• Journal of Korea Multimedia Society
• /
• v.12 no.11
• /
• pp.1692-1700
• /
• 2009

This paper proposes the direction distribution of surface normal vectors as a feature descriptor of three-dimensional models. Proposed the feature descriptor handles rotation invariance using a principal component analysis(PCA) method, and performs mesh simplification to make it robust and nonsensitive against noise addition. Our method picks samples for the distribution of normal vectors to be proportional to the area of each polygon, applies weight to the normal vectors, and applies interpolation to enhance discrimination so that the information on the surface with less area may be less reflected on composing a feature descriptor. This research measures similarity between models with a L1-norm in the probability density histogram where the distances of feature descriptors are normalized. Experimental results have shown that the proposed method has improved the retrieval performance described in an average normalized modified retrieval rank(ANMRR) by about 17.2% and the retrieval performance described in a quantitative discrimination scale by 9.6%~17.5% as compared to the existing method.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2008.05a
• /
• pp.164-168
• /
• 2008

일반적으로 확률강우량은 관측지점에서 관측된 연최대 강우량자료를 바탕으로 빈도해석을 적용하여 산정한다. 그러나 국내에서는 매시각별로 관측된 자료가 대부분이기 때문에 단기간 혹은 장기간의 지속기간에 대한 확률강우량을 산정하는 것은 쉽지 않다. 따라서 본 연구에서는 매시각단위의 지속기간 강우자료를 바탕으로 다양한 지속기간에 대한 확률 강우량을 산정할 수 있는 스케일 성질을 적용하여 확률강우량을 산정하여 정확성을 판단하였다. 강우자료는 비교적 신뢰성이 높고 자료기간이 긴 기상청 지점 22개 자료를 사용하였으며, 2003년까지의 관측된 자료를 이용하여 확률강우량을 산정한 후 지점빈도해석 프로그램인 FARD2006과 비교하여 지점빈도해석의 결과 값을 참값으로 절대상대오차를 산정하여 비교하였다. 산정한 방법은 기준이 되는 확률강우량을 산정한 후 그보다 긴 지속기간에 대한 확률강우량을 산정하는 방법인 상향스케일링 (Up-scaling)과 그 보다 짧은 지속기간에 대한 확률강우량을 산정하는 방법인 하향스케일링(Down-scaling)의 두 가지 방법으로 확률강우량을 산정하였다. 두 방법 모두 1시간$\sim$24시간의 지속기간에 대한 확률강우량을 2년$\sim$500년의 재현기간에 대하여 확률강우량을 산정하였으며, 빈도해석으로 산정한 FARD2006의 결과값과 비교하여 절대상대오차를 산정하였다. 그 결과, 시간단위자료를 사용할 경우 대부분 절대상대오차가 10% 미만인 결과를 얻을 수 있었으며, 14개의 재현기간 중에서 8개 이상의 재현기간에 대해 적용이 가능한 것으로 나타났다. 지속기간 1시간 강우자료를 기준 지속기간으로 1시간 미만의 지속기간에 대한 확률강우량을 추정한 결과 10분을 제외하고는 대부분 절대상대오차가 10% 내외의 정확도를 가지는 것으로 나타났다. 따라서 스케일 성질을 이용하여 미계측 강우지속기간의 확률강우량을 추정할 수 있을 것으로 판단된다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.39 no.4
• /
• pp.370-380
• /
• 2002

This paper proposes an ATR (Automatic Target Recognition) algorithm for identifying non-occluded and occluded military vehicles in natural FLIR (Forward Looking InfraRed) images. After segmenting a target, a radial function is defined from the target boundary to extract global shape features. Also, to extract local shape features of upper region of a target, a distance function is defined from boundary points and a line between two extreme points. From two functions and target contour, four global and four local shape features are proposed. They are much more invariant to translation, rotation and scale transform than traditional feature sets. In the experiments, we show that the proposed feature set is superior to the traditional feature sets with respect to the similarity-transform invariance and recognition performance.

• Journal of Advanced Navigation Technology
• /
• v.16 no.2
• /
• pp.394-400
• /
• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.9
• /
• pp.4467-4486
• /
• 2018

Trying to deal with the problem of low robustness of Copy-Move Forgery Detection (CMFD) under various transformation and degradation attacks, a novel CMFD method is proposed in this paper. The main advantages of proposed work include: (1) Discrete Analytical Fourier-Mellin Transform (DAFMT) and Locality Sensitive Hashing (LSH) are combined to extract the block features and detect the potential copy-move pairs; (2) The Euclidian distance is incorporated in the pixel variance to filter out the false potential copy-move pairs in the post-verification step. In addition to extracting the effective features of an image block, the DAMFT has the properties of rotation and scale invariance. Unlike the traditional lexicographic sorting method, LSH is robust to the degradations of Gaussian noise and JEPG compression. Because most of the false copy-move pairs locate closely to each other in the spatial domain or are in the homogeneous regions, the Euclidian distance and pixel variance are employed in the post-verification step. After evaluating the proposed method by the precision-recall-$F_1$ model quantitatively based on the Image Manipulation Dataset (IMD) and Copy-Move Hard Dataset (CMHD), our method outperforms Emam et al.'s and Li et al.'s works in the recall and $F_1$ aspects.

• Journal of Korean Society on Water Environment
• /
• v.34 no.4
• /
• pp.391-398
• /
• 2018

The main purpose of this study is to suggest and recommend the more reliable flow direction methods within the framework of DEM and power law distribution, by investigating the existing methodologies. To this end SFD (single flow direction method), MFD (multiple flow direction method) and IFD (Infinite flow direction method) are applied to analyze the determination of a flow direction for the water particles as seen in the Jeonjeokbigyo basin, and then assessed with respect to the variation of flow accumulation in that region. As the main results revealed, the study showed the different patterns of flow accumulation are found out from each applications of flow direction methods utilized in this study. This brings us to understand that as the flow dispersion on DEM increases, in this case the contributing areas to the outlet grow in sequence of SFD, IFD, MFD, but it is noted that the contribution of individual pixels into outlet decreases at that time. In what follows, especially with the MFD and IFD, the result tends to make additional hydrologic abstraction from rainfall excess, as noted due to the flow dispersion within flow paths on DEM. Based on the parameter estimation for a power law distribution, which is frequently used for identify the aggregation structure of complex system, by maximum likelihood flow accumulation can be thought of as a scale invariance factor. In this regard, the combination of flow direction methods could give rise to the more realistic water flow on DEM, as revealed through the separate flow direction methods as utilized for dispersion and aggregation effects of water flow within the available different topographies.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.195-195
• /
• 2018

기상청에서는 영국 전지구기후모델인 HadGEM2-AO 기반의 영국 지역기후모델 HadGEM3-RA로부터 생산된 기후변화 시나리오를 기후변화예측을 위한 국가표준시나리오 자료로 제공하고 있다. 하지만, 기후모델의 특성상, 관측자료와 모의자료 간에는 통계적인 차이가 존재하며, 이러한 차이를 무시하고 원자료를 그대로 분석에 사용하는 것은 무의미 하다. 따라서 이러한 보정하기 위해서 주로 Quantile Mapping, Quantile Delta Mapping, Detrended Quantile Mapping 방법이 주로 사용된다. 하지만 어떠한 편의보정 방법이든 극값이 다수 존재하는 미래기간 모의자료를 보정할 때에는 외삽법(extrapolation)의 적용이 필요하다. 외삽법의 경우 constant correction 방법이 주로 적용된다. 본 연구에서는 기상청의 국가표준시나리오를 대상으로 이러한 편의보정 방법의 적용에 따른 미래 극한강우량의 차이를 분석하고자 하였다. 우선, 모의자료에서 우리나라 주요 기상관측지점에 해당하는 격자로부터 강우량자료를 추출하고 연최대강우시계열을 산정하였다. 그 후, 위의 세 가지 편의보정 방법을 이용하여 강우자료의 편의보정을 수행하였으며, constant correction 방법을 적용하여 이상치를 보정하였다. 그 후, 보정된 미래기간 모의자료의 추세를 분석하고, 이를 미래 확률강우량 산정방법인 scale-invariance 기법에 적용하여 미래 확률강우량을 산정하였다. 그 결과, 외삽법의 적용에 따라 편의보정 방법에 따라 미래 자료의 추세 또는 확률강우량의 변화패턴은 큰 차이를 나타내지 않았지만, 그 값 자체는 다소 차이가 있는 것으로 나타났다. 이러한 차이는 사용된 GCM과 RCM 조합으로 인한 오차와 더해져, 미래 예측결과의 불확실성으로 나타나기에 미래 극한강우량 예측을 위해서는 다수의 GCM, RCM 조합뿐만 아니라 다수의 편의보정 방법에 따른 결과도 함께 고려(ensemble)하여 결과를 나타내는 것이 필요할 것으로 판단된다.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.4
• /
• pp.47-57
• /
• 2009

Event detection and recognition is an active and challenging topic recent in Computer Vision. This paper describes a new method for recognizing events caused by human motion from video sequences in an office environment. The proposed approach analyzes human motions using Motion History Image (MHI) sequences, and is invariant to body shapes. types or colors of clothes and positions of target objects. The proposed method has two advantages; one is thant the proposed method is less sensitive to illumination changes comparing with the method using color information of objects of interest, and the other is scale invariance comparing with the method using a prior knowledge like appearances or shapes of objects of interest. Combined with edge detection, geometrical characteristics of the human shape in the MHI sequences are considered as the features. An advantage of the proposed method is that the event detection framework is easy to extend by inserting the descriptions of events. In addition, the proposed method is the core technology for event detection systems based on context-aware computing as well as surveillance systems based on computer vision techniques.

• Journal of the Institute of Convergence Signal Processing
• /
• v.5 no.4
• /
• pp.256-262
• /
• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.8
• /
• pp.187-195
• /
• 2013

Most of former studies for car license plate detection restrict the image acquisition environment. The aim of this research is to diminish the restrictions by proposing a new method of using SIFT and neural network. SIFT can be used in diverse situations with less restriction because it provides size- and rotation-invariance and large discriminating power. SIFT extracted from the license plate image is divided into the internal(inside class) and the external(outside class) ones and the classifier is trained using them. In the proposed method, by just putting the various types of license plates, the trained neural network classifier can process all of the types. Although the classification performance is not high, the inside class appears densely over the plate region and sparsely over the non-plate regions. These characteristics create a local feature map, from which we can identify the location with the global maximum value as a candidate of license plate region. We collected image database with much less restriction than the conventional researches. The experiment and evaluation were done using this database. In terms of classification accuracy of SIFT keypoints, the correct recognition rate was 97.1%. The precision rate was 62.0% and recall rate was 50.2%. In terms of license plate detection rate, the correct recognition rate was 98.6%.