• Title/Summary/Keyword: Stochastic Image Modeling

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Robust Lane Detection Algorithm for Autonomous Trucks in Container Terminal

  • Ngo Quang Vinh;Sam-Sang You;Le Ngoc Bao Long;Hwan-Seong Kim
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2023.05a
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    • pp.252-253
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    • 2023
  • Container terminal automation might offer many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A robust lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure lane positioning robustness. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions and tested the robustness of the lane detection method with stochastic noise.

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Adaptive Digital Watermarking using Stochastic Image Modeling Based on Wavelet Transform Domain (웨이브릿 변환 영역에서 스토케스틱 영상 모델을 이용한 적응 디지털 워터마킹)

  • 김현천;권기룡;김종진
    • Journal of Korea Multimedia Society
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    • v.6 no.3
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    • pp.508-517
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    • 2003
  • This paper presents perceptual model with a stochastic multiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embeds at the texture and edge region for more strongly embedded watermark by the SSQ. The watermark embedding is based on the computation of a NVF that has local image properties. This method uses non- stationary Gaussian and stationary Generalized Gaussian models because watermark has noise properties. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model uses the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark 3.1 benchmark test.

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Adaptive Image Watermarking Using a Stochastic Multiresolution Modeling

  • Kim, Hyun-Chun;Kwon, Ki-Ryong;Kim, Jong-Jin
    • Proceedings of the IEEK Conference
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    • 2002.07a
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    • pp.172-175
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    • 2002
  • This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.

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Improved Super-Resolution Algorithm using MAP based on Bayesian Approach

  • Jang, Jae-Lyong;Cho, Hyo-Moon;Cho, Sang-Bock
    • Proceedings of the KIEE Conference
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    • 2007.04a
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    • pp.35-37
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    • 2007
  • Super resolution using stochastic approach which based on the Bayesian approach is to easy modeling for a priori knowledge. Generally, the Bayesian estimation is used when the posterior probability density function of the original image can be established. In this paper, we introduced the improved MAP algorithm based on Bayesian which is stochastic approach in spatial domain. And we presented the observation model between the HR images and LR images applied with MAP reconstruction method which is one of the major in the SR grid construction. Its test results, which are operation speed, chip size and output high resolution image Quality. are significantly improved.

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Direct Depth and Color-based Environment Modeling and Mobile Robot Navigation (스테레오 비전 센서의 깊이 및 색상 정보를 이용한 환경 모델링 기반의 이동로봇 주행기술)

  • Park, Soon-Yong;Park, Mignon;Park, Sung-Kee
    • The Journal of Korea Robotics Society
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    • v.3 no.3
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    • pp.194-202
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    • 2008
  • This paper describes a new method for indoor environment mapping and localization with stereo camera. For environmental modeling, we directly use the depth and color information in image pixels as visual features. Furthermore, only the depth and color information at horizontal centerline in image is used, where optical axis passes through. The usefulness of this method is that we can easily build a measure between modeling and sensing data only on the horizontal centerline. That is because vertical working volume between model and sensing data can be changed according to robot motion. Therefore, we can build a map about indoor environment as compact and efficient representation. Also, based on such nodes and sensing data, we suggest a method for estimating mobile robot positioning with random sampling stochastic algorithm. With basic real experiments, we show that the proposed method can be an effective visual navigation algorithm.

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Adaptive Watermarking Using Successive Subband Quantization and Perceptual Model Based on Mukiwavelet Transform (멀티웨이브릿 변환 기반에서 연속 부대역 양자화 및 지각 모델을 이용한 적응 워터마킹 기술)

  • 권기룡;강균호;조영웅;문광석;이준재
    • Proceedings of the IEEK Conference
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    • 2002.06d
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    • pp.121-124
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    • 2002
  • This paper presents an adaptive digital image watermarking scheme that uses successive subband quantization (SSQ) and perceptual modeling. Our approach performs a multiwavelet transform to determine the local image properties optimal and the watermark embedding location. The multiwavelet used in this paper is the DGHM multiwavelet with approximation order 2 to reduce artifacts in the reconstructed image. A watermark is embedded into the perceptually significant coefficients (PSC) of the image in each subband. The PSCs in high frequency subbands are selected by setting the thresholds to one half of the largest coefficient in each subband. After the PSCs in each subband are selected, a perceptual model is combined with a stochastic approach based on the noise visibility function to produce the final watermark.

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A Double Z-buffer Antialiasing Method for Voxelized Implicit Surfaces (복셀로 표현된 임플리시트 곡면을 위한 시프트(shifted) 더블 Z-버퍼 앤티 앨리어싱)

  • 김학란;박화진
    • Journal of Korea Multimedia Society
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    • v.7 no.1
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    • pp.44-53
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    • 2004
  • This paper aims at presenting high quality at low resolution apply by a new antialiasing method for voxelized implicit surfaces. Implicit surfaces create a unique type of 3D-modeling. Some use of implicit surfaces are scientific and medical visualization, animation, medical simulation and interactive modeling. One of previous antialiasing methods for implicit surfaces presented by raytracing or texture mapping is making use of a stochastic sampling. But this method requires more calculation time and costs which is caused by complicated and difficult implicit functions. In the meanwhile, voxelized implicit surfaces generally use high resolution for good quality images but it costs to generate. In order to this problem, this paper suggests a shifted double Z-buffer which is very simple, more efficient and easy. Tn addition, there are applied box-filter and tent-filter to the double Z-buffer antialiasing method for better images. For results this method generate high quality image and it is easy to apply to various filters and is able to extend to multi Z-buffer.

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Content Adaptive Watermarking Using a Stochastic Image Modeling Based on Wavelet Transform Domain (웨이브릿 변환 영역에서 스토케스틱 영상 모델을 이용한 내용기반 적응 워터마킹)

  • 김현천;강균호;권기룡;김종진
    • Proceedings of the Korea Multimedia Society Conference
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    • 2002.11b
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    • pp.283-286
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    • 2002
  • 본 논문에서는 보다 효과적이고 강인한 워터마크 은닉을 위한 방법으로 웨이브릿 변환 영역에서 영상의 통계적 특성에 기초한 비정상상태(non-stationary)에서와 정상상태(stationary) 일반화 가우스(generalized Gaussian: GG)모델을 이용한 적응 워터마크 은닉 기술을 제안한다. 워터마크는 고주파 영역에서 연속 부대역 양자화(successive subband quantization: SSQ)를 이용하여 다해상도 영상의 웨이브릿 계수 중에서 시각적 중요 계수(perceptual significant coefficients: PSC)를 선택하여 삽입한다. 워터마크 은닉을 위한 지각 모델은 NVF(noise visibility function)함수에 의해 계산된다. 이것은 비정상상태와 정상상태의 통계적 특성을 이용하고, 국부영상 특성을 가진다. 은닉모델은 다해상도내의 각 부대역별 분산과 형상계수(shape parameter)를 사용한다. Stirmark benchmark test에 근거하여 여러 가능한 왜곡에 대한 실험에서 강인성과 비가시성에서의 우수함을 확인하였고, 비정상상태의 경우와 정상상태의 경우를 비교하였다.

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