• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2004년도 Asia Display / IMID 04
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• pp.149-152
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• 2004

A new joint source channel coding reduces both input-width and average current consumption to transmit image data to LCD source drivers. As a source coding, it is based on entropy coding of differential pulse code modulation scheme, especially using median edge detector of image predictor. As a channel coding, it is not a simple pulse amplitude modulation, but linked by source entropy to reduce average amplitude. Simulation results show 1/4 width is achievable by 16-valued transmission with keeping conventional current consumption (0.36 to 1.3).

• Journal of Biosystems Engineering
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• 제35권3호
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• pp.197-205
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• 2010

An automatic echelon feeder of brown rice was presented for quality inspection system using color image processing. A echelon feeder was developed with vibratory feeder and cylindrical indent singulator having oblique light. The vibratory feeder consisted of a hopper, electromagnetic vibrator and multichannel grooves and supply the grain sample to the singulator. The feeding performance such as feed rate, blocking frequency of the channel was dependent on the size of groove and vibration pattern. A cylindrical indent singulator consisted of a rotating cylinder, prisms and a tungsten-halogen light source. It delivered grain kernels under the camera in a echelon form and illuminate the kernels with oblique ray and ambient light. The size of the indents installed on the surface of the rotating cylinder was determined by the dimensions of the paddy and a small triangular prism was placed in each indent to apply $ 20^{\circ}$ oblique light to the grain kernel.

• Journal of Multimedia Information System
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• 제8권4호
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• pp.203-210
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• 2021

Deep leaning convolutional neural networks (CNN) have successfully been applied to image super-resolution (SR). Despite their great performances, SR techniques tend to focus on a certain upscale factor when training a particular model. Algorithms for single model multi-scale networks can easily be constructed if images are upscaled prior to input, but sub-pixel convolution upsampling works differently for each scale factor. Recent SR methods employ multi-scale and multi-path learning as a solution. However, this causes unshared parameters and unbalanced parameter distribution across various scale factors. We present a multi-scale single-path upsample module as a solution by exploiting the advantages of sub-pixel convolution and interpolation algorithms. The proposed model employs sub-pixel convolution for the highest scale factor among the learning upscale factors, and then utilize 1-dimension interpolation, compressing the learned features on the channel axis to match the desired output image size. Experiments are performed for the single-path upsample module, and compared to the multi-path upsample module. Based on the experimental results, the proposed algorithm reduces the upsample module's parameters by 24% and presents slightly to better performance compared to the previous algorithm.

• 대한전자공학회논문지SP
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• 제47권5호
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• pp.18-24
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• 2010

본 논문에서는 돌비 프로로직 II/IIx를 대체하기 위한 가상 음원 위치 정보 기반의 새로운 메트릭스 디코더 시스템을 제안하고자 한다. 제안하는 신규 메트릭스 디코더는 역행렬 계산을 통해 얻어지는 수동 메트릭스 디코딩부와 수동 메트릭스 디코딩을 통해서 얻은 신호들을 멀티채널 신호의 채널간 이미지 특성에 따라서 적응적으로 가변시키는 능동 메트릭스 디코딩부로 구성된다. 멀티채널 환경에서 채널 간에 형성되는 다수의 이미지는 실제 청각 시스템에 의해서 인지되어 만들어지는 가상의 사운드 이벤트와 연결이 되어 있다. 따라서 이 이미지의 위치와 크기에 기반하여 멀티채널 신호를 적응적으로 가변시키면, 인지적인 관점에서 우수한 성능의 메트릭스 디코더를 설계할 수 있다. 더불어 채널간 분리도를 향상시키기 위해서 비선형 삼각함수의 조합을 사용하였다.

• 한국광학회지
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• 제15권4호
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• pp.299-308
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• 2004

공액상 없이 여러 가지 물체를 동시에 재생 가능한 다채널 다단계 위상형 컴퓨터-생성 홀로그램(CGH)을 설계할 수 있는 보다 효과적인 부호화 방법을 제안하였다. 다채널 CGH 패턴을 설계하기 위하여 pixel oriented CGH 제작방식을 이용하였다. 설계된 CGH 패턴의 성능평가를 위해 양자화 위상 단계수에 따른 여러 가지 다채널 CGH들의 회절효율(η), 평균제곱에러(MSE) 및 신호 대 잡음비(SNR) 등의 변화를 살펴보았다. 일반적으로 CGH에 기록되는 물체 수가 증가할수록 CGH의 재생품질은 떨어진다. 그러나 회절효율의 경우 1채널 CGH가 70%이고 제안한 부호화 방법으로 설계한 2채널, 4채널, 8채널 CGH들은 각각 62%, 62%, 63%로 채널수가 증가하여도 큰 차이가 없음을 컴퓨터 모의실험을 통하여 확인할 수 있었다. 또한 렌즈로 결합되어 있는 PAL-SLM과 XGA형 LCD 그리고 이에 빛을 조명하는 LD 등으로 구성되어 있는 액정 공간 광 위상 변조기를 사용하여 광학적으로 CGH를 구현하여 입력영상을 재생$.$고찰해 보았다.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2006년도 제33회 추계학술대회 발표논문집
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• pp.100-100
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• 2006

• 전자공학회논문지
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• 제49권9호
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• pp.43-48
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• 2012

본 논문에서는 다중 접합 기반 수신기 (MPDR)의 영상 제거 성능을 향상시키는 반복적인 단일주파수 CW 신호 기반 I/Q 신호 재생 알고리즘을 제안한다. 이 논문에서는 MPDR 수신기의 I/Q 신호 재생이 직접변환수신기의 I/Q 부정합 보상임을 보인다. 분석을 바탕으로 I/Q 재생의 정확도를 영상 제거비로써 평가한다. 제안한 방법은 기존의 I/Q 신호 재생 방법에 비해서 20dB 이상 영상 제거비를 향상시킨다. 모의실험 결과는 제안한 방법을 이용한 MPDR 수신기의 영상 제거비가 70dB 이상임을 보이고, 비트오율 성능은 페이딩 채널 환경에서 조차도 일반적인 동기 수신기의 성능과 거의 같음을 보인다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권7호
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• Journal of Information Processing Systems
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• 제13권3호
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.

• Current Optics and Photonics
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• 제7권3호
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.