• ETRI Journal
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• 제40권2호
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• pp.246-256
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• 2018

A spatial augmented reality (SAR) system enables a virtual image to be projected onto the surface of a real-world object and the user to intuitively control the image using a tangible interface. However, occlusions frequently occur, such as a sudden change in the lighting environment or the generation of obstacles. We propose a robust object tracker based on a multithreaded system, which can track an object robustly through occlusions. Our multithreaded tracker is divided into two threads: the detection thread detects distinctive features in a frame-to-frame manner, and the tracking thread tracks features periodically using an optical-flow-based tracking method. Consequently, although the speed of the detection thread is considerably slow, we achieve real-time performance owing to the multithreaded configuration. Moreover, the proposed outlier filtering automatically updates a random sample consensus distance threshold for eliminating outliers according to environmental changes. Experimental results show that our approach tracks an object robustly in real-time in an SAR environment where there are frequent occlusions occurring from augmented projection images.

• Child Studies in Asia-Pacific Contexts
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• 제2권2호
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• pp.77-90
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• 2012

The present study investigated infants' ability to form a category of a support relation (i.e., "on") when the objects depicting the relation were perceptually simple versus more complex. Twenty Korean infants of 14 months were habituated to dynamic support events with objects that were either simple or more complex in appearance. They were then tested with events that differed from the habituation events in the specific objects, spatial relation, or both. Infants formed a support category whether familiarized to simple or complex objects, looking significantly longer at test events with a novel than familiar relation. The results indicate that at 14 months of age, object features do not impact infants' ability to form a categorical representation of support.

• Journal of information and communication convergence engineering
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• 제18권2호
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• pp.69-74
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• 2020

Myanmar, also known as Burma, has a rich cultural heritage, and its historical tourist attractions well known around the world. Therefore, we designed and developed an interactive spatial augmented reality (iSAR) book on the cultural heritage of Myanmar. This iSAR book has total of 18 pages with rich media content including videos, animations, audio, and images featuring the cultural heritage of Myanmar in a digital format. In addition to virtual content, navigational features such as virtual buttons and touch-based hand gestures were implemented using Leap Motion and VVVV. Therefore, the developed iSAR book allows virtual content and navigational features to merge seamlessly into a physical book. Five participants were recruited to evaluate the prototype iSAR book, and interviews were conducted to gather their feedback based on its immersive qualities. Thus, the developed iSAR book on Myanmar effectively shares the cultural heritage of Myanmar, and ultimately allows users to explore and gain more insight into the country.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.4060-4079
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• 2020

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

• 대한공간정보학회지
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• 제17권3호
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• pp.13-21
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• 2009

수치지형도는 정확성과 함께 최신성을 가져야만 국가공간정보로서 그 역할을 다할 수 있으나 항공사진 촬영과 측량, 현지조사를 수행해야 하는 제작과정의 특성상 많은 시간과 비용이 소요되어 최신성을 유지하는데 한계가 있다. 준공도면을 활용하여 수시 갱신한다면 기 작성된 도면을 재활용하므로 불필요한 국가예산의 낭비를 막을 수 있고 측량된 성과를 이용할 수 있으므로 정확한 갱신이 가능하여 그 효과가 크나 CAD 도면 표준화 미비, 준공도면과 수치지형도 파일 형식의 상이, 대장자료의 미비, 입력 객체와 기존 객체와 위상 관계 등으로 인해 어려움이 있는 실정이다. 본 연구에서는 첫째, 준공도면에서 추출된 CAD 객체를 기존 객체의 존재 여부에 따라 신규 혹은 갱신으로 안착시키고 객체간 공간관계에 따라 위상정보를 생성한 후 둘째, 이의 무결성 여부를 검수하여 효율적으로 수치지형도의 갱신을 수행할 수 있는 방안을 제시하였고 그 결과로 수치지형도의 정확성과 최신성을 확보하였다.

• 한국지리정보학회지
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• 제7권1호
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• pp.39-51
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• 2004

본 연구는 선형사상 일반화에서 사상들 간의 공간적 충돌, 벡터변위에 따른 위치변동 등의 문제를 개선하기 위해 새로운 Simoo 알고리즘을 제안하고자 하였다. Simoo 알고리즘의 원리는 단순화와 완만화를 채택하였다. Simoo 알고리즘에서 사용된 임계치는 수선길이, 편각, 평균 vertex 길이이다. Simoo 알고리즘은 축척별 적용 가능, 지도학적 세련미, 자료점의 위치 변동에 따른 논리적 오류 발생의 감소, 공간현상특징 유지 등의 특징을 갖는다. Simoo 알고리즘의 적용 결과는 Douglas-Peucker 알고리즘과 비교하였다. 그 결과 두 알고리즘에서 해안선과 하계망 길이는 97% 이상 유지되었으며, 자료점 제거율은 Simoo보다 Douglas-Pecuker 알고리즘이 효과적이었고, 사상들간의 충돌은 Simoo에서 최소화되었다. 선의 곡률과 완만화는 Simoo 적용시 축척에 따라 감소하였다. 결과적으로 Douglas-Peucker보다는 Simoo 알고리즘이 지도학적 일반화에 적합하다고 볼 수 있다.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.177-186
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• 2023

Hyperspectral imaging technology is one of the most efficient and fast-growing technologies in recent years. Hyperspectral image (HSI) comprises contiguous spectral bands for every pixel that is used to detect the object with significant accuracy and details. HSI contains high dimensionality of spectral information which is not easy to classify every pixel. To confront the problem, we propose a novel RGB channel Assimilation for classification methods. The color features are extracted by using chromaticity computation. Additionally, this work discusses the classification of hyperspectral image based on Domain Transform Interpolated Convolution Filter (DTICF) and 3D-CNN with Bi-directional-Long Short Term Memory (Bi-LSTM). There are three steps for the proposed techniques: First, HSI data is converted to RGB images with spatial features. Before using the DTICF, the RGB images of HSI and patch of the input image from raw HSI are integrated. Afterward, the pair features of spectral and spatial are excerpted using DTICF from integrated HSI. Those obtained spatial and spectral features are finally given into the designed 3D-CNN with Bi-LSTM framework. In the second step, the excerpted color features are classified by 2D-CNN. The probabilistic classification map of 3D-CNN-Bi-LSTM, and 2D-CNN are fused. In the last step, additionally, Markov Random Field (MRF) is utilized for improving the fused probabilistic classification map efficiently. Based on the experimental results, two different hyperspectral images prove that novel RGB channel assimilation of DTICF-3D-CNN-Bi-LSTM approach is more important and provides good classification results compared to other classification approaches.

• 한국멀티미디어학회논문지
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• 제18권4호
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• pp.460-472
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• 2015

Most of previous visual attention system finds attention regions based on saliency map which is combined by multiple extracted features. The differences of these systems are in the methods of feature extraction and combination. This paper presents a new system which has an improvement in feature extraction method of color and motion, and in weight decision method of spatial and temporal features. Our system dynamically extracts one color which has the strongest response among two opponent colors, and detects the moving objects not moving pixels. As a combination method of spatial and temporal feature, the proposed system sets the weight dynamically by each features' relative activities. Comparative results show that our suggested feature extraction and integration method improved the detection rate of attention region.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 1999년도 Proceedings of International Symposium on Remote Sensing
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• pp.310-315
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• 1999

We extracted narrow open water streams from JERS-1 SAR images of the Amazon rain forest. The extracted range of these streams were almost comparable to a high level extraction of the same streams from near-IR images of JERS-1 VNIR data notwithstanding that these features in SAR images show the strong dependence of the observation angle. Large water bodies are relatively easy to extract from JERS-1 SAR images, as they tend to appear as very dark areas; but streams whose width is nearly equal to or less than the spatial resolution no longer appear as very dark features. By using strong scatterers distributed sparsely along the radar facing sides of the streams, we can successfully estimate approximate ranges of waterways and then extract relatively dark line-like features within these ranges.

• 전자공학회논문지B
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• 제31B권3호
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.