• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1416-1426
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• 2021

In the case of a scanning type multi-channel LiDAR sensor, the distance error called a walk error may occur due to a difference in received signal power. This work error causes different distance values to be output for the same object when scanning the surrounding environment based on multiple LiDAR sensors. For minimizing walk error in overlapping regions when scanning all directions using multiple sensors, to calibrate distance for each channels using convolution on external system. Four sensors were placed in the center of 6×6 m environment and scanned around. As a result of applying the proposed filtering method, the distance error could be improved by about 68% from average of 0.5125 m to 0.16 m, and the standard deviation could be improved by about 48% from average of 0.0591 to 0.030675.

• 한국정보통신설비학회:학술대회논문집
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• 2007.08a
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• pp.386-389
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• 2007

TV technology started from black and white TV. Color TV invented and users request more realistic TV technology. The next technology is 3DTV. For 3DTV, 3D display technology, 3D coding technology, digital mux/demux technology in broadcast and 3D video acquisition are needed. Moreover, Almost every contents now exist are 2D contents. It causes necessity to convert from 2D to 3D. This article describes 2D/3D conversion algorithm and H/W platform on FPGA board. Time difference makes 3D effect and convolution filter increased the effect. Distorted image and original image give 3D effect. The algorithm is shown on 3D display. The display device shows 3D effect by parallax barrier method and has FPGA board.

• The Journal of the Korea Contents Association
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• v.8 no.4
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• pp.17-29
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• 2008

In Computer Graphics, fluid dynamics is used for animating and expressing the various special effects of water. As the hardware performance is getting higher, the several algorithms for fluid dynamics become to be executed in real time. However, it still requires a lot of computational time to get the realistic and detailed results. Therefore, there are many researches on the techniques of balancing between performance and quality. It must give priority to the executive performance preserving the visual reality even though sacrificing the physical reality, specially in applications with the game context which need to express the interaction between 3D objects and the surface of the water such as the sea or a lake. In this paper, we propose a method for the realtime animation of interactions between 3D objects and the surface of the water using the linear convolution of height fields and the bounding spheres of object.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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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.

• Journal of Advanced Navigation Technology
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• v.15 no.6
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• pp.1068-1074
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• 2011

In this paper, the proposed scheme is STBC(space-time block codes) system combined with convolution code which is the most popular channel coding to ensure the reliability of data transmission for a high data rate wireless communication. The STBC is one of MIMO(multi-input multi-output) techniques. In addition, this scheme uses a modified viterbi algorithm in order to get a high system gain when data is transmitted. Because we combine STBC and convolution code, the proposed scheme has a little high quantity of computation but it can get a maximal diversity gain of STBC and a high coding gain of convolution code at the same time. Unlike existing viterbi docoding algorithm using Hamming distance in order to calculate branch matrix, the modified viterbi algorithm uses Euclidean distance value between received symbol and reference symbol. Simulation results show that the modified viterbi algorithm improved gain 7.5 dB on STBC 2Tx-2Rx at $BER=10^{-2}$. Therefore the proposed scheme using STBC combined with convolution code can improve the transmission reliability and transmission efficiency.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.686-689
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• 2018

3D 손 포즈 추정(Hand Pose Estimation, HPE)은 스마트 인간 컴퓨터 인터페이스를 위해서 중요한 기술이다. 이 연구에서는 딥러닝 방법을 기반으로 하여 단일 RGB-Depth 카메라로 촬영한 양손의 3D 손 자세를 실시간으로 인식하는 손 포즈 추정 시스템을 제시한다. 손 포즈 추정 시스템은 4단계로 구성된다. 첫째, Skin Detection 및 Depth cutting 알고리즘을 사용하여 양손을 RGB와 깊이 영상에서 감지하고 추출한다. 둘째, Convolutional Neural Network(CNN) Classifier는 오른손과 왼손을 구별하는데 사용된다. CNN Classifier 는 3개의 convolution layer와 2개의 Fully-Connected Layer로 구성되어 있으며, 추출된 깊이 영상을 입력으로 사용한다. 셋째, 학습된 CNN regressor는 추출된 왼쪽 및 오른쪽 손의 깊이 영상에서 손 관절을 추정하기 위해 다수의 Convolutional Layers, Pooling Layers, Fully Connected Layers로 구성된다. CNN classifier와 regressor는 22,000개 깊이 영상 데이터셋으로 학습된다. 마지막으로, 각 손의 3D 손 자세는 추정된 손 관절 정보로부터 재구성된다. 테스트 결과, CNN classifier는 오른쪽 손과 왼쪽 손을 96.9%의 정확도로 구별할 수 있으며, CNN regressor는 형균 8.48mm의 오차 범위로 3D 손 관절 정보를 추정할 수 있다. 본 연구에서 제안하는 손 포즈 추정 시스템은 가상 현실(virtual reality, VR), 증강 현실(Augmented Reality, AR) 및 융합 현실 (Mixed Reality, MR) 응용 프로그램을 포함한 다양한 응용 분야에서 사용할 수 있다.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.119-120
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• 2002

The aim is to urge the need of elaborate commissioning of 3D RTP system from the firsthand experience. A 3D RTP system requires so much data such as beam data and patient data. Most data of radiation beam are directly transferred from a 3D dose scanning system, and some other data are input by editing. In the process inputting parameters and/or data, no error should occur. For RTP system using algorithm-bas ed-on beam-modeling, careless beam-data processing could also cause the treatment error. Beam data of 3 different qualities of photon from two linear accelerators, patient data and calculated results were commissioned. For PDD, the doses by Clarkson, convolution, superposition and fast superposition methods at 10 cm for 10${\times}$10 cm field, 100 cm SSD were compared with the measured. An error in the SCD for one quality was input by the service engineer. Whole SCD defined by a physicist is SAD plus d$\sub$max/, the value was just SAD. That resulted in increase of MU by 100${\times}$((1_d$\sub$max//SAD)$^2$-1)%. For 10${\times}$10 cm open field, 1 m SSD and at 10 cm depth in uniform medium of relative electron density (RED) 1, PDDs for 4 algorithms of dose calculation, Clarkson, convolution, superposition and fast-superposition, were compared with the measured. The calculated PDD were similar to the measured. For 10${\times}$10 cm open field, 1 m SSD and at 10 cm depth with 5 cm thick inhomogeneity of RED 0.2 under 2 cm thick RED 1 medium, PDDs for 4 algorithms were compared. PDDs ranged from 72.2% to 77.0% for 4 MV X-ray and from 90.9% to 95.6% for 6 MV X-ray. PDDs were of maximum for convolution and of minimum for superposition. For 15${\times}$15 cm symmetric wedged field, wedge factor was not constant for calculation mode, even though same geometry. The reason is that their wedge factor is considering beam hardness and ray path. Their definition requires their users to change the concept of wedge factor. RTP user should elaborately review beam data and calculation algorithm in commissioning.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.3
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• pp.659-666
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• 2015

In this paper, we propose a novel reconstruction method of spatially filtered 3D images in integral imaging based on parallel lens array. The parallel lens array is composed of two lens arrays, which are positioned side by side through longitudinal direction. Conventional spatial filtering method by using convolution property between periodic functions has drawback that is the limitation of the position of target object. this caused the result that the target object should be located on the low depth resolution region. The available spatial filtering region of the spatial filtering method is depending on the focal length and the number of elemental lens in the integral imaging pickup system. In this regard, we propose the parallel lens array system to enhance the available spatial filtering region and depth resolution. The experiment result indicate that the proposed method outperforms the conventional method.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.230-236
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• 2018

3D depth perception has played an important role in robotics, and many sensory methods have also proposed for it. As a photodetector for 3D sensing, single photon avalanche diode (SPAD) is suggested due to sensitivity and accuracy. We have researched for applying a SPAD chip in our fusion system of time-of-fight (ToF) sensor and stereo camera. Our goal is to upsample of SPAD resolution using RGB stereo camera. Currently, we have 64 x 32 resolution SPAD ToF Sensor, even though there are higher resolution depth sensors such as Kinect V2 and Cube-Eye. This may be a weak point of our system, however we exploit this gap using a transition of idea. A convolution neural network (CNN) is designed to upsample our low resolution depth map using the data of the higher resolution depth as label data. Then, the upsampled depth data using CNN and stereo camera depth data are fused using semi-global matching (SGM) algorithm. We proposed simplified fusion method created for the embedded system.