• 한국컴퓨터정보학회논문지
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• 제24권1호
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• pp.9-23
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• 2019

In this paper, we propose a layer structure of a pest image classifier model using CNN (Convolutional Neural Network) and background removal image processing algorithm for improving classification accuracy in order to build a smart monitoring system for pine wilt pest control. In this study, we have constructed and trained a CNN classifier model by collecting image data of pine wilt pest mediators, and experimented to verify the classification accuracy of the model and the effect of the proposed classification algorithm. Experimental results showed that the proposed method successfully detected and preprocessed the region of the object accurately for all the test images, resulting in showing classification accuracy of about 98.91%. This study shows that the layer structure of the proposed CNN classifier model classified the targeted pest image effectively in various environments. In the field test using the Smart Trap for capturing the pine wilt pest mediators, the proposed classification algorithm is effective in the real environment, showing a classification accuracy of 88.25%, which is improved by about 8.12% according to whether the image cropping preprocessing is performed. Ultimately, we will proceed with procedures to apply the techniques and verify the functionality to field tests on various sites.

• 스마트미디어저널
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• 제2권2호
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• pp.33-38
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• 2013

본 논문에서는 급성 알코올성 간 손상과 만성 에탄올성 간섬유증이 유도된 마우스로부터 획득한 라만 스펙트럼에서 배경 잡음을 제거하기 위한 기준선 추정 방법을 조사하였다. 기준선을 추정하기 위해 일차 미분, 선형계획법, rolling ball을 이용한 방법을 적용하였다. 각 방법의 적절한 압력 파라미터를 MAP(maximum a posteriori probability)의 훈련율에 의해 결정하였다. 실험 절과에 따르면 rolling ball 알고리즘을 이용한 기준선 추정 방법이 급성 알코올성 간 손상과 만성 에탄올성 간섬유증의 MAP 분류에서 평균 89.4%로 가장 좋은 결과를 나타냈다. 이 결과로부터 라만 스펙트럼의 기준선 추정에 적절한 방법과 파라미터를 결정하는 것이 분류 성능에 미치는 영향을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.162.4-162
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• 2001

This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.42.1-42
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• 2001

This paper presents a fuzzy-based method for classification skin color object in a complex background under varying illumination. Parameters of fuzzy rule base are generated using a genetic algorithm(GA). The color model is used in the YCbCr color space. We propose a unique fuzzy system in order to accommodate varying background color and illumination condition. This fuzzy system approach to skin color classification is discussed along with an overview of YCbCr color space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1264-1286
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• 2018

In this paper, a novel background prior-based salient object detection framework is proposed to deal with images those are more complicated. We take the superpixels located in four borders into consideration and exploit a mechanism based on image boundary information to remove the foreground noises, which are used to form the background prior. Afterward, an initial foreground prior is obtained by selecting superpixels that are the most dissimilar to the background prior. To determine the regions of foreground and background based on the prior of them, a threshold is needed in this process. According to a fixed threshold, the remaining superpixels are iteratively assigned based on their proximity to the foreground or background prior. As the threshold changes, different foreground priors generate multiple different partitions that are assigned a likelihood of being foreground. Last, all segments are combined into a saliency map based on the idea of similarity voting. Experiments on five benchmark databases demonstrate the proposed method performs well when it compares with the state-of-the-art methods in terms of accuracy and robustness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4092-4107
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• 2015

We propose a background subtraction method for moving cameras based on trajectory classification, image segmentation and label inference. In the trajectory classification process, PCA-based outlier detection strategy is used to remove the outliers in the foreground trajectories. Combining optical flow trajectory with watershed algorithm, we propose a trajectory-controlled watershed segmentation algorithm which effectively improves the edge-preserving performance and prevents the over-smooth problem. Finally, label inference based on Markov Random field is conducted for labeling the unlabeled pixels. Experimental results on the motionseg database demonstrate the promising performance of the proposed approach compared with other competing methods.

• 한국도서관정보학회지
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• 제41권4호
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• pp.151-170
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• 2010

도서관 분류작업의 의미와 실효성에 대한 의문이 제기되면서, 도서관계에서는 이용자 중심적 분류 또는 독자의 관심을 바탕으로 하는 분류에 대한 관심이 크게 증가하고 있다. 북미 공공도서관계에서는 bookstore model이라 불리는 장르 분류의 적용을 통해 업무의 효율은 물론이고 자료에 대한 도서관 이용자들의 접근성을 향상시킴으로써 결과적으로 도서관 이용율과 서비스 만족도의 증대에 기여하고 있다. 이 연구에서는 북미 공공도서관에서의 장르 분류의 적용양상과 그 과정에서 발견되는 특징을 살펴봄으로써 우리 도서관계에서 진행되는 분류업무의 의미와 기본 방향에 대해 진지하게 성찰해 보았다. 연구에 필요한 데이터는 문헌조사와 북미 공공도서관 실무자와의 면담 또는 서신 교환을 통해 수집하였다.

• 한국도서관정보학회지
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• 제37권1호
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• pp.329-352
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• 2006

이 연구는 우리나라 근대문헌분류사의 중요한 분류표의 하나인 한은도서분류법의 편찬 및 개정 경위를 살펴보고 편찬당시 참조한 각종 분류표와 이를 비교하고 분류표 자체를 구체적으로 분석하였다. 한은도서분류법은 한국은행정보자료실에서 사용할 목적으로 초판을 간행하였고, 이후 한차례 수정판을 간행하였다. 그리고 편찬 당시 주요 주류와 조기표에서 NDC를 참조한 것으로 나타났으며, 종교와 어학, 문학에서는 KDCP를 참조한 것으로 나타났다.

• 전기학회논문지
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• 제65권3호
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• pp.477-486
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• 2016

This paper presents the area classification, identification, and tracking for multiple moving objects with the similar colors. To do this, first, we use the GMM(Gaussian Mixture Model)-based background modeling method to detect the moving objects. Second, we propose the use of the binary and morphology of image in order to eliminate the shadow and noise in case of detection of the moving object. Third, we recognize ROI(region of interest) of the moving object through labeling method. And, we propose the area classification method to remove the background from the detected moving objects and the novel method for identifying the classified moving area. Also, we propose the method for tracking the identified moving object using Kalman filter. To the end, we propose the effective tracking method when detecting the multiple objects with the similar colors. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1707-1710
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• 2003

In this paper, we propose a method of decision on blurring for business card images using block classification. In the proposed method, an input image is partitioned into 8${\times}$8 blocks and each block is classified into character block or background block using a block energy calculated in DCT domain. Whether the input image is blurring or non-blurring is determined using a ratio of low frequency energy and high frequency energy in DCT domain. Experimental results show that the proposed block classification classifies block well and the proposed decision on blurring decides well for various business card images.