• Journal of Korean Society for Geospatial Information Science
• /
• v.21 no.2
• /
• pp.63-69
• /
• 2013

Change detection technique is essential to various applications of Very High-Resolution(VHR) satellite imagery and land monitoring. However, change detection accuracy of VHR satellite imagery can be decreased due to various geometrical dissimilarity. In this paper, the existing fusion evaluation indexes were revised and applied to improve VHR imagery based change detection accuracy between multi-temporal images. In addition, appropriate change detection methodology of VHR images are proposed through comparison of general change detection algorithm with cross-sharpened image based change detection algorithm. For these purpose, ERGAS, UIQI and SAM, which were representative fusion evaluation index, were applied to unsupervised change detection, and then, these were compared with CVA based change detection result. Methodologies for minimizing the geometrical error of change detection algorithm are analyzed through evaluation of change detection accuracy corresponding to image fusion method, also. The experimental results are shown that change detection accuracy based on ERGAS index by using cross-sharpened images is higher than these based on other estimation index by using general fused image.

• Korean Journal of Remote Sensing
• /
• v.34 no.5
• /
• pp.777-786
• /
• 2018

GF (Guided Filtering) is a representative image processing technique to effectively remove noise while preserving edge information in the digital image. In this paper, we proposed a unsupervised change detection method for the KOMPSAT-3 satellite image using the GF and evaluated its performance. In order to utilize GF for the unsupervised change detection, cross-sharpened images were generated based on GF, and CVA (Change Vector Analysis) was applied to the generated cross-sharpened images to extract the changed area in the multitemporal satellite imagery. Experimental results using KOMPSAT-3 satellite images showed that the proposed method can be effectively used to detect changed regions compared with CVA results based on existing cross-sharpened images.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.08a
• /
• pp.197-200
• /
• 2000

본 논문에서는 교통정보 수집용 영상검지기를 위한 실제 교차로 상황에 잘 맞는 배경영상 생성 방법을 제안한다. 교차로 특성상 진행중인 차량 및 신호 대기중인 차량 등 여러 가지 통행패턴이 있을 수 있는데 차량의 움직임 정보를 추출하기 위해 장면차이 방법을 사용한다. 영상열내 차량의 움직임을 관찰하여 배경영상의 생성 과정에 선택적으로 부분 영역을 반영함으로써 보다 좋은 초기 배경영상을 얻을 수 있다. 기존 방법으로 해결하지 못하는 복잡한 상황하에서의 좋은 초기 배경영상을 생성하므로, 차량으로 탐지되지 않는 영상의 부분영역만을 배경생성 과정에 참여시키는 기존의 배경생성 방법에 이 방법을 사용할 경우, 복잡한 상황에서도 견고하게 차량 탐지를 할 수 있는 배경영상을 생성할 수 있다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.4
• /
• pp.297-304
• /
• 2015

The change detection algorithms, based on remotely sensed satellite imagery, can be applied to various applications, such as the hazard/disaster analysis and the land monitoring. However, unchanged areas sometimes detected as the changed areas due to various errors in relief displacements and noise pixels, included in the original multi-temporal dataset at the application of unsupervised change detection algorithm. In this research, the object-based changed detection for the high-spatial resolution satellite images is applied by using the IR-MAD (Iteratively Reweighted- Multivariate Alteration Detection), which is one of those representative change detection algorithms. In additionally, we tried to increase the accuracy of change detection results with using the additional information, based on the cross-sharpening method. In the experiment, we used the KOMPSAT-2 satellite sensor, and resulted in the object-based IR-MAD algorithm, representing higher changed detection accuracy than that by the pixel-based IR-MAD. Also, the object-based IR-MAD, focused on cross-sharpened images, increased in accuracy of changed detection, compared to the original object-based IR-MAD. Through these experiments, we could conclude that the land monitoring and the change detection with the high-spatial-resolution satellite imagery can be accomplished efficiency by using the object-based IR-MAD algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.919-921
• /
• 2013

영상시장의 개척과 디지털 기술의 발전과 더불어 차세대 3D 입체영상기술에 대한 관심과 수요가 증가하고 있다. 입체 정보는 크게 '단안 입체 정보(monoscopic depth cue)'와 '양안 입체 정보(stereoscopic depth cue)'로 분류 할 수 있다. 단안 입체 정보는 은폐, 상대적 크기, 상대적 밀도, 시야 안의 높이, 공기투시, 운동투시, 초점조절인 7가지로 경험에 의한 입체감을 지각하는 것을 말하며 양안 입체 정보는 두 눈으로 볼 때 처음으로 깊이를 지각 할 수 있는 것으로 크게 '동시시(simultaneous perception)', '융합(sensory fusion)', '입체시(stereoscopic vision)'의 세종류의 기능으로 분류한다. 3D 촬영은 이 양안시의 원리를 이용하여 두 대의 카메라의 좌우 영상을 합성하여 깊이감 있는 영상을 만들어 내게 된다. 본 논문에서는 3D 촬영방법은 촬영방식에 따라 크게 평행방식, 직교방식, 교차방식이 있는데 이중 중 원거리 촬영에 유리한 교차방식을 활용하여 사이드 바이 사이드 리그(Rig; 카메라를 수평으로 설치할 수 있도록 만들어진 장치)를 원거리 촬영에 맞게 축간거리를 기존의 리그 사이즈보다 2배 이상 긴 리그를 제작하여 보다 먼 거리에서의 상이한 좌우 영상획득이 가능하도록 설계하였다. 또한, 일정한 간격에 따라 피사체를 촬영하면서 거리에 따른 양 카메라의 가장 이상적인 IOD(Interocular Distance)와 CONV(Convergence)를 찾고, 교차방식촬영에 따른 특징적인 아티팩트인 키스톤 왜곡(Keystone distance)의 보정을 통한 원거리 입체영상을 효과적으로 획득하는데 본 연구방법을 제안하고자 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.660-662
• /
• 2023

3차원 포인트 클라우드의 의미적 분할은 환경을 구성하는 물체 단위로 포인트 클라우드를 분할하는 작업으로서, 환경의 3차원적 구성을 이해하고 환경과 상호작용에 필수적인 시각 지능을 요구한다. 본 논문에서는 포인트 클라우드에서 추출하는 3차원 기하학적 특징과 함께 멀티-뷰 영상에서 추출하는 2차원 시각적 특징들도 활용하는 새로운 3차원 포인트 클라우드 의미적 분할 모델 MFNet을 제안한다. 제안 모델은 서로 이질적인 2차원 시각적 특징과 3차원 기하학적 특징의 효과적인 융합을 위해, 새로운 중기 융합 전략과 멀티-모달 교차 주의집중을 이용한다. 본 논문에서는 ScanNetV2 벤치마크 데이터 집합을 이용한 다양한 실험들을 통해, 제안 모델 MFNet의 우수성을 입증한다.

• Journal of the Korea Convergence Society
• /
• v.8 no.5
• /
• pp.19-28
• /
• 2017

In this paper, we propose a method to reduce the difference in image characteristics when multiple camera images are captured for virtual space production. Sixty-four images were used by cross-mounting eight bodies and lenses, respectively. Image analysis compares and analyzes the standard deviation of the histogram and pixel distribution values. As a result of the analysis, it shows different image characteristics depending on the lens or image sensor, though it is a camera of the same model. In this paper, we have adjusted the distribution of the overall brightness value of the image to compensate for this difference. As a result, the average deviation was the maximum of (Indoor: 6.89, outdoor: 24.23), we obtained images with almost no deviation (Indoor: maximum 0.42, outdoor: maximum: 2.73). In the future, we will study and apply more accurate image analysis methods than image brightness distribution.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.843-847
• /
• 2023

In this paper, we propose a model for detecting efficient fire identification to prevent fires that can lead to various industrial accidents, farmland and large forest fires, with the widespread use of various chemicals and flammable substances as modern technology advances. This paper presents an algorithm that can detect fire smoke in a high-efficiency and short time using images, and an algorithm based on SVM(Support Vector Machine) and K fold cross-verification technologies. By analyzing images, fire and smoke detection algorithms have relatively superior detection performance compared to existing algorithms, and the analysis of fire and smoke characteristics detected in this paper is analyzed stably and efficiently and is expected to be used in various fields that may be exposed to fire risks in the future.

• Journal of the Korea Convergence Society
• /
• v.10 no.9
• /
• pp.23-29
• /
• 2019

In this paper, the mood of images was classified into eight categories through a deep convolutional neural network and video was automatically generated using proper background music. Based on the collected image data, the classification model is learned using a multilayer perceptron (MLP). Using the MLP, a video is generated by using multi-class classification to predict image mood to be used for video generation, and by matching pre-classified music. As a result of 10-fold cross-validation and result of experiments on actual images, each 72.4% of accuracy and 64% of confusion matrix accuracy was achieved. In the case of misclassification, by classifying video into a similar mood, it was confirmed that the music from the video had no great mismatch with images.

• Journal of the Institute of Convergence Signal Processing
• /
• v.15 no.4
• /
• pp.128-134
• /
• 2014

In this paper, we propose a detection method of the leukocyte motions in a microvessel by using spatiotemporal image analysis. The leukocyte motions that adhere to blood vessel walls can be visualized to move along the blood vessel wall's contours in a sequence of images. In this proposal method, we use the constraint that the leukocytes move along the blood vessel wall's contours and detect the leukocyte motions by using the spatiotemporal image analysis method. The generated spatiotemporal image is processed by a special-purpose orientation-selective filter and then subsequent grouping processes are done. The subsequent grouping processes select and group the leukocyte trace segments among all the segments obtained by simple thresholding and skeletonizing operations. Experimental results show that the proposed method can stably detect the leukocyte motions even when multiple leukocyte traces intersect each other.