• 한국GIS학회:학술대회논문집
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• 한국GIS학회 2008년도 공동추계학술대회
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• pp.245-246
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• 2008

The goal of this study is to explore the possibility of KLT tracker for tracking the features between two images including rotation and shift. As a test site, Jangsu-Gun area of South Korea is selected and the images taken from UAV camera are used for analysis. The analysis was carried out using KLT tracker developed in a PC environment. The results of the experiment used two images with the large overlapping area are compared with the results of two images with the little overlapping area and rotation. Overall, the research indicates that the integrated features of littlerotation and motion images can significantly increase during the tracking process. But using KLT tracker for extracting and tracking features between images with large rotation and motion, the number of tracked features are decreased.

• 컴퓨터교육학회논문지
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• 제11권4호
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• pp.85-93
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• 2008

본 논문은 3차원 기하 모델에서 돌출된 특징 영역을 유지하며 단순화하기 위한 방법에 관한 것으로서 3차원 레인지 스캐닝 시스템으로 부터 삼각형 기하 데이터를 입력받아 기하 데이터의 각 점들에 대해 근사화 표면을 생성하고, 생성된 표면에서의 점들에 대한 곡률과 곡률 미분 값을 측정한 후, 기하 데이터의 에지에 대해 제로-클로싱을 측정하여 특정점을 찾아낸다. 특정점을 주 곡률 방향으로 연결하여 특정 선을 생성하고, 거리기반오차에 특정에지오차를 조합한 FQEM(Feature Quadric Error Metric)을 이용하여 단순화를 수행하게 된다. 본 논문에서는 제안방법의 우수성을 기존 방법과의 실험결과의 비교를 통하여 보여 준다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.520-523
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• 1999

Specially, form-based documents are easily understood, quickly processed and thus used more than the general documents. In this paper, a method to classify the documents with minimum features is proposed, not like former methods which use all possible features. To apply this characteristics. a document was first partitioned to areas of certain shape and size, then features were extracted from the partitioned area. It is also possible to sort the partitioned area by using the fact that each partitioned area has the different significance in the point of feature. In conclusion, by using proposed method of extracting features from partitioned document, the processing time decreases due to search area reduction.

• 한국음향학회지
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• 제21권4호
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• pp.156-156
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• 2002

We apply independent component analysis (ICA) for extracting an optimal basis to the problem of finding efficient features for representing speech signals of a given speaker The speech segments are assumed to be generated by a linear combination of the basis functions, thus the distribution of speech segments of a speaker is modeled by adapting the basis functions so that each source component is statistically independent. The learned basis functions are oriented and localized in both space and frequency, bearing a resemblance to Gabor wavelets. These features are speaker dependent characteristics and to assess their efficiency we performed speaker identification experiments and compared our results with the conventional Fourier-basis. Our results show that the proposed method is more efficient than the conventional Fourier-based features in that they can obtain a higher speaker identification rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2154-2168
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• 2022

Our work addresses the issues associated with usage of the semantic features by Bag of Words model, which requires construction of the dictionary. Extracting the relevant features and clustering them into code book or dictionary is computationally intensive and requires large storage area. Hence we propose to use a simple distribution of multiple shape based features, which is a mixture of gradients, radius and slope angles requiring very less computational cost and storage requirements but can serve as an equivalent image representative. The experimental work conducted on PASCAL VOC 2007 dataset exhibits marginally closer performance in terms of accuracy with the Bag of Word model using Self Organizing Map for clustering and very significant computational gain.

• 한국멀티미디어학회논문지
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• 제16권7호
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• pp.820-828
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• 2013

영상에서 임의의 점에 대한 고유한 특징을 계산하는 알고리즘은 파노라마 영상의 제작, 스테레오 영상의 획득, 물체 인식, 이미지 분석 등에 다양하게 사용되는 중요한 요소이다. 일반적으로 어떤 점의 특징은 스칼라 형태가 아닌 벡터형태로 나타나게 되는데, 무수히 많은 특징 점들을 서로 비교하는 작업은 매우 많은 계산량을 요구한다. 본 연구에서는 영상의 특징점 계산에 SURF(speeded up robust features)를 이용하였고, 이미지로부터 추출된 특징을 PCA(principal component analysis)기법을 이용하여 벡터의 차원을 축소하여 연결리스트 자료구조에 정렬한 다음 특징을 비교하는 기법을 제안한다. 제안된 특징의 비교 방법을 적용할 경우 기존 방법의 매칭 정확도는 유지한 상태에서 계산시간을 줄일 수 있는 것을 실험을 통하여 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권9호
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• pp.4658-4679
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• 2017

Android is now the most popular smartphone platform and remains rapid growth. There are huge number of sensitive privacy information stored in Android devices. Kinds of methods have been proposed to detect Android malicious applications and protect the privacy information. In this work, we focus on extracting the fine-grained features to maximize the information of Android malware detection, and selecting the least joint features to minimize the number of features. Firstly, permissions and APIs, not only from Android permissions and SDK APIs but also from the developer-defined permissions and third-party library APIs, are extracted as features from the decompiled source codes. Secondly, feature selection methods, including information gain (IG), regularization and particle swarm optimization (PSO) algorithms, are used to analyze and utilize the correlation between the features to eliminate the redundant data, reduce the feature dimension and mine the useful joint features. Furthermore, regularization and PSO are integrated to create a new joint feature mining method. Experiment results show that the joint feature mining method can utilize the advantages of regularization and PSO, and ensure good performance and efficiency for Android malware detection.

• Journal of information and communication convergence engineering
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• 제12권4호
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• 한국멀티미디어학회논문지
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• 제2권2호
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• pp.129-136
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• 1999

특정 이미지에서의 선형적 특정은 이미지를 분석하고 이해하는데 충분한 정보를 제공하기도 한다. 본고에 서는 이미지에서 선형적 특징을 추출하기 위한 신뢰성 있는 방법을 제시한다. 일반적으로 후프 변형 방법은 이러한 선형적 특정을 추출하는 최적의 방법 중의 하나로 인식되어 왔다. 대부분의 후프 기반 방법들은 특정 edge 모델올 선택하고, 인식된 edge 픽셀의 속성을 반영하는 변형식을 활용하여 파라미터 공간에 그 발생빈도 를 기록하는 과정을 거치게 된다. 주로 edge 픽셀의 gradient 크기와 방향이 선형적 특정을 결정하는데 사용되 지만, 본고에서는 그 값틀이 퍼지변수로 활용될 수 있음을 보이고 파라미터 공간에 누적값을 계산하는데 활용한다- 이 방법을 기존의 방법과 비교하기 위하여 에러 측정 방식을 제안하고, 실험을 한 결과, 기존의 방법과 비교하여 우수한 성능을 보인다.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2004년도 The 3rd Annual Conference for The Korean Society for Bioinformatics Association of Asian Societies for Bioinformatics 2004 Symposium
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• pp.158-166
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• 2004

Predicting the destination of a protein in a cell gives valuable information for annotating the function of the protein. Recent technological breakthroughs have led us to develop more accurate methods for predicting the subcellular localization of proteins. The most important factor in determining the accuracy of these methods, is a way of extracting useful features from protein sequences. We propose a new method for extracting appropriate features only from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reach 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which show the highest prediction accuracy among methods reported so far with such data sets. Our numerical experimental results confirm that our feature extraction method based on pairwise sequence alignment, is useful for this classification problem.