• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2212-2215
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• 2003

In this paper, we proposed speech feature vector extractor for embedded system using TMS 320C31 DSP chip. For this extractor, we used algorithm using cepstrum coefficient based on LPC(Linear Predictive Coding) that is reliable algorithm to be is widely used for speech recognition. This system extract the speech feature vector in real time, so is used the mobile system, such as cellular phones, PDA, electronic note, and so on, implemented speech recognition.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1293-1308
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• 2020

In fine-tuning-based transfer learning, the size of the dataset may affect learning accuracy. When a dataset scale is small, fine-tuning-based transfer-learning methods use high computing costs, similar to a large-scale dataset. We propose a mid-level feature extractor that retrains only the mid-level convolutional layers, resulting in increased efficiency and reduced computing costs. This mid-level feature extractor is likely to provide an effective alternative in training a small-scale medical image dataset. The performance of the mid-level feature extractor is compared with the performance of low- and high-level feature extractors, as well as the fine-tuning method. First, the mid-level feature extractor takes a shorter time to converge than other methods do. Second, it shows good accuracy in validation loss evaluation. Third, it obtains an area under the ROC curve (AUC) of 0.87 in an untrained test dataset that is very different from the training dataset. Fourth, it extracts more clear feature maps about shape and part of the chest in the X-ray than fine-tuning method.

• 로봇학회논문지
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• 제11권3호
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• pp.115-126
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• 2016

This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2018년도 춘계학술발표대회
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• pp.405-406
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• 2018

본 논문은 소표본 의료용 영상 분석의 정확도 향상을 위해 전이학습 모델을 feature extractor로 구축하여 학습시키는 방법을 연구하였으며 성능 평가를 위해 선학습모델로 AlexNet, ResNet, DenseNet을 사용하여 fine tuning 기법을 적용하였을 때와의 성능을 비교 분석하였다. 그 결과 실험에 사용된 3개의 모델에서 fine tuning 기법보다 향상된 정확도를 보임을 확인하였고, 또한 ImageNet으로 학습된 AlexNet, ResNet, DenseNet이 소표본 의료용 X-Ray 영상에 적용될 수 있음을 보였다.

• 한국멀티미디어학회논문지
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• 제22권2호
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• pp.203-209
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• 2019

The performance of an intelligent classifier for detecting malwares added to multimedia contents based on machine learning is highly dependent on the properties of feature set. Especially, in order to determine the malicious code in real time the size of feature set should be as short as possible without reducing the accuracy. In this paper, we introduce an optimal feature selection method to satisfy both high detection rate and the minimum length of feature set against the feature set provided by PEFeatureExtractor well known as a feature extraction tool. For the evaluation of the proposed method, we perform the experiments using Windows Portable Executables 32bits.

• 한국정밀공학회지
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• 제25권2호
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• pp.35-42
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• 2008

This study introduces a robust feature detector for sonar data from a general fixed-type of sonar ring. The detector is composed of a data association filter and a feature extractor. The data association filter removes false returns provided frequently from sonar sensors, and classifies set of data from various objects and robot positions into a group in which all the data are from the same object. The feature extractor calculates the geometries of the feature for the group. We show the possibility of extracting circle feature as well as a line and a point features. The proposed method was applied to a real home environment with a real robot.

• 전기전자학회논문지
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• 제24권1호
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• pp.200-207
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• 2020

본 논문에서는 보안 감시용 레이다 시스템을 위한 저복잡도 특징점 추출기를 제안하고, 이의 FPGA 기반 설계 결과를 제시하였다. 특징점 추출기의 메모리 요구량을 최소화하기 위해 레이다 스펙트로그램 전체에 대한 통계처리를 요구하는 프레임 단위의 특징점을 배제하고, 단위 도플러 프로파일에서 추출 가능한 특징점을 적용하였다. 제안된 특징점 추출기는 Verilog-HDL을 이용하여 RTL 설계 후, Xilinx Zynq-7000 FPGA를 활용하여 구현되었으며, 기존 연구대비 58.3%의 slice 및 98.3%의 메모리 요구량을 감소 가능함을 확인하였다. 또한, 제안된 특징점 추출기가 통합된 레이다 기반 보안 감시 시스템을 통해 차, 자전거, 보행자 및 전동 킥보드에 대한 분류 실험이 수행되었고, 성능 분석 결과 93.4%의 정확도 성능을 확인하였다.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• International Journal of Control, Automation, and Systems
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• 제4권6호
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• pp.705-713
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• 2006

For the analysis of multiunit extracellular neural signals as multiple spike trains, neural spike sorting is essential. Existing algorithms for the spike sorting have been unsatisfactory when the signal-to-noise ratio(SNR) is low, especially for implementation of fully-automated systems. We present a novel method that shows satisfactory performance even under low SNR, and compare its performance with a recent method based on principal component analysis(PCA) and fuzzy c-means(FCM) clustering algorithm. Our system consists of a spike detector that shows high performance under low SNR, a feature extractor that utilizes projection pursuit based on negentropy maximization, and an unsupervised classifier based on Gaussian mixture model. It is shown that the proposed feature extractor gives better performance compared to the PCA, and the proposed combination of spike detector, feature extraction, and unsupervised classification yields much better performance than the PCA-FCM, in that the realization of fully-automated unsupervised spike sorting becomes more feasible.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2009년도 춘계학술발표대회
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.