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检索条件"任意字段=IEEE Visual Communications and Image Processing Conference"

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A Novel Face Inpainting Approach Based on Guided Deep Learning 4

A Novel Face Inpainting Approach Based on Guided Deep Learni...

引用

4th International conference on communications, Signal processing, and their Applications, ICCSPA 2020

作者： Salem, Nermin M. Mahdi, Hani M.K. Abbas, Hazem M. Electrical Engineering Department Future University in Egypt Cairo Egypt Computer and Systems Engineering Department Ain Shams University Cairo Egypt

ISBN: (纸本)9781728165356

In the last few years, deep learning has shown significant improvement for many computer vision open problems, especially image inpainting. image inpainting is the process of filling missing regions across images. One of the most challenging problems in image inpainting is face inpainting. In this work, a new novel approach for face inpainting is proposed which can capture and preserve the identity of each human face in images while reproducing the missing irregular region in images. A two-stage cascaded model is proposed. It is composed of a shape-predictor of the key-points of the face followed by an inpainting network. The shape-predictor identifies the human face's structure-preserving its local points, i.e. eyes, mouth, nose, and then the inpainting network fills any random-irregular missing regions guided by the obtained knowledge as a priori. The effectiveness of the proposed model was evaluated using the CelebA dataset. The obtained results from the trained model outperform the recently proposed technique with contextual attention. © 2020 ieee

关键词： image enhancement

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Synchronization for VLC Using Orthogonally Aligned Rolling Shutter Cameras and LED Array

Synchronization for VLC Using Orthogonally Aligned Rolling S...

引用

Asia-Pacific conference on communications

作者： Ayumu Otsuka Takaya Yamazato Hiraku Okada Toshiaki Fujii Koji Kamakura Masayuki Kinoshita Shintaro Arai Tomohiro Yendo Shan Lu Department of Information and Communication Engineering Nagoya University Furo-cho Chikusa-ku Nagoya Aichi Japan Chiba Institute of Technology 2-17-1 Tsudanuma Narashino Chiba Japan Okayama University of Science 1-1 Ridaicho Kita-ku Okayama Japan Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Japan

ISBN: (数字)9798331517816

ISBN: (纸本)9798331517823

This research addresses challenges in visible light communication (VLC) systems that use two orthogonally aligned rolling shutters (RS) image sensors as receivers and an LED array as a transmitter. The study focuses on overcoming burst signal loss caused by unsensed periods between frames in RS image sensors. To improve VLC performance, we propose and compare three different schemes. The first is a conventional approach that superimposes a Barker code synchronization signal on the transmission signal using pulse width modulation (PWM). The second introduces a new method of spatial synchronization by dedicating specific LEDs in an array for synchronization signals. The third scheme extends this spatial approach by incorporating a 4 -level PWM for information transmission to increase data rates. The study aims to evaluate and compare the error rate characteristics of these three schemes, assessing their effectiveness in mitigating burst errors and enhancing overall VLC system performance. This research has potential applications, including vehicle communication, Internet of Things devices, and smartphones.

关键词： image sensors Transmitters Receivers Information processing Pulse width modulation Light emitting diodes Parity check codes Synchronization Visible light communication Sensor arrays

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An Optimization Framework for image Contouring of Handwriting Recognition using Genetic Algorithm 13

An Optimization Framework for Image Contouring of Handwritin...

引用

2021 ieee 13th International conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2021

作者： DIaz, Julianne Alyson I. Munsayac, Francisco Emmanuel T. Bugtai, Nilo T. Baldovino, Renann G. Gokongwei College of Engineering De La Salle University Department of Electronics Engineering and Communications 2401 Taft Avenue Manila0922 Philippines Department of Maufacturing Engineering and Management Gokongwei College of Engineering De La Salle University 2401 Taft Avenue Manila0922 Philippines De La Salle University 2401 Taft Avenue Manila0922 Philippines

ISBN: (纸本)9781665401678

Handwriting recognition poses a huge challenge in image processing due to the lack of defined character construction written by different people. This paper proposes a new technique addressing the research gap in the manual tuning enhancement of classification of handwritten Arabic numerals through introduction of image contouring in the pre-processing stage and its optimization using genetic algorithm (GA). Moreover, the study utilized image contouring techniques in extracting features of the image. Furthermore, the image contouring process was assisted by GA to optimize the tunings of the contours to maximize the features extracted. The experiment utilized the MNIST dataset for the training and testing. The accuracy per character varies due to the number of variations of a given character. Total accuracy of 99% was obtained during the testing. © 2021 ieee.

关键词： Genetic algorithms

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Adaptive Structure-Aware Connectivity-Preserving Loss for Improved Road Segmentation in Remote Sensing images

Adaptive Structure-Aware Connectivity-Preserving Loss for Im...

引用

ieee Winter Applications and Computer Vision Workshops (WACVW)

作者： Sara Shojaei Trevor Bohl Kannappan Palaniappan Filiz Bunyak Department of Electrical Engineering and Computer Science University of Missouri-Columbia MO USA

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Accurate road extraction from remote sensing images is a critical step for many applications, including navigation, environmental monitoring, and disaster response. In remote sensing images, roads appear as thin curvilinear structures and are very prone to fragmentation due to challenges such as low-contrast materials and occlusions by surrounding vegetation or buildings. Although deep learning has greatly advanced image segmentation techniques, pixel-level loss functions are insufficient for preserving connectivity and topology of thin curvilinear structures such as roads. Small pixel-level errors can disrupt the extraction of accurate road graphs, complicating subsequent characterizations and analysis tasks. In this paper, we propose a novel, adaptive, structure-aware, connectivity-preserving loss function SAC-Loss. This loss function combines global and local processing to improve structure awareness and uses a proximity-based weighting scheme with asymmetric penalties to fill gaps in the road structures while limiting spurious detections elsewhere. Experimental results demonstrate improved road segmentation in terms of visual quality and quantitative performance.

关键词： image segmentation visualization Accuracy Limiting Network topology Roads Vegetation mapping Network analyzers Topology Remote sensing

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Multi-Scale Feature Fusion using Channel Transformers for Guided Thermal image Super Resolution

Multi-Scale Feature Fusion using Channel Transformers for Gu...

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

作者： Raghunath Sai Puttagunta Birendra Kathariya Zhu Li George York University of Missouri-Kansas City US Air Force Academy

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Thermal imaging, leveraging the infrared spectrum, offers a compelling alternative to visible spectrum (VIS) imagery in challenging environmental conditions like low- light, occlusions, and adverse weather. However, its widespread adoption in computer vision tasks is hampered by lower spatial resolution. We address this challenge by proposing a novel framework titled Multi-Scale Feature Fusion using Channel Transformers (MSFFCT) for Guided Thermal image Super-Resolution (GTISR).GTISR tackles the resolution limitations of thermal imagery. It leverages high-resolution RGB information as a guide to reconstruct high-resolution thermal imagery from low-resolution thermal inputs. At the core of MSFFCT lies a novel deep learning architecture that combines the strengths of two powerful approaches: channel-based transformers and multi-scale *** overcomes inherent limitations of Convolutional Neural Networks (CNNs) typically used in super-resolution tasks. CNNs often suffer from restricted receptive fields, limiting their ability to capture long-range dependencies within the image. Additionally, computational cost grows significantly with larger inputs. MSFFCT addresses these shortcomings by enabling efficient processing of global information and offering superior scalability. MSFFCT achieved state-of-the-art results on the ×8 and × 16 GTISR tasks of the 2024 Perception Beyond visual Spectrum (PBVS) challenge, winning 2 nd place in both tasks and demonstrating its effectiveness in real-world scenarios.

关键词： Computer vision visualization Limiting Superresolution Imaging Transformer cores Transformers

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Underwater image Restoration Based on An Improved Underwater image Formation Model

Underwater Image Restoration Based on An Improved Underwater...

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International conference on Wireless communications and Signal processing (WCSP)

作者： XinNan Fan Xuan Zhou PengFei Shi HuanRu Sun ShuYue Ye College of Internet of Things Engineering Hohai University Changzhou China

ISBN: (纸本)9781665450867

Underwater images often show characteristics of low contrast, low visibility and color distortion. To improve underwater image quality, a novel underwater image restoration algorithm is proposed in this paper. First of all, we put forward an underwater image restoration network (UIRnet) based on CNN(Convolutional Neural Network) for transmission estimation in the underwater imaging model. Besides, the Leaky ReLU(Rectified Linear Units) activation function and SENet(Squeeze-and-Excitation Networks) unit are used to improve the ability of extracting image features and training parameters. Secondly, the fuzzy patch dataset which is used as input dataset is synthesized by the underwater optical imaging model and randomly generated transmission. Meanwhile, the corresponding transmission value is used as label dataset. Then, we use the dataset to train and optimize all parameters in the UIRnet. Finally, the trained UIRnet is used in underwater image restoration. The experimental results show that the proposed algorithm can restore the details of the image, improve the contrast, and have a more natural visual effect.

关键词： Training Wireless communication image quality Neural networks Signal processing algorithms Network architecture Feature extraction

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Detection of human behavioral events combining perceptual causality 2

Detection of human behavioral events combining perceptual ca...

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2nd ieee Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2021

作者： Tian, Wei Lv, Jingyuan University of Jinan School of Electrical Engineering Jinan China Information and Communications Division Shandong Provincial Public Security Department Jinan China

ISBN: (纸本)9781728190181

In this paper, we propose a computational model of human behavior events analysis that integrates perceptual causality. Design and establish a causal rule representation method based on default logic, which provides a concise syntax and semantic formal tool for potential causality. Through the analysis of the internal structure of the event, the technical framework of causality with the smallest action unit as the entity is established, which makes the traditional different entities, objects, events, states and actions naturally interrelated. By establishing the perceptual causal relationship between human goals and objects, events, reasoning the intention of human goals, and predicting their behavior, it can effectively solve the problem of human action occlusion and action error detection. The effectiveness of this method is evaluated on a new video dataset. © 2021 ieee.

关键词： Semantics

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Transfer learning based multi-class malware classification using VGG16 feature extractor and SVM classifier

Transfer learning based multi-class malware classification u...

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International conference on communications and Signal processing

作者： Harman Saggu Er. Karandeep Singh T. N. Suresh Babu Department of Computer Science and Engineering Punjabi University Patiala India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Malicious programs are becoming a severe problem in today’s internet-connected world. Several works have explored image analysis-based malware detection in recent years. The image analysis method converts the malicious code into gray-scale images, which enables the use of deep learning models in the domain of malware detection. This paper proposes a new architecture using a fine-tuned visual Geometry Group(VGG16) model with a support vector machine (SVM) classifier to achieve high-performance accuracy in detecting and classifying evolving malware. At first, the malicious code is represented in gray-scale images, which are further reshaped and resized to be fed into a fine-tuned VGG16 model as end-to-end feature extraction with the least dependence on human-crafted feature engineering. The extracted textural feature maps are further correlated to remove the highly correlated features before using them to train a support vector machine (SVM) classifier. The experiments are performed on public bench-marked MalImg datasets and actual malware samples. The VGG 16+ SVM model obtained 99.25% classification accuracy for the MalImg and 80.29% for real-world malware, which signifies that the model can detect real-world instances of malware. The proposed method outperforms similar existing works and is suitable for deployment in an IoT environment due to the least cost involved in feature engineering.

关键词： Support vector machines Geometry Deep learning visualization image analysis Costs Transfer learning

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Guess What I Think: Streamlined EEG-to-image Generation with Latent Diffusion Models

Guess What I Think: Streamlined EEG-to-Image Generation with...

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

作者： Eleonora Lopez Luigi Sigillo Federica Colonnese Massimo Panella Danilo Comminiello Dept. Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. The code is available at https://***/LuigiSigillo/GWIT.

关键词： Neuroimaging Adaptation models visualization Streaming media Functional magnetic resonance imaging Diffusion models Brain modeling Electroencephalography Real-time systems Spatial resolution

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A Discriminative DeepLab Model (DDLM) for Surface Anomaly Detection and Localization

A Discriminative DeepLab Model (DDLM) for Surface Anomaly De...

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ieee Annual Computing and Communication Workshop and conference (CCWC)

作者： Nana Kankam Gyimah Kishor Datta Gupta Mahmoud Nabil Xuyang Yan Abenezer Girma Abdollah Homaifar Daniel Opoku Dept. of Electrical & Computer Eng North Carolina A&T State Univ Greensboro USA Dept. of Computer Science Clark Atlanta University Atlanta Georgia Dept. of Electrical & Computer Eng Kwame Nkrumah Univ of Science & Tech Kumasi Ghana

Recent reconstructive model-based visual anomaly detection (AD) methods have been shown to reconstruct the normal regions of the image but often struggle to detect the required anomalous deviations. These approaches are typically trained on images without anomalies and consequently require manual post-processing techniques to localize the anomalies. In this paper, we proposed a discriminative deeplab model that is trained discriminatively end-to-end on out-of-distribution simulated anomalies. Our proposed framework directly localizes anomalies without the need for complex post-processing via learning a combined representation of anomalous images and a decision boundary between anomalous and normal samples. The effectiveness of our DDLM is evaluated extensively on the MVTec AD dataset. Our results show that the DDLM framework outperformed state-of-the-art frameworks between 1.5% and 18.8% in AUROC on the anomaly detection challenge and between 20.2% and 22.9% with regards to the AP on the anomaly localization challenge.

关键词： Location awareness visualization conferences Computational modeling Manuals Anomaly detection image reconstruction

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