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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jaehong Yoon Heegwang Kim Chanyeong Park Junbo Jang Jiyoon Lee Joonki Paik Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Image Processing and Intelligent System Laboratory Graduate School of Artificial Intelligence Chung-Ang University Seoul Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Object detection with thermal infrared images is vital for applications like autonomous driving in low-light and adverse environmental conditions. While TIRDet has improved thermal-only object detection through a Thermal-To-Visible (T2V) translation model, this module can suffer from information loss in certain scenarios. To address this, we propose enhancing the CSPDarknet53 backbone of YOLOX within TIRDet by incorporating SimAM, a parameter-free attention mechanism. SimAM enhances feature representation by inferring 3D attention weights based on an energy function inspired by neuroscience theories, without increasing model parameters. By strengthening feature extraction within the backbone, our approach compensates for the T2V module's information loss, thereby improving detection performance under challenging conditions. Experiments on the LLVIP and FLIR datasets demonstrate superior performance, underscoring the effectiveness of parameter-free attention mechanisms for advancing thermal object detection.

关键词： Solid modeling Translation Attention mechanisms Three-dimensional displays Neuroscience Object detection Feature extraction Text to video Autonomous vehicles Consumer electronics

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A Comprehensive Privacy Protection Solution: Enhancing Face Obscuring Techniques with Automated Identification, Mobile Integration, and Scalable Services

A Comprehensive Privacy Protection Solution: Enhancing Face ...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Ilhwan Kim Hyeongseok Oh Sangwoo Yun Heunseung Lim Jaeseok Ryu Joonki Paik Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Image Processing and Intelligent System Laboratory Graduate School of Artificial Intelligence Chung-Ang University Seoul Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

We propose a novel application aimed at protecting personal privacy. With the proliferation of various media platforms, issues related to personal portraits have emerged. To address these problems, previous applications propose methods for either Mosaic or replace it with another object. However, existing methods suffer from issues such as high time costs and unnatural rendering. To address these problems, we propose designing an automatic face identification and replacement system by combining face detection, super-resolution, and face replacement models. This approach aims to reduce processing time and enhance natural rendering. In addition to the technological advancements, we suggest expanding this system into a mobile service, allowing users to easily protect their privacy on the go. This mobile platform would provide a user-friendly interface and seamless integration with various social media and communication apps, making it accessible to a broader audience. Furthermore, we explore potential expansion opportunities, such as cloud-based processing for scalability and the development of APIs that other developers can integrate into their own applications, thereby broadening the service's reach and impact. We demonstrate that our approach is simpler and more natural compared to traditional handcrafted tasks by applying it to diverse scenarios.

关键词： Privacy Accuracy Scalability Superresolution Media Rendering (computer graphics) Face detection Protection Usability Faces

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ViSeNet: A Visual-Semantic Fusion Network for Enhancing Few-Shot Classification

ViSeNet: A Visual-Semantic Fusion Network for Enhancing Few-...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Dongheon Lee Sangwoo Yun Joonki Paik Image Processing and Intelligent & System Laboratory Graduate School of Artificial Intelligence Chung-Ang University Seoul Korea Image Processing and Intelligent & System Laboratory Graduate School of Advanced & Imaging Science Multimedia and Film Chung-Ang University Seoul Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Few-shot learning addresses the challenge of recognizing novel classes with only a few labeled examples, a critical issue in limited data scenarios. This paper proposes a multi-modal Visual and Semantic Network (ViSeNet), which enhances few-shot classification by mining linguistic features and fusing weighted vectors. ViSeNet generates enriched class prototypes by leveraging both visual and semantic information. The linguistic feature mining process utilizes image-to-text models to produce class-specific textual descriptions, which are then transformed into representative semantic embeddings. For visual feature extraction, we employ a pretrained Swin Transformer, while text embeddings are integrated for each class using weighted factors. By combining these modalities, ViSeNet constructs class prototypes that capture both visual and semantic characteristics. Experimental results on benchmark datasets, particularly CIFAR-FS, demonstrate that ViSeNet achieves performance comparable to state-of-the-art approaches.

关键词： Visualization Semantics Prototypes Linguistics Transformers Feature extraction Vectors Data mining Few shot learning Consumer electronics

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Cone-Beam Computed Tomography Limited-Angle image Reconstruction Using Planning CT-Guided Deep Diffusion Model

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IEEE Transactions on Instrumentation and Measurement 2025年

作者： Wu, Zhan Yang, Yang Hu, Dianlin Lyu, Tianling Li, Yinsheng Tang, Hao Shu, Huazhong Xi, Yan Zhang, Yi Chen, Yang Southeast University Laboratory of Image Science and Technology Nanjing 210096 China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing 210096 China Zhejiang University of Technology Academy for Advanced Interdisciplinary Science and Technology Hangzhou China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System China Capital Medical University Beijing Jishuitan Hospital China First-Imaging Tech Shanghai China Sichuan University School of Cyber Science and Engineering Chengdu China Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Laboratory of Image Science and Technology the School of Computer Science and Engineering Nanjing 210096 China

For existing image-guided radiation therapy systems (IGRT), the limited angular range of the cone-beam computed tomography (CBCT) scanning improves the compactness of the system and compatibility between CBCT and linear accelerator configurations. However, the limited-angle CBCT reconstruction under incomplete projection data causes severe artifacts in the reconstructed images, further having a negative impact on the registration accuracy in IGRT. In this paper, we propose a planning CT-guided deep diffusion model, termed LARGE-DiffNet, to improve imaging quality and registration accuracy for limited-angle CBCT image reconstruction. In stark contrast to existing approaches leveraging hand-crafted regularization, such as local smoothness and/or sparsity of the image, LARGE-DiffNet learns the distribution-level prior knowledge to describe the features of ideal CBCT images via a planning CT-guided diffusion model. The proposed LARGE-DiffNet is evaluated on the clinical CBCT dataset, and extensive experimental results demonstrate that the proposed method outperforms the state-of-the-art methods. The proposed LARGE-DiffNet technique has great potential in IGRT. © 1963-2012 IEEE.

关键词： cone-beam computed tomography deep learning diffusion model image registration image-guided radiation therapy limited-angle

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Smoke detection based on fire-starting regions

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IEIE Transactions on Smart processing and Computing 2019年第6期8卷 475-481页

作者： Lee, Jaemin Park, Seokmok Son, Changeui Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul06974 Korea Republic of

Smoke detection is very important for fire detection, because smoke usually occurs before a fire breaks out. Smoke has a variety of characteristics, such as color, shape, texture, and so on. Using these characteristics, there has been a lot of research on smoke detection based on image processing. However, smoke detection is still difficult, because these characteristics can change due to environmental factors, such as wind and lighting changes. So, in this paper, a new smoke detection method based on fire-starting regions is presented to reduce sensitivity to environmental factors. The proposed method consists of three main parts: i) generating a candidate smoke region, ii) fire starting-region detection, and iii) detecting smoke based on fire-starting regions and predicting the direction of smoke movement. As a result, the proposed smoke detection method is less sensitive to the motion of non-smoke objects (humans, shadows, vehicles, etc.) and can detect smoke quickly by determining the fire-starting region where the smoke is generated. The proposed detection method can be applied to a video surveillance system and a fire- and smoke-analysis system. Copyrights © 2019 The Institute of Electronics and Information Engineers

关键词： Smoke

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Improved deeplab v3+ with metadata extraction for small object detection in intelligent visual surveillance systems

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IEIE Transactions on Smart processing and Computing 2021年第3期10卷 209-218页

作者： Oh, Heungmin Lee, Minjung Kim, Hyungtae Paik, Joonki Department of Image Engineering Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University / Seoul06974 Korea Republic of

A surveillance system deploys multiple cameras to monitor a wide area in real time to detect abnormal situations such as a crime scene, traffic accident, and natural disaster. An Increased number of cameras results in the same number of monitors, which makes human decisions or automatic decisions difficult. To solve the problem, a smart surveillance scheme has recently been proposed. The smart surveillance system automatically detects an object and provides an alarm to a surveillant. In this paper, we present a metadata extraction method for object-based video summary. The proposed method adopts deep learning-based object detection and background elimination to correctly estimate an object region. Finally, metadata extraction is performed on the estimated object information. The proposed metadata consists of the representative color, size, aspect ratio, and patch of an object. The proposed method can extract reliable metadata without motion features in both static and dynamic cameras. The proposed method can be applied to various object detection areas using complex metadata. Copyrights © 2021 The Institute of Electronics and Information Engineers

关键词： Object detection

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Computational imaging system with real-time controllable color coded aperture using an LCD

Computational imaging system with real-time controllable col...

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Digital Holography and Three-Dimensional imaging, DH 2016

作者： Park, Sangwoo Jang, Jinbeum Paik, Joonki Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781943580156

This paper presents a novel computational camera system that has a variable aperture using a thin-film-transistor liquid crystal display. The proposed system can electronically change the geometric shape and color of the aperture and generate the best aperture for a specific application. Experimental results show that the dual off-axis color-filtered aperture provides the most appropriate geometric shape to estimate the distance map. © OSA 2016.

关键词： Color

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image fusion using asymmetric dual camera for digital zooming

Image fusion using asymmetric dual camera for digital zoomin...

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imaging systems and Applications, IS 2016

作者： Jo, Jieun Jang, Jinbeum Paik, Joonki Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul Korea Republic of

This paper presents a method for estimating a seam to fuse two images acquired by asymmetric dual cameras that have different field of views. This method consist of an optimization-based active contour algorithm and m... 详细信息

ISBN: (纸本)9781943580156

关键词： image fusion

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Efficient Pavement Crack Detection in Drone images using Deep Neural Networks

Efficient Pavement Crack Detection in Drone Images using Dee...

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2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

作者： Kim, Heegwang Sung, Junghoon Kim, Mingi Park, Chanyeong Paik, Joonki Chung-Ang University Image Processing and Intelligent System Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Korea Republic of Chung-Ang University Image Processing and Intelligent System Laboratory Graduate School of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350320213

Since the drone input images are usually filmed at very high altitudes and with various viewing angles, cracks appear in many different sizes and shapes. A commonly used crack detection dataset has a small size such as 480x320, and it is important that how well it detects the prominent continuous cracks. To detect pavement cracks, we proposed an efficient crack detection network. The proposed network achieved better performance than the conventional network. © 2023 IEEE.

关键词： crack detection sigmoid fusion module YOLOX

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Robust kernelized correlation filter using adaptive feature weight

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IEIE Transactions on Smart processing and Computing 2018年第6期7卷 433-439页

作者： Kim, Yeongbin Park, Hasil Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of Department of Integrative Engineering Chung-Ang University Seoul Korea Republic of

In this paper, we propose a robust tracking method for fast motion and background noise via improved kernelized correlation filters (KCFs). The proposed tracking algorithm consists of four steps: i) generate a Gaussian blurred image of the input image, ii) compute each correlation response and weight update score, iii) compute adaptive feature weight using a weight update score, and iv) integrate features using the adaptive feature weight, and train a correlation filter using the integrated features. As a result, the proposed tracking method can robustly track fast motion and background noise. The proposed tracking algorithm can be applied to video surveillance systems and intelligent vehicles. © 2018 Institute of Electronics and Information Engineers. All Rights Reserved.

关键词： Security systems

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