. Moreover, to improve the 3D reconstruction quality when 2D images of only very few viewpoints are available, we propose to learn the shape prior knowledge to provide sufficient shape information for compensating the limited 2D observations. Overall, we present VIPNet, benefiting from Viewpoint Information and Shape Prior learning for high-quality multi-view 3D reconstruction. Extensive experiments validate the effectiveness of the proposed VIPNet, which achieves state-of-the-art performance on challenging datasets and shows well generalization ability in real-world scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： 3D modeling

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Research on Real Scene image Super-Resolution Based on Diffusion Model 24

Research on Real Scene Image Super-Resolution Based on Diffu...

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Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Yu Liu Xuan Li Yuxia Bie Shenyang Aerospace University Shenyang Liaoning China

ISBN: (纸本)9798400713880

Previous image super-resolution algorithms based on convolutional neural networks have primarily been trained on ideal synthetic datasets; however, their performance often declines significantly when applied to real-world scenarios. To effectively extract original information from real scene images, this paper proposes a generative diffusion model, Diffusion-Net, designed for real scene images, which integrates diffusion models into a U-shape network. Specifically, the backbone of this model consists of multiple layers of encoders and decoders, with processed images being fed into the diffusion model for further optimization. Additionally, we define a hybrid loss function to enhance the visual quality of the reconstructed images. To accelerate the model's training speed, a feature distillation module is designed. Furthermore, a multi-head attention mechanism is incorporated to enable the model to reconstruct high-quality texture details. The model is trained on the RealSR dataset, and experimental results demonstrate that, compared to state-of-the-art super-resolution models (such as RCAN and SwinIR), Diffusion-Net achieves superior performance across three key evaluation metrics—Peak Signal-to-Noise Ratio, Structural Similarity Index, and No-Reference image Quality Assessment—while also exhibiting enhanced visual effects.

关键词： Diffusion model

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Correcting Road image Annotations 20th

Correcting Road Image Annotations

引用

20th International conference on Computing in Civil and Building Engineering, ICCCBE 2024

作者： Lam, Percy Chen, Weiwei de Silva, Lavindra Brilakis, Ioannis Civil Engineering Building University of Cambridge 7a JJ Thomson Avenue CambridgeCB3 0FA United Kingdom Bartlett School of Sustainable Construction University College London 1-19 Torrington Place LondonWC1E 7HB United Kingdom

ISBN: (纸本)9783031842078

Digitisation provides a promising avenue to meet new socio-economic demands in infrastructure delivery. The current state of practice suggests that human annotators still need to participate substantially in data preparation for digitising infrastructure, such as annotating real-life domain-specific visual datasets for road maintenance. Research in the past focuses on predicting better labels with less human effort, leaving a gap in not maximizing the gains from the subsequent human corrections of pseudo-labels to ground truths. The gap highlights the opportunities for a solution to mimic human corrections by "correcting like instances alike". We propose an extension to Mask R-CNN to tackle this gap. Our auto-correction method harvests learnings from past corrections and automates corrections in forthcoming images. The method first gauges the corrections made between the pseudo-labels and the final ground truth. The method then compares the feature vectors and attribute data of a new batch of unlabelled images against past corrections. When the deep learning model predicts similarly wrong features, the method will mimic human corrections in the past and prompt additions, deletions or category changes. The method concludes with post-processing to eliminate unwanted predictions. This similarity-based approach improves the precision of the testing batch by 15–70% and reduces the number of mouse clicks by approximately 20%. The solution therefore partially automates the human review after predicting pseudo-labels by similarity-based corrections. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image annotation

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Comparison of On-Board and Off-Board processing Power Consumption for Drone Camera images

Comparison of On-Board and Off-Board Processing Power Cons...

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16th International conference on ICT Innovations, ICT Innovations 2024

作者： Mitrev, Atanasko Boris Stojkoska, Biljana Risteska Faculty of Computer Science and Engineering Ss. Cyril and Methodius University in Skopje Skopje North Macedonia

ISBN: (纸本)9783031861611

Unmanned aerial vehicles (UAVs) equipped with cameras, capture images and videos from high altitude, usually for surveillance purpose. This paper investigates the power consumption of a drone processing unit under two different operational scenarios: (i) On-Board (local image processing using an onboard Raspberry Pi) and (ii) Off-Board (streaming video over Wi-Fi for remote processing). Therefore, we deployed the well-known YOLO (You Only Look Once) image detection algorithm on the Raspberry Pi and investigated the power consumption for the both scenarios. Contrary to the expectations, our findings prove that energy consumption is very similar in both scenarios. The results of this study are valuable for designers of energy-efficient UAV systems, particularly for applications where prolonged flight duration and real-time data processing are critical. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computer vision Drone Energy Efficiency IoT Object detection Raspberry Pi UAV Wi-Fi Streaming

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Using Convolutional Neural Networks for image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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Pervasive Computational Technologies (ICPCT), International conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things image classification

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CNN-Based Circular Economy in E-Waste Management with Blockchain and IoT Integration

CNN-Based Circular Economy in E-Waste Management with Blockc...

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visual Analytics and Data visualization (ICVADV), International conference on

作者： P. Santhuja A Suresh Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Increasing electronic waste (e-waste) is a serious environmental and financial problem that calls for creative ideas for effective resource recovery and recycling. This paper proposes an IoT- and Blockchain-integrated e-waste management system based on Convolutional Neural Networks (CNNs) to boost automated waste classification and, thus, circular economy practices. While Blockchain guarantees open and safe tracking of waste transportation, IoT sensors and RFID tags allow real-time monitoring of e-waste. Training on a collection of 50,000 e-waste images, a CNN-based image classification model attained an accuracy of 96.2% in classifying components into recyclables, reusables, and hazardous items. Using automated decision-making, the system showed a 28% reduction in processing time and a 32% gain in sorting efficiency over conventional approaches. Blockchain incorporation enhanced traceability through 100% secure transaction records, lowering fraud and illegal disposal. Experimental data show that this method improves 23% resource recovery rates by promoting sustainable e-waste management. The proposed architecture minimizes environmental effects by encouraging ethical e-waste disposal and a closed-loop recycling system. These results show how Blockchain-secured, AI-driven IoT devices might help to advance circular economic ideas for world e-waste management.

关键词： Waste management Training Accuracy Electronic waste Real-time systems Recycling Blockchains Internet of Things Convolutional neural networks Sorting

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