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image Enhancement and Corrosion Detection for UAV visual Inspection of Pressure Vessels 7th

Image Enhancement and Corrosion Detection for UAV Visual Ins...

引用

7th International conference on Life System Modeling and Simulation, LSMS 2021, and 7th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Fei, Zixiang Yang, Erfu Yang, Beiya Yu, Leijian Department of Design Manufacturing and Engineering Management University of Strathclyde GlasgowG1 1XJ United Kingdom

ISBN: (纸本)9789811672064

The condition of a pressure vessel is normally checked by human operators, which have health and safety risks as well as low working efficiency and high inspection cost. visual inspection for pressure vessels can be done by an Unmanned Aerial Vehicle (UAV) with the sensing module. image enhancement techniques and image processing techniques are vital in the UAV inspection of pressure vessels. However, there are several issues to be overcome in the UAV visual inspection of pressure vessels. The images captured by the UAV are of low quality under the cluttered environment due to poor lighting, noises and vibrations caused by the UAV. In this research, a system is developed for UAV visual inspection of pressure vessels using image processing and image enhancement techniques. In the developed system, the input image is captured by the UAV first. Next, efficient image enhancement techniques are applied to the images in order to enhance image qualities. After that, the corrosion part is detected and the percentage of the corrosion area in the entire image is measured. The proposed system has the potential to be implemented for the autonomous correction detection with the image enhancement techniques in UAV visual inspection for the pressure vessels. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image enhancement

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A Level Set Model Driven by New Signed Pressure Force Function for image Segmentation 27

A Level Set Model Driven by New Signed Pressure Force Functi...

引用

27th National conference on communications (NCC)

作者： Biswas, Soumen Hazra, Ranjay Prasad, Shitala Sirvee, Arvind Natl Inst Technol Silchar Dept Elect & Instrumentat Engn Silchar India Inst Infocomm Res Dept Visual Intelligence Singapore Singapore Univ Coll Engn & Technol Dept Elect & Commun Engn Bikaner India

ISBN: (纸本)9781665441773

An image segmentation model using histogram-based image fitting (HF) energy is proposed to identify objects with poorly defined boundaries. The proposed energy model considers an improved fitting energy function based on normalized histogram and average intensities of objects inside as well as outside the contour curve. The fitting energy functions are computed before the curve evolution thereby reducing the complexity of intensity inhomogeneity images. Further, a new signed pressure force function is incorporated in the proposed energy model which can increase the efficiency of the curve evolution process at blur edges or at weak edge regions. The comparative analysis of the proposed energy model produces better segmentation results compared to the other state-of-the-art energy models namely the Li et. al. model, local binary fitting (LBF), and Chen-Vese (C-V) models. The proposed model is also robust to intensity inhomogeneity. In addition, the calculation of the Jaccard Index (JI) proves the robustness of the proposed energy model.

关键词： Histogram fitting Level set signed pressure force image segmentation

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A Hybrid Transfer Learning Architecture Based image Captioning Model for Assisting visually Impaired

A Hybrid Transfer Learning Architecture Based Image Captioni...

引用

ieee Applied Signal processing conference (ASPCON)

作者： Moumita Sarkar Shreya Biswas Biswarup Ganguly Electronics & Communication Engineering Department Techno Main Saltlake Kolkata India Clavictor Academy Private Limited Foundation for Technology & Business Incubation National Institute of Technology Rourkela Odisha India MIEEE Electrical Engineering Department National Institute of Technology Silchar Assam India

The task of automatically presenting the subject matter of a picture without human involvement is a highly intricate task. The issue at hand is commonly addressed through the utilization of computer vision and natural language processing techniques. Firstly, the image content must be analyzed and interpreted. Subsequently, this understanding needs to be transformed into grammatically accurate and contextually meaningful sentences. An issue that arises when employing deep learning (DL) based model that produces insufficient understanding of semantic context along with the encoder’s inability to transmit essential visual information to the decoding unit in an efficient manner. To mitigate this issue, a hybrid transfer learning scheme has been proposed that is capable of generating image captions with a high degree of accuracy and minimal occurrence of errors. In this article, Inception module has been fused with residual network for extracting significant features from the images for encoding. Gated recurrent unit has been used as the decoder by producing appropriate captions for the images. The novelty lies in the inclusion of an attention layer that facilitates the acquisition of a linguistically relevant and syntactically coherent representation by the model. The attention interface is situated between the encoder and decoder components. Experimental study reveals the effectiveness of the proposed hybrid DL framework over existing DL methods. The proposed image captioning model can further be implemented for assisting the visually impaired.

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Optimization in Mobile Augmented Reality Systems for the Metaverse Over Wireless communications

Optimization in Mobile Augmented Reality Systems for the Met...

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ieee conference on Global communications (GLOBECOM)

作者： Tianming Lan Jun Zhao School of Computer Science and Engineering Nanyang Technological University Singapore

As the essential technical support for Metaverse, Mobile Augmented Reality (MAR) has attracted the attention of many researchers. MAR applications rely on real-time processing of visual and audio data, and thus those heavy workloads can quickly drain the battery of a mobile device. To address such problem, edge-based solutions have appeared for handling some tasks that require more computing power. However, such strategies introduce a new trade-off: reducing the network latency and overall energy consumption requires limiting the size of the data sent to the edge server, which, in turn, results in lower accuracy. In this paper, we design an edge-based MAR system and propose a mathematical model to describe it and analyze the trade-off between latency, accuracy, server resources allocation and energy consumption. Furthermore, an algorithm named LEAO is proposed to solve this problem. We evaluate the performance of the LEAO and other related algorithms across various simulation scenarios. The results demonstrate the superiority of the LEAO algorithm. Finally, our work provides insight into optimization problem in edge-based MAR system for Metaverse.

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Enhanced Threshold-based Segmentation for Maize Plantation 7

Enhanced Threshold-based Segmentation for Maize Plantation

引用

7th International conference on Communication, image and Signal processing, CCISP 2022

作者： Gumiran, Joel M. Fajardo, Arnel F. Medina, Ruji P. Technological Institue of the Philippines Quezon City Quezon City Philippines College of Computing Studies Information and Communications Technology Isabela State University Isabela Philippines

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

Phenotyping, mainly plant' health monitoring, is labor-and time-intensive, particularly for large-scale operations like maize plantations. Therefore, this research used a drone equipped with an RGB image to photograph the whole plantation quickly. On the other hand, RGB photographs do not categorize plants and weeds due to high brightness, shadows, and overlapped foliage. Therefore, several segmentation algorithms are used to solve various challenges. For instance, threshold-based segmentation can only accept progressive illumination, which is crucial for outdoor lighting, simplicity, and distinguishing objects with identical hues. For this kind of segmentation, however, intense light requires modification. Consequently, threshold-based segmentation was improved to normalize the disturbances above while rapidly separating leaves from weeds. In this manner, the Enhanced threshold-based segmentation had applied to RGB images of maize plantations like cornfields with distractions seen in the gathered photos with a segmentation accuracy of 92.41%. In comparison, the threshold-based segmentation had used in the same dataset without normalizing the picture's luminance, with a segmentation accuracy of 5.71%. Thus, the enhanced segmentation method improved segmentation accuracy by 86.7% compared to threshold-based segmentation, which is limited to extreme light conditions. Thus, the incorporated normalization in the segmentation process significantly increases the segmentation accuracy. © 2022 ieee.

关键词： Luminance

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Analysis of Intravesical Prostatic Protrusion Ultrasound image

Analysis of Intravesical Prostatic Protrusion Ultrasound Ima...

引用

Electronic communications, Internet of Things and Big Data (ICEIB), International conference on

作者： Ching-Ting Chou Jian-Jiun Ding Meng-Lin Chang Communication Engineering National Taiwan University Taipei Taiwan Department of Urology Fu Jen Catholic University Hospital New Taipei City Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Traditional ultrasound images are interpreted manually. However, we proposed a formulaic approach for processing ultrasound images and interpreting ultrasound images through computer vision image analysis. We determined the depression index to assess the condition of the prostate protruding into the bladder. The identification methods included noise elimination, edge detection, dynamic threshold, morphology, and shortest distance evaluation. Additionally, we employed the principal component analysis (PCA) and vertical distance. By eliminating noise and determining relative positions, regularization, threshold comparison, segmentation and merging, and elliptical approximation of bladder contours were inferred to diagnose the intravesical prostatic protrusion (IPP) using bladder ultrasound images. By utilizing elliptical approximation, we reconstructed the original appearance of the bladder from the ultrasound images.

关键词： Ultrasonic imaging Machine learning algorithms Noise Merging Morphology Machine learning Medical services

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ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP image ANIMATION

ULTRA-LOW BITRATE VIDEO CONFERENCING USING DEEP IMAGE ANIMAT...

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ieee International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Konuko, Goluck Valenzise, Giuseppe Lathuiliere, Stephane Inst Polytech Paris Telecom Paris LTCI Paris France Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Paris France

ISBN: (纸本)9781728176055

In this work we propose a novel deep learning approach for ultra-low bitrate video compression for video conferencing applications. To address the shortcomings of current video compression paradigms when the available bandwidth is extremely limited, we adopt a model-based approach that employs deep neural networks to encode motion information as keypoint displacement and reconstruct the video signal at the decoder side. The overall system is trained in an end-to-end fashion minimizing a reconstruction error on the encoder output. Objective and subjective quality evaluation experiments demonstrate that the proposed approach provides an average bitrate reduction for the same visual quality of more than 80% compared to HEVC.

关键词： Video compression Video conferencing Model-based compression Deep learning

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Guarding Against ChatGPT Threats: Identifying and Addressing Vulnerabilities

Guarding Against ChatGPT Threats: Identifying and Addressing...

引用

ieee conference on Multimedia Information processing and Retrieval (MIPR)

作者： Dingzong Zhang Khushi Jain Priyanka Singh School of EECS The University of Queensland Brisbane Australia

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

This study examines the dual nature of ChatGPT as a potential tool that can act both as an attacker and a defender in the cybersecurity domain. It highlights how this tool can both enable sophisticated phishing attacks and also help defend against them. The research focuses on a growing problem: cybercriminals using AI to create phishing content. This allows them to carry out attacks with unprecedented scale and sophistication. Utilizing ChatGPT's advanced linguistic analysis, we developed a Python-based automation tool that leverages ChatGPT's API to enhance phishing detection mechanisms and assess the model's effectiveness in identifying phishing attempts through both text and image analysis. Our findings highlight ChatGPT's potential in detecting sophisticated phishing attacks, alongside the challenges it faces in differentiating between legitimate and fraudulent communications absent of clear phishing indicators. The study proposes innovative strategies for leveraging ChatGPT's phishing simulation and detection capabilities, offering insights into future-proofing cybersecurity efforts against evolving AI technologies. Through a methodical evaluation of ChatGPT versions in detecting phishing content and detailed security analysis, this investigation contributes to the ongoing discourse on the responsible and ethical deployment of AI tools in cybersecurity, advocating for robust countermeasures to mitigate AI-augmented threats.

关键词： Ethics Analytical models image analysis Phishing Refining Information processing Linguistics Chatbots Artificial intelligence Resilience

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A Novel Full Reference-image Quality Assessment (FR-IQA) for Adaptive visual Perception Improvement 6

A Novel Full Reference-Image Quality Assessment (FR-IQA) for...

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6th International conference on Inventive Computation Technologies (ICICT)

作者： Narsaiah, D. Reddy, R. Surender Kokkula, Aruna Kumar, P. Anil Karthik, A. Lords Inst Engn & Technol Dept ECE Hyderabad India Matrusri Engn Coll Dept ECE Derabad India Malla Reddy Coll Engn Dept ECE Derabad India Inst Aeronaut Engn Dept ECE Derabad India

ISBN: (纸本)9781728185019

To introduce a new IQA method called SC-QI, this research work adapts a structural contrast index (SCI) that characterizes the perceptions of local and also the global visual features on behalf of different image characters through different varieties of structural distortion. This research work also attempts for the development of SC-QI visual dependability involvement and expand the refitted picture quality optimization (SC-QI) called SC-DM. For local image characteristics & different forms of distortion, several appearances recycled in computational IQA can almost not describe visual quality conditions [18]. The complexity of the texture is a quality that increases as the visibility of distortions, which increases due to the contrast effect in the texture of the background image. Selecting user-friendly methods for optimization depends on FR-IQA. Here, image quality is considered as an important aspect of image processing, so these approaches can certainly contribute towards enhancing the visual quality by reducing the image structural distortion. FR-IQA (SC-QI) methods (SC-DM) have directly or indirectly affected the role of contrast/structural data in image signals that have observed the pictorial feature. Conclusion operative geographies that are used to describe such contrast/structural data could therefore remain as a frequent problem in displaying. [5]

关键词： image Quality Assessment Texture Full-Reference Complexity

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Enhancement of image Resolution using RDB

Enhancement of Image Resolution using RDB

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2025 International conference on Intelligent Control, Computing and communications, IC3 2025

作者： Anu, P. Kalyani, N. Mohanapriya, D. Rekha, S. Vimala, S. School of Computing Thanjavur613402 India Sastra Deemed to be University Thanjavur613402 India Psg College of Arts & Science Department of Computer Science Coimbatore641014 India Department of Computer Applications Tirunelveli627011 India

ISBN: (纸本)9798331527495

By using hierarchical features, Convolutional Neural Networks (CNNs) which is a Deep Learning method have shown notable improvements in image restoration (IR) tasks. However, poor performance arises from many existing deep CNN-based IR models' inability to adequately use the hierarchical information included in low-resolution images. To address the problem of image resolution, this study uses the Residual Dense Network (RDN) technology. RDN seeks to improve how well it uses hierarchical features from every convolutional layer. To be more precise, we have extracted localized information using the Residual Dense Block (RDB) and dense convolutional layers. By connecting all the layers of the present RDB twice the state of the preceding RDB, RDB creates a contiguous memory system. We incorporated Local Feature Fusion (LFF) into Residual Dense Blocks to improve the feature extraction from historical and current local data. This improved the stability of training for larger networks. After getting dense local properties, we used Global Feature Fusion (GFF) to extract global hierarchical features. Through the use of RDN in image Super-Resolution (ISR), we have proven its efficacy. Our tests on real datasets show that RDN performs exceptionally well on ISR tasks in visual quality as well as quantitative measures. © 2025 ieee.

关键词： Convolutional neural networks

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