Hardware Design and Implementation of a Parallel Processor for High-Performance Multimedia Processing

Kim, Yongmin; Hwang, Chul-Hee; Kim, Cheol-Hong; Kim, Jong‐Myon

doi:10.9708/jksci.2011.16.5.001

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Due to the similarity between fire and non-fire features, it is difficult to ensure linear separation. Thus, to classify fire from the candidate moving region, we use the non-linear SVM with Gaussian radial basis kernel function which performs better than other kernels [7]:…”

Section: Proposed Fire Detection Approachmentioning

confidence: 99%

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Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire

Nguyen¹,

Kang²,

Kim³

et al. 2013

Journal of the Korea Society of Computer and Information

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This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for

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Section: Proposed Fire Detection Approachmentioning

confidence: 99%

“…For instance, Borges and others analyzed frames to extract changing features of fires such as color, boundary, roughness and skewness for a Bayer classifier and made decisions about whether fires happened or not [4]. In addition, several researchers applied discrete wavelet transforms to extract features for classification [5][6][7].…”

mentioning

confidence: 99%

Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire

Nguyen¹,

Kang²,

Kim³

et al. 2013

Journal of the Korea Society of Computer and Information

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Parallel Implementation and Performance Evaluation of the SIFT Algorithm Using a Many-Core Processor

Son¹,

Kim²,

Jun³

2013

Journal of the Korea Society of Computer and Information

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In this paper, we implement the SIFT(Scale-Invariant Feature Transform) algorithm for feature point extraction using a many-core processor, and analyze the performance, area efficiency, and system area efficiency of the many-core processor. In addition, we demonstrate the potential of the proposed many-core processor by comparing the performance of the many-core processor with that of high-performance CPU and GPU(Graphics Processing Unit). Experimental results indicate that the accuracy result of the SIFT algorithm using the many-core processor was same as that of OpenCV. In addition, the many-core processor outperforms CPU and GPU in terms of execution time. Moreover, this paper proposed an optimal model of the SIFT algorithm on the many-core

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Hardware Design and Implementation of a Parallel Processor for High-Performance Multimedia Processing

Cited by 2 publications

References 12 publications

Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire

Fire Detection Approach using Robust Moving-Region Detection and Effective Texture Features of Fire

Parallel Implementation and Performance Evaluation of the SIFT Algorithm Using a Many-Core Processor

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