• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.621-629
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• 2016

This paper presents a decision method of middle ear disease which is developed in children and adults. In the proposed method, features are extracted from the middle ear disease images and normal images using HoG (histogram of oriented gradient) descriptor and the extracted features are learned by SVM (support vector machine) classifier. To obtain an input vector into SVM, an input image is resized to a predefined size and then the resized image is partitioned into 16 blocks each of which is partitioned into 4 sub-blocks (namely cell). Finally, the feature vector with 576 components is given by using HoG with 9 bins and it is used as SVM learning and classification. Input images are classified by SVM classifier based on the model of learning features. Experimental results show that the proposed method yields the precision of over 90% in decision.

• Journal of Digital Contents Society
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• v.10 no.4
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• pp.597-604
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• 2009

For the improvement of license plate recognition system, correct extraction of a license plate region as well as character recognition is important. In this paper, with the analysis and classification of the error patterns in the process of plate region extraction, we tried to improve the extraction of the region using HoG(histogram of gradient) features and Adaboost. The results show that the HoG feature is robust to the noise and various types of the plates, and also is very effective to extract the region failed before.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.385-392
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• 2016

Market analysis for a business plain is required for the success in the modern world. Most important part in this analysis is pedestrian traffic counting. A traditional way for this is counting it in person. However, it causes high labor costs and mistakes. This paper proposes an automatic algorithm to measure the pedestrian traffic count using images with webcam. The proposed algorithm is composed of two parts: pedestrian area detection and movement tracking. In pedestrian area detection, moving blobs are extracted and pedestrian areas are detected using HoG features and Adaboost algorithm. In movement tracking, multi-level matching and false positive removal are applied to track pedestrian areas and count the pedestrian traffic. Multi-level matching is composed of 3 steps: (1) the similarity calculation between HoG area, (2) the similarity calculation of the estimated position with Kalman filtering, and (3) the similarity calculation of moving blobs in the pedestrian area detection. False positive removal is to remove invalid pedestrian area. To analyze the performance of the proposed algorithm, a comparison is performed with the previous human area detection and tracking algorithm. The proposed algorithm achieves 83.6% accuracy in the pedestrian traffic counting, which is better than the previous algorithm over 11%.

• Journal of Internet Computing and Services
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• v.16 no.3
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• pp.21-32
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• 2015

Since spatio-temporal local features for video representation have become an important issue of modeless bottom-up approaches in action recognition, various methods for feature extraction and description have been proposed in many papers. In particular, BoF(bag of features) has been promised coherent recognition results. The most important part for BoF is how to represent dynamic information of actions in videos. Most of existing BoF methods consider the video as a spatio-temporal volume and describe neighboring 3D interest points as complex volumetric patches. To simplify these complex 3D methods, this paper proposes a novel method that builds BoF representation as a way to learn 2D interest points directly from video data. The basic idea of proposed method is to gather feature points not only from 2D xy spatial planes of traditional frames, but from the 2D time axis called spatio-temporal frame as well. Such spatial-temporal features are able to capture dynamic information from the action videos and are well-suited to recognize human actions without need of 3D extensions for the feature descriptors. The spatio-temporal BoF approach using SIFT and SURF feature descriptors obtains good recognition rates on a well-known actions recognition dataset. Compared with more sophisticated scheme of 3D based HoG/HoF descriptors, proposed method is easier to compute and simpler to understand.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1357-1358
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• 2015

본 논문에서는 축사에서 비젼 기반으로 이동 객체를 분류하는 방법을 제안한다. 제안하는 방법은 축사 내 설치된 CCTV로부터 영상을 입력받아 Adaptive GMM알고리즘을 이용하여 이동 객체를 추출한다. 다음, 이동 객체 가 사람인지 소인지 또는 차량인지 분류하기 위해 이동 객체의 특징을 추출한다. 이동 객체 특징 추출 방법으로는 기존의 Monolithic-based방법인 HoG알고리즘을 개선하여 축사의 복잡한 환경에서 다양한 자세를 가지는 사람과 소 그리고 차량의 구조적 특징을 추출한다. 추출한 특징은 벡터화 하여 SVM분류기 입력값에 적합하도록 한다. SVM 분류를 통해 이동 객체의 구조적 특징을 블록화 하여 이동 객체의 신체 모델을 생성한다. 마지막으로 생성된 신체 모델을 이용하여 이동 객체가 사람인지 소인지 또는 차량인지 분류한다.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.2
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• pp.81-88
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• 2020

Pleurotus pulmonarius 8 cultivas, domestically bred, were cultivated and evaluated for cultural properties. The morphological properties of 8 cultivars were big diameter of pileus and short stipe. Stipe was eccentrically combined with pileus. The longest length of stipe was Yeoleumneutali 2ho(72.8mm) while shortest cultivar was Yeoleumneutali 1ho(33.8mm). The diameter of pileus ranged 45.8-62,3mm, Yaksan was the smallest one and Yeoleumneutali 2ho was largest cultivar. Yield was largest in Yeoleumneutali 2ho(80.7g) whereas smallest was in Yeoleumneutali 1ho(35.5g). The highest L value was in Yeoleumneutali 2ho and Hosan. Lightness was not even around pileus but thicker in rim area and thinner in center of pileus. Average period of pinheading and harvest were 4.6 and 8.0 days thus only 3-4 days after pinheadng were required for harvest.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.189-192
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• 2006

본 논문은 그래픽 가속기를 포함한 모바일 시스템에서 멀티미디어 응용을 위한 통합전력관리 기법을 제안한다. 가속기가 포함된 시스템이 멀티미디어 프로세스를 실행할 때 QoS를 유지하면서 에너지 절약을 하기 위해서는 가속기의 특징을 고려한 DVS 알고리즘이 필요하다. 그러나 기존 DVS 알고리즘은 CPU 위주로 연구된 알고리즘이여서 가속기가 포함된 시스템에 적용하는 것은 문제가 있기 때문에 CPU와 가속기의 특징을 고려한 통합전력관리 DVS 방법이 필요하다. 제안된 DVS 스케줄링은 리눅스 운영체제 상에 구현하였으며 Intel 2700G 그래픽 가속기가 포함된 Xscale 장치에서 실험을 하였다. 따라서 제안된 DVS 기법이 범용적인 프로세스의 QoS를 보장하면서 에너지 소비를 CPU위주로 연구된 알고리즘보다 평균 12.5% 줄일 수 있음을 밝혔다.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Korean Journal of Environmental Agriculture
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• v.19 no.4
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• pp.364-369
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• 2000

In order to obtain the reference data to be used as an vegetation index for degree of desalinization, vegetation successions were surveyed and soil chemical characteristics were analyzed at the naturally maintained plot in Dae-Ho reclaimed land. Seven Groups $(A\;{\sim}\;G\;group)$ were classified as major vegetation; A group (Suaeda asparagoides MAKINO etc.), B group ( Aster tripolium L. etc.), C group (Stearia viridis L. BEAUV), D group ( Phragmites longivalvis STEUD), E group (Limonium tettagonum THUMB A. A. BULLOCK), F group (Trifolium repens L.), G group (Zoysia sinca HANCE etc.). As desalinization process proceeded, the wild vegetation changed in order of $A\;{\to}\;B\;group$, $D\;{\to}\;C\;group$, $E\;{\to}\;F$, G group. Soil texture of the naturally maintained plot was silt loam and soil fertility was very low compared with agricultural cultivated soil. Soil pH ranged from 7.0 to 8.0. Electrical conductivity (ECe) was below 10 and $20\;dS{\cdot}m^{-1}$ at top and subsoil, respectively, except the plot where A group were growing. Resulting from SAR and ECe, The plot where A group was growing was saline-sodic soil and the others were saline soil. The relation between vegetation sucession and soil desalinization showed that vegation appeared under $10\;dS{\cdot}m^{-1}$ of ECe and 15 of SAR except A group.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.838-840
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• 2005

본 논문에서는 감시 시스템 내에서 검출된 객체에 대해 정확한 특징벡터를 추출하기 위한 그림자 제거(shadow casting)방법을 제안한다. 그림자에 외해 부정확한 특징벡터를 가지게 되는 객체는 동일한 객체임에도 불구하고 서로 다른 객체로 인식하는 잘못된 결과를 가져온다. 이러한 문제점을 해결하기 위해 객체가 가지는 대칭성을 사용하여 그림자 후보 영역을 추출한 후 중심축으로부터의 거리에 비례한 가중치값을 사용하여, 추출한 영역에 대해 그림자를 제거를 수행한다.