• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.9
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• pp.49-55
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• 2019

This paper presents a marker image evaluation method based on analysis of object distribution in images and classification of images with repetitive patterns for effective marker-based augmented reality (AR) system development. We measure the variance of feature point coordinates to distinguish marker images that are vulnerable to occlusion, since object distribution affects object tracking performance according to partial occlusion in the images. Moreover, we propose a method to classify images suitable for object recognition and tracking based on the fact that the distributions of descriptor vectors among general images and repetitive-pattern images are significantly different. Comprehensive experiments for marker images confirm that the proposed marker image evaluation method distinguishes images vulnerable to occlusion and repetitive-pattern images very well. Furthermore, we suggest that scale-invariant feature transform (SIFT) is superior to speeded up robust features (SURF) in terms of object tracking in marker images. The proposed method provides users with suitability information for various images, and it helps AR systems to be realized more effectively.

• Journal of Korea Multimedia Society
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• v.4 no.3
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• pp.215-221
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• 2001

In this paper, we propose a algorithm that apply different weight-sampling values according to the directions of the contour to reduce errors that can arise in extracting the feature of an contoured object. Especially, it 8is designed to have invariant property under the circumstances like the rotation, transition and scaling. The output matrix of feature set is made through the size function of the proposed algorithm, and the euclidean distance between the output matrix and that of the original image is calculated. Experimental result shows that the proposed algorithm reduces the euclidean distance between the original image and the changed image-by 57% in rotation and by 91% in scaling.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.8C
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• pp.783-792
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• 2005

This paper proposes a feature extraction method for sketch-based image retrieval of animation character. We extract the specific regions using the detection of scene change and correlation points between two frames, and the property of animation production. We detect the area of focused similar colors in extracted specific region. And it is used as feature descriptor for image retrieval that focused color(FC) of regions, size, relation between FCs. Finally, an user can retrieve the similar character using property of animation production and user's sketch as a query Image.

• 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 Information Processing Systems
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• v.16 no.1
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• pp.49-60
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• 2020

To solve the edge ringing or block effect caused by the partial differential diffusion in image enhancement domain, a new image enhancement algorithm based on bidirectional diffusion, which smooths the flat region or isolated noise region and sharpens the edge region in different types of defect images on aviation composites, is presented. Taking the image pixel's neighborhood intensity and spatial characteristics as the attribute descriptor, the presented bidirectional diffusion model adaptively chooses different diffusion criteria in different defect image regions, which are elaborated are as follows. The forward diffusion is adopted to denoise along the pixel's gradient direction and edge direction in the pixel's smoothing area while the backward diffusion is used to sharpen along the pixel's gradient direction and the forward diffusion is used to smooth along the pixel's edge direction in the pixel's edge region. The comparison experiments were implemented in the delamination, inclusion, channel, shrinkage, blowhole and crack defect images, and the comparison results indicate that our algorithm not only preserves the image feature better but also improves the image contrast more obviously.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1179-1191
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• 2019

We design an ingenious view-pooling method named learning-based multiple pooling fusion (LMPF), and apply it to multi-view convolutional neural network (MVCNN) for 3D model classification or retrieval. By this means, multi-view feature maps projected from a 3D model can be compiled as a simple and effective feature descriptor. The LMPF method fuses the max pooling method and the mean pooling method by learning a set of optimal weights. Compared with the hand-crafted approaches such as max pooling and mean pooling, the LMPF method can decrease the information loss effectively because of its "learning" ability. Experiments on ModelNet40 dataset and McGill dataset are presented and the results verify that LMPF can outperform those previous methods to a great extent.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.346-348
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• 2006

본 논문은 잎맥 특징을 이용한 식물의 잎 이미지 검색 방법을 제안한다. 식물의 검색을 위해 모양 기반의 검색방법을 사용하였으며, 잎의 외곽선 분만 아니라 내부의 잎맥 정보를 이용하여 정확율을 향상시켰다. 외곽선은 MPP(Minimum Perimeter Polygons) 알고리즘을 개선하여 표현하고, 내부의 잎맥의 특징은 CSS(Curvature Scale Space)를 개선하여 주맥과 교차점, 끝점을 추출하여 표현하였다. 특징 점들간의 관계와 거리값을 통해 가중치가 있는 그래프로 표현하고 이 값을 통해 유사도를 계산하였다. 실험에서는 식물도감에서 1000여개의 식물 잎 이미지를 추출하여 기존의 알고리즘인 Fourier Descriptor, CSSD, CCD, Moment Invariants, MPP와 비교하였다.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.57-60
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• 2001

In this paper, we propose a new 3D object retrieval system using the shape information of 2D silhouette images. 2D images at different view points are derived from a 3D model and linked to the model. Shape feature of 2D image is extracted by a region-based descriptor. In the experiment, we compare the results of the proposed system with those of the system using curvature scale space(CSS) to show the efficiency of our system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.40-41
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• 2010

실시간 파노라마 영상 합성 기술을 모바일 콘텐츠에 적용하기 위해서는 모바일 디바이스에 적합한 영상 합성 방식을 정의해야 한다. 특징점을 추출하여 연속적인 영상들 사이의 유사성을 찾아내는 방식 [1]은 소모되는 연산비용을 고려할 때 모바일 디바이스에 응용하기 부적합하다. 따라서 기존의 접근방식에 비해 소요되는 연산비용을 줄이고, 성능은 유지하는 기법이 필요하다. 본 논문에서는 특징점의 표현자(descriptor)를 생성하지 않고 특징점(feature point) 주변의 움직임을 추정(motion estimation)하여 연속적인 영상의 위치를 찾아내는 기법을 제안한다.

• Journal of Korea Multimedia Society
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• v.17 no.1
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• pp.15-25
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• 2014

In this paper, we propose a loitering detection using trajectory probability distribution and local direction descriptor for intelligent surveillance system. We use a background modeling method for detecting moving object and extract the motion features from each moving object for making feature vectors. After that, we detect the loitering behavior person using K-Nearest Neighbor classifier. We test the proposed method in real world environment and it can achieve real time and robust detection results.