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Smoke detection based on fire-starting regions

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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A fully digital auto-focusing system using a priori point spread function dataset

A fully digital auto-focusing system using a priori point sp...

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2010 International Conference on Consumer Electronics, ICCE 2010

作者： Park, Younguk Jeon, Jaehwan Lee, Jinhee Paik, Joonki Image Processing and Intelligent Systems Laboratory Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781424443161

This paper presents a fully digital auto-focusing (FDAF) system using a priori obtained point spread function (PSF) dataset. The proposed FDAF algorithm aims at low-cost, affordable consumer's imaging electronics free from either optical or mechanical focusing parts. The proposed algorithm consists of three steps: i) a priori estimation of a set of PSFs, ii) automatic selection of a proper PSF from the blurred input image, and iii) restoration the out-of-focused image by selecting the optimal PSF from the a priori obtained dataset. The proposed algorithm can realize low-cost, high-quality imaging systems, such as digital still cameras, mobile camera phones, and surveillance cameras. ©2010 IEEE.

关键词： Optical transfer function

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Edge based adaptive Kalman filtering for real-time video stabilization

Edge based adaptive Kalman filtering for real-time video sta...

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International Conference on Consumer Electronics, ICCE '06

作者： Kwon, Ohyun Shin, Jeongho Paik, Joonki Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)0780394593

image stabilization, particularly during zoom-in is an important feature in consumer cameras. Existing methods to stabilize an image often make an incorrect motion vector by an improper choice of motion estimation or correction method. The proposed algorithm is simple and effective;it is suitable for low cost camcorders, digital cameras, CCTV, surveillance video systems, and television broadcasting systems. © 2006 IEEE.

关键词： Kalman filtering

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Weighted image defogging method using statistical RGB channel feature extraction

Weighted image defogging method using statistical RGB channe...

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2010 International SoC Design Conference, ISOCC 2010

作者： Yoon, Inhye Jeon, Jaehwan Lee, Jinhee Paik, Joonki Department of Image Processing Intelligent Systems Laboratory Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781424486335

In this paper, we present a weighted adaptive image defogging method by extracting features in the RGB color channels. We adaptively detect an atmospheric light through undesired fog in the dark channel prior obtained in the YCbCr color channels and generate a transmission map based on the detected atmospheric light. We adaptively remove the fog by applying the color correction algorithm based on the feature extraction in the RGB color channels. The proposed algorithm can overcome the problem of local color distortion, which is known to be the limitations of existing defogging techniques. Experimental results demonstrate that the proposed algorithm can remove image degradation caused by fog, clouds, smoke, and dust in digital imaging devices. ©2010 IEEE.

关键词： Fog

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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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Variational low-light image enhancement based on a haze model

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IEIE Transactions on Smart processing and Computing 2018年第4期7卷 325-331页

作者： Shin, Joongchol Park, Hasil Park, Jinho Ha, Jinsol 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

Under low-illumination conditions, an acquired image is degraded by a limited dynamic range and noise in signal amplification. To solve this problem, we propose a haze model-based variational low-light image-enhancement method. The proposed method includes two steps: i) estimation of the initial transmission map using block-based dark channel prior and a Gaussian pyramid, and ii) L2-norm regularized optimization based on the haze model. Experimental results show that the proposed enhancement method outperforms conventional state-of-the-art methods in terms of visible contrast without noise amplification. Copyrights © 2018 The Institute of Electronics and Information Engineers.

关键词： image enhancement

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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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Imaging inverse problem using sparse representation with adaptive dictionary learning 5

Imaging inverse problem using sparse representation with ada...

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2015 5th IEEE International Advance Computing Conference, IACC 2015

作者： Mittu George, P. Vivek, M. Paik, Joonki Oxford College of Engineering Bangalore India Image Processing and Intelligent Systems Laboratory Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781479980475

Sparse representation is one of the powerful emerging statistical techniques for modeling images. This representation approximates the image as a combination of the codewords within a dictionary which is over complete. Recent years have seen a tremendous growth in the field of sparse representation. Here a method is proposed for image inversing by using iterative deblurring based on sparse representation of an image which is uniformly blurred. The key idea behind this methodology is the sparseness of natural images in some domain. The quality of recovered image majorly depends on the domain or the dictionaries that are used to represent it. In this paper, the K-SVD algorithm is used to train thegroup of codewords from a set of quality natural image patches. For each of the local patch within the blurred image, the best suited sub-dictionary from the trained dictionary data base is selected. In addition, a smoothness regularization constraint is added that prevents the reblurring of the image edges. For numerical stability the sparsity weight is adaptively computed which also improves the reconstructed image quality. Comparative study on some of the existing restoration algorithm proves the proposed method outperforms them all. © 2015 IEEE.

关键词： image reconstruction

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An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse

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Journal of intelligent Manufacturing 2016年第2期27卷 389-408页

作者： Chen, Fangyu Wang, Hongwei Xie, Yong Qi, Chao Key Laboratory of Image Processing and Intelligent Control of Education Ministry Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China

One of the challenging problems in order picking is how to deal with the congestion happens in warehouse with multiple pickers. In this paper, we consider an ant colony optimization (ACO)-based online routing method to find picking routes for multiple order pickers under nondeterministic picking time. Here, a default route is formed by ACO for each single picker. Then, we coordinate these routes to alleviate congestion by dedicated rules based on indoor positioning and information sharing technologies, during order pickers serve the picking task. Our results indicate that the proposed method can achieve a reduction in the order service time primarily by coping with the congestion. We conclude that the new method is particularly effective in multiple-block picker-to-parts warehouses. © 2014, Springer Science+Business Media New York.

关键词： Ant colony optimization Congestion Multiple order pickers Routing method Warehouse management

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Optimization of H.264 encoder using adaptive mode decision and SIMD instructions

Optimization of H.264 encoder using adaptive mode decision a...

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2005 Digest of Technical Papers - International Conference on Consumer Electronics, ICCE 2005

作者： Kim, Yong-Hwan Yoo, Jin-Woo Lee, Seong-Won Paik, Joonki Choi, Byeongho Image Processing and Intelligent Systems Laboratory Department of Image Engineering Chung-Ang University Seoul Korea Republic of Digital Media Research Center Korea Electronics Technology Institute

This paper presents algorithms that optimize the baseline H.264 encoder without loss of image quality and compression ratio. We propose an adaptive mode decision algorithm to speedup complex mode decision with rate distortion optimization (RDO). Also we optimize computation-intensive modules using single instruction multiple data (SIMD) instructions. As a result, the proposed encoder is ten times faster than JM72 reference encoder. ©2005 IEEE.

关键词： image coding

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