• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.275-279
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• 2013

Recently, as multimedia information becomes popular, there are many studies to retrieve images based on images in the web. However, it is hard to find the matching images which users want to find because of various patterns in images. In this paper, we suggest an efficient images retrieval system based on images for finding products in internet shopping malls. We extract features for image retrieval by using SIFT (Scale Invariant Feature Transform) algorithm, repeat keypoint matching in various dimension by using PCA-SIFT, and find the image which users search for by combining them. To verify efficiency of the proposed method, we compare the performance of our approach with that of SIFT and PCA-SIFT by using images with various patterns. We verify that the proposed method shows the best distinction in the case that product labels are not included in images.

• Journal of KIISE:Databases
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• v.36 no.6
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• pp.446-454
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• 2009

Among various region detector and shape feature extraction method, MSER(Maximally Stable Extremal Region) and SIFT and its variant methods are popularly used in computer vision application. However, since SIFT is sensitive to the illumination change and MSER is sensitive to the scale change, it is not easy to apply the image similarity retrieval. In this paper, we present a Scale and Rotation Invariant Region Feature(SRIRF) descriptor using scale pyramid, MSER and affine normalization. The proposed SRIRF method is robust to scale, rotation, illumination change of image since it uses the affine normalization and the scale pyramid. We have tested the SRIRF method on various images. Experimental results demonstrate that the retrieval performance of the SRIRF method is about 20%, 38%, 11%, 24% better than those of traditional SIFT, PCA-SIFT, CE-SIFT and SURF, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1690-1704
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• 2013

An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.125-129
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• 2012

In this paper, we propose an intelligent method to surveil moving objects and to judge an unusual-behavior by using network cameras. To surveil moving objects, the Scale Invariant Feature Transform (SIFT) algorithm is used to characterize the feature information of objects. To judge unusual-behaviors, the virtual human skeleton is used to extract the feature points of a human in input images. In this procedure, the Principal Component Analysis (PCA) improves the accuracy of the feature vector and the fuzzy classifier provides the judgement principle of unusual-behaviors. Finally, the experiment results show the effectiveness and the feasibility of the proposed method.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.637-639
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• 2005

영상 정보를 통한 실내 환경의 인식은 지능형 로봇에서 매우 중요한 문제이다. 영상을 통한 실내 환경정보는 로봇의 각도나 위치의 영향으로 불확실해질 수 있으므로 영상 인식 기법은 이러한 불확실함에 강인함을 갖고 있어야 한다. 본 논문에서는 불확실하게 들어오는 실내 환경 정보에서 PCA를 통한 위치 정보와 SIFT를 통한 물체 존재 정보를 추출하고 이를 베이지안 네트워크에 적용하여 장소 및 물체를 인식하는 방법을 제안한다. 실제 실내 환경에서의 실험을 통하여 8곳의 위치 및 20개의 오브젝트를 효과적으로 인식하는 것을 확인할 수 있었으며 위치에 따른 물체의 존재 확률 추론 및 존재 물체에 의한 위치 확률의 수정 등 다양한 방향의 추론도 가능하다.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.55-65
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• 2011

In this paper, a novel method of marker detection robust against marker occlusion in augmented reality is proposed. the proposed method consists of four steps. In the first step, in order to effectively detect an occluded marker, we first utilize the Affine-SIFT (ASIFT, Affine-Scale Invariant Features Transform) for detecting matching points between an enrolled marker and an input images with an occluded marker. In the second step, we apply the Principal Component Analysis (PCA) for eliminating outlier of the matching points in the enrolled marker. And then matching points are projected to the first and second axis for longest value and the shortest value of an ellipse are determined by average distance between the projected points and a center of the points. In the third step, Convex-hull vertices including matching points are considered as polygon vertices for estimating a geometric affine transformation. In the final step, by estimating the geometric affine transformation of the points, a marker robust against a marker occlusion is detected. Experimental results have shown that the proposed method effectively detects occlude markers.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1910-1911
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• 2011

본 논문에서는 네트워크 카메라를 이용한 물체 감시 및 비정상 행위의 판단을 위한 실시간 시스템을 제안한다. 제안된 시스템은 먼저 물체의 감시를 위해 SIFT 알고리즘에 기반으로 감시 물체의 특징 정보를 DB화 하고, 히스토그램(Histogram)기법을 활용하여 감시지역을 설정한다. 또한 인간의 행동 및 비정상 행위를 판단하기 위하여, 가상 인간 스켈레톤 모델을 이용하여 입력된 영상에서의 인간의 특징점을 추출한다. 추출된 특징점을 바탕으로 PCA(Principal Component Analysis)를 이용하여 인간의 움직임을 보다 정확하게 표현할 수 있는 특징벡터를 생성하였다. 생성된 특징벡터를 기반으로 퍼지분류기를 이용하여 인간의 행동을 분류하고, 생성된 특징벡터와 특정물체의 거리를 기반으로 인간의 비정상행위를 판단한다. 제안된 방법은 실험을 통해 시스템의 응용 가능성을 증명한다.