• Journal of IKEEE
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• v.14 no.4
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• pp.317-323
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• 2010

The video surveillance system is adopted in many places due to its efficiency and constancy in monitoring a specific area over a long period of time. However, many surveillance systems composed of a single static camera often produce unsatisfactory results due to their lack of field of view. In this paper, we present a video surveillance system based on wide baseline stereo cameras to overcome the limitation. We adopt the codebook algorithm and mathematical morphology to robustly model the foreground pixels of the moving object in the scene and calculate the trajectory of the moving object via 3D reconstruction. The experimental results show that the proposed system detects a moving object and generates a top view trajectory successfully to track the location of the object in the world coordinates.

• Journal of Korean Neurosurgical Society
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• v.51 no.2
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• pp.120-123
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• 2012

Objective : Percutaneous vertebroplasty (PVP) is usually carried out under three-dimensional (2D) fluoroscopic guidance. However, operative complications or bone cement distribution might be difficult to assess on the basis of only 2D radiographic projection images. We evaluated the feasibility of performing an intraoperative and postoperative examination in patients undergoing PVP by using three-dimensional (3D) reconstructive C-arm. Methods : Standard PVP procedures were performed on 14 consecutive patients by using a Siremobil Iso-$C^{3D}$ and a multidetector computed tomography machine. Post-processing of acquired volumetric datasets included multiplanar reconstruction (MPR) and surface shaded display (SSD). We analyzed intraoperative and immediate postoperative evaluation of the needle trajectory and bone cement distribution. Results : The male : female ratio was 2 : 12; mean age of patients, 70 (range, 77-54) years; and mean T score, -3.4. The mean operation time was 52.14 min, but the time required to perform and post-process the rotational acquisitions was 7.76 min. The detection of bone cement distribution and leakage after PVP by using MPR and SSD was possible in all patients. However, detection of the safe trajectory for needle insertion was not possible. Conclusion : 3D rotational image acquisition can enable intra- or post-procedural assessment of vertebroplasty procedures for the detection of bone cement distribution and leakage. However, it is difficult to assess the safe trajectory for needle insertion.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.472-473
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• 2013

The growth in number and efficiency of smart devices such as GPS enabled smart phones and PDAs present an unparalleled opportunity for diverse areas of life. However extraction of GPS traces for provision of services demand a huge storage space as well as computation overhead. This is a challenging task especially for the applications which provide runtime services. In this paper we provide a simplified model to extract GPS traces of moving objects at runtime. Road segment partitioning and measure of deviation in angle of trajectory path is incorporated to identify the significant data points. The number of these data points is minimized by our proposed approach in an efficient manner to overwhelm the storage and computation overhead. Further, the competent reconstruction of complete itinerary based on gathered data, is also ensured by proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.321-340
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• 2016

The Compressive Video Sensing (CVS) is a useful technology for wireless systems requiring simple encoders but handling more complex decoders, and its rate-distortion performance is highly affected by the quantization of measurements and reconstruction of video frame, which motivates us to presents the Space-Time Quantization (ST-Q) and Motion-Aligned Reconstruction (MA-R) in this paper to both improve the performance of CVS system. The ST-Q removes the space-time redundancy in the measurement vector to reduce the amount of bits required to encode the video frame, and it also guarantees a low quantization error due to the fact that the high frequency of small values close to zero in the predictive residuals limits the intensity of quantizing noise. The MA-R constructs the Multi-Hypothesis (MH) matrix by selecting the temporal neighbors along the motion trajectory of current to-be-reconstructed block to improve the accuracy of prediction, and besides it reduces the computational complexity of motion estimation by the extraction of static area and 3-D Recursive Search (3DRS). Extensive experiments validate that the significant improvements is achieved by ST-Q in the rate-distortion as compared with the existing quantization methods, and the MA-R improves both the objective and the subjective quality of the reconstructed video frame. Combined with ST-Q and MA-R, the CVS system obtains a significant rate-distortion performance gain when compared with the existing CS-based video codecs.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1162-1164
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• 2003

The methods of the geometric reconstruction and sensor calibration of satellite linear pushbroom images are investigated. The model of the sensor used is based on the SPOT model that is developed by Kraiky. The satellite trajectory is a Keplerian trajectory in the approximation. Four orbit parameters, longitude of the ascending node(${\omega}$), inclination of the orbit plan(I), latitude argument of the satellite(W) and distance between earth center and satellite, are used for the camera modeling. Time-dependent orbit parameters are expressed by quadratic polynomials. SPOT-5 images have been used for validation tests. The results are that the RMSE acquired from 20 GCPs is 1.763m and the RMSE of 5 checking points 2.470m. Because the ground resolution of SPOT-5 is 2.5m, the result obtained in this study has a good accuracy. It demonstrates that the sensor model developed by this study can be used to reconstruct the geometry of satellite image using pushbroom camera.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.190-197
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• 2020

Recently there was an incident that military radars, coastal CCTVs and other surveillance equipment captured a small rubber boat smuggling a group of illegal immigrants into South Korea, but guards on duty failed to notice it until after they reached the shore and fled. After that, the detection of such vessels before it reach to the Korean shore has emerged as an important issue to be solved. In the fields of marine navigation, Automatic Identification System (AIS) is widely equipped in vessels, and the vessels incessantly transmits its position information. In this paper, we propose a method of automatically identifying abnormally behaving vessels with AIS using convolutional autoencoder (CAE). Vessel anomaly detection can be referred to as the process of detecting its trajectory that significantly deviated from the majority of the trajectories. In this method, the normal vessel trajectory is gridded as an image, and CAE are trained with images from historical normal vessel trajectories to reconstruct the input image. Features of normal trajectories are captured into weights in CAE. As a result, images of the trajectories of abnormal behaving vessels are poorly reconstructed and end up with large reconstruction errors. We show how correctly the model detects simulated abnormal trajectories shifted a few pixel from normal trajectories. Since the proposed model identifies abnormally behaving ships using actual AIS data, it is expected to contribute to the strengthening of security level when it is applied to various maritime surveillance systems.

• Journal of Biomedical Engineering Research
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• v.44 no.4
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• pp.255-263
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• 2023

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

• Journal of the Korean Society of Radiology
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• v.14 no.4
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• pp.477-486
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• 2020

The purpose of this study was to evaluate the effects of reconstruction filters, X-ray source trajectories and intervals in the quality of digital tomosynthesis (DT) images, and the results was clinically validated. The filtered back-projection was implemented by using Ramp, Shepp-Logan, Cosine, Hamming, Hann and Blackman filters, and the X-ray source trajectories were simulated with 1 × 36, 2 × 18, 3 × 12, 4 × 9 and 6 × 6 arrays. The X-ray source intervals were 5, 10, 20, 30 and 40 mm. The depth resolution, spatial resolution and noise of DT image were evaluated by measuring artifact spread function (ASF), full width at half maximum (FWHM) and signal-to-noise ratio (SNR), respectively. The results showed that the spatial resolution and noise properties of DT images were maximized by the Ramp and Blackman filters, respectively, and the depth resolution and noise properties of the DT images obtained with a 1 × 36 X-ray source trajectory were superior to the other trajectories. The depth resolution and noise properties of DT images improved with an increase of X-ray source intervals, and the high X-ray source intervals degraded the spatial resolution of DT images. Therefore, the characteristics of DT images are highly dependent on reconstruction filters, X-ray source trajectories and intervals, and it is necessary to use optimal imaging parameters in accordance with diagnostic purpose.

• Journal of Korean Society of Transportation
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• v.29 no.1
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• pp.125-134
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• 2011

In order to reconstruct vehicle-pedestrian collision accidents, this paper presents a fuzzy tool to estimate accurately the impact velocity of the vehicle using parameters which could be easily collectable at the accident scene. The fuzzy rules and membership functions were set up using number of over 200 domestic and foreign data from accidents and empirical tests and 700 data from multibody simulation experiments. The developed fuzzy tool deduces the category of pedestrian trajectory and impact speed of the vehicle using 4 membership functions and 2 logic rules. The membership function of throw distance was differently set according to the deduced category of trajectories. The implemented fuzzy program was validated through comparing with the domestic and foreign empirical data. The output results agree very well in impact velocities of vehicle resulting the accuracy and usefulness of the developed tool in the reconstruction analysis of vehicle-pedestrian collision accidents.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.309-312
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• 2016

최근 실세계에 존재하는 물체의 3차원 형상과 색상을 디지털화하는 3차원 객체 복원에 대한 관심이 날로 증가하고 있다. 3차원 객체 복원은 영상 획득, 영상 보정, 점군 획득, 반복적 점군 정합, 무리 조정, 3차원 모델 표현과 같은 단계를 거처 통합된 3차원 모델을 생성한다. 그 중 반복적 점군 정합 방법은 카메라 궤적의 초기 값을 획득하는 방법으로서 무리 조정 단계에서 전역 최적 값으로의 수렴을 보장하기 위해 중요한 단계이다. 기존의 반복적 점군 정합 (iterative closest points) 방법에서는 시간이 지남에 따라 누적된 궤적 오차 때문에 발생하는 객체 표류 문제가 발생한다. 본 논문에서는 이 문제를 해결하기 위해 색상 영상에서 SIFT 특징점을 획득하고 3차원 점군을 얻은 뒤 가중치를 부여함으로써 점 군 간의 더 정확한 정합을 수행한다. 실험결과에서 기존의 방법과 비교하여 제안하는 방법이 절대 궤적 오차 (absolute trajectory error)가 감소하는 것을 확인 했고 복원된 3차원 모델에서 객체 표류 현상이 줄어드는 것을 확인했다.