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A Fast nonparametric sampling Method for Time to Event in Individual-Level Simulation Models

MEDICAL DECISION MAKING 2025年第2期45卷 205-213页

作者： Garibay-Trevino, David U. Jalal, Hawre Alarid-Escudero, Fernando Univ Ottawa Fac Med Sch Epidemiol & Publ Hlth 600 Peter Morand 301 Ottawa ON K1G 5Z3 Canada Stanford Univ Sch Med Dept Hlth Policy Stanford CA USA Stanford Univ Freeman Spogli Inst Ctr Hlth Policy Stanford CA USA

Purpose. Individual-level simulation models often require sampling times to events;however, efficient parametric distributions for many processes may often not exist. For example, time to death from life tables cannot be accurately sampled from existing parametric distributions. We propose an efficient nonparametric method to sample times to events that does not require any parametric assumption on the hazards. Methods. We developed a nonparametric sampling (NPS) approach that simultaneously draws multiple time-to-event samples from a categorical distribution. This approach can be applied to univariate and multivariate processes. We discretize the entire period into equal-length time intervals and then derived the interval-specific probabilities. The times to events can then be used directly in individual-level simulation models. We compared the accuracy of our approach in sampling time-to-events from common parametric distributions, including exponential, gamma, and Gompertz. In addition, we evaluated the method's performance in sampling age to death from US life tables and sampling times to events from parametric baseline hazards with time-dependent covariates. Results. The NPS method estimated similar expected times to events from 1 million draws for the 3 parametric distributions, 100,000 draws for the homogenous cohort, 200,000 draws from the heterogeneous cohort, and 1 million draws for the parametric distributions with time-varying covariates, all in less than 1 second. Conclusion. Our method produces accurate and computationally efficient samples for time to events from hazards without requiring parametric assumptions.

关键词： discrete event simulation time to event non-parametric sampling multivariate categorical sampling nonhomogeneous poisson point process (NHPPP)

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Side scan sonar image segmentation and synthesis based on extreme learning machine

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APPLIED ACOUSTICS 2019年 146卷 56-65页

作者： Song, Yan He, Bo Liu, Peng Yan, Tianhong Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China Ocean Univ China Sch Informat Sci & Engn Qingdao 266100 Peoples R China Tianjin Univ Sch Microelect Tianjin 300072 Peoples R China China Jiliang Univ Sch Mech & Elect Engn Hangzhou 310018 Zhejiang Peoples R China

This paper presents side scan sonar (SSS) image segmentation and synthesis methods based on extreme learning machine (ELM). As an algorithm derived from single-hidden layer feedforward neural networks (SLFNs), ELM has superior performance and fast learning speed with randomly generated hidden layer parameters. The SSS image segmentation uses ELM as a classifier with features generated by convolutional neural network (CNN) of multiple pathways. The CNN of multiple pathways can learn local and global features from SSS images adaptively. Taking these features as input, ELM assigns the central pixel of each input image patch of CNN to one class. Moreover, the presented SSS image synthesis method utilizes ELM as a regression algorithm, in which the non-parametric sampling algorithm is used first to synthesize coarse SSS images according to segmentation maps and sample images for each class. Then ELM trained with the coarse synthesis images and their ground truth maps (the Gaussian-filtered SSS images) synthesizes fine SSS images. Furthermore, peak signal to noise ratio (PSNR) of the synthetic SSS images with the Gaussian-filtered SSS images as ref is used as one evaluation metric for segmentation performance. Experimental results demonstrate that the SSS image segmentation method combining convolutional features with ELM outperforms typical CNN and support vector machine (SVM), and the presented SSS image synthesis method and the evaluation metric are applicable. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Side scan sonar Image segmentation Extreme learning machine Convolutional neural network Texture synthesis non-parametric sampling

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Piku Piku Interpolation An artist-guided sampling algorithm for synthesizing detail applied to facial animation

Piku Piku Interpolation An artist-guided sampling algorithm ...

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12th ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia

作者： Roberts, Richard A. dos Anjos, Rafael K. Anjyo, Ken Lewis, J. P. Victoria Univ Wellington CMIC Wellington New Zealand Victoria Univ Wellington Wellington New Zealand

We propose a sampling algorithm that reassembles real-life movements to add detail to early-stage facial animation. We examine the results of applying our algorithm with FACS data extracted from video. Using our algor... 详细信息

ISBN: (纸本)9781450369459

关键词： non-parametric sampling facial motion facial animation FACS

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Deep Monocular Depth Estimation via Integration of Global and Local Predictions

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2018年第8期27卷 4131-4144页

作者： Kim, Youngjung Jung, Hyungjoo Min, Dongbo Sohn, Kwanghoon Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea Ewha Womans Univ Dept Comp Sci & Engn Seoul 03760 South Korea

Recent works on machine learning have greatly advanced the accuracy of single image depth estimation. However, the resulting depth images are still over-smoothed and perceptually unsatisfying. This paper casts depth prediction from single image as a parametric learning problem. Specifically, we propose a deep variational model that effectively integrates heterogeneous predictions from two convolutional neural networks (CNNs), named global and local networks. They have contrasting network architecture and are designed to capture the depth information with complementary attributes. These intermediate outputs are then combined in the integration network based on the variational framework. By unrolling the optimization steps of Split Bregman iterations in the integration network, our model can be trained in an end-to-end manner. This enables one to simultaneously learn an efficient parameterization of the CNNs and hyper-parameter in the variational method. Finally, we offer a new data set of 0.22 million RGB-D images captured by Microsoft Kinect v2. Our model generates realistic and discontinuity-preserving depth prediction without involving any low-level segmentation or superpixels. Intensive experiments demonstrate the superiority of the proposed method in a range of RGB-D benchmarks, including both indoor and outdoor scenarios.

关键词： Depth estimation 2D-to-3D conversion non-parametric sampling convolutional neural networks RGB-D database

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Depth Analogy: Data-Driven Approach for Single Image Depth Estimation Using Gradient Samples

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2015年第12期24卷 5953-66页

作者： Choi, Sunghwan Min, Dongbo Ham, Bumsub Kim, Youngjung Oh, Changjae Sohn, Kwanghoon Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea Chungnam Natl Univ Dept Comp Sci & Engn Taejon 305764 South Korea Ecole Normale Super Inst Natl Rech Informat & Automat F-75013 Paris France

Inferring scene depth from a single monocular image is a highly ill-posed problem in computer vision. This paper presents a new gradient-domain approach, called depth analogy, that makes use of analogy as a means for synthesizing a target depth field, when a collection of RGB-D image pairs is given as training data. Specifically, the proposed method employs a non-parametric learning process that creates an analogous depth field by sampling reliable depth gradients using visual correspondence established on training image pairs. Unlike existing data-driven approaches that directly select depth values from training data, our framework transfers depth gradients as reconstruction cues, which are then integrated by the Poisson reconstruction. The performance of most conventional approaches relies heavily on the training RGB-D data used in the process, and such a dependency severely degenerates the quality of reconstructed depth maps when the desired depth distribution of an input image is quite different from that of the training data, e.g., outdoor versus indoor scenes. Our key observation is that using depth gradients in the reconstruction is less sensitive to scene characteristics, providing better cues for depth recovery. Thus, our gradient-domain approach can support a great variety of training range datasets that involve substantial appearance and geometric variations. The experimental results demonstrate that our (depth) gradient-domain approach outperforms existing data-driven approaches directly working on depth domain, even when only uncorrelated training datasets are available.

关键词： Depth estimation 2D-to-3D conversion gradient transfer non-parametric sampling image analogy

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Hairstyle Suggestion Using Statistical Learning

Hairstyle Suggestion Using Statistical Learning

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18th International Conference on Multimedia Modeling (MMM 2012)

作者： Yang, Wei Toyoura, Masahiro Mao, Xiaoyang Univ Yamanashi Kofu Yamanashi 4008511 Japan

ISBN: (纸本)9783642273544

Hairstyle is one of the most important features people use to characterize one's appearance. Whether a hairstyle is suitable or not is said to be closely related to one's facial shape. This paper proposes a new technique for automatically retrieving a suitable hairstyle from a collection of hairstyle examples through learning the relationship between facial shapes and suitable hairstyles. A method of hair-face image composition utilizing modern matting technique was also developed to synthesize realistic hairstyle images. The effectiveness of the proposed technique was validated through evaluation experiments.

关键词： Hairstyle retrieval example-based statistical learning non-parametric sampling hairstyle image synthesis

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On pixel-based texture synthesis by non-parametric sampling

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COMPUTERS & GRAPHICS-UK 2006年第5期30卷 767-778页

作者： Shin, Seunghyup Nishita, Tomoyuki Shin, Sung Yong Korea Adv Inst Sci & Technol Div CS Taejon 305701 South Korea Elect & Telecommun Res Inst Comp Graph Res Team Digital Content Res Div Taejon 305700 South Korea Univ Tokyo Grad Sch Frontier Sci Dept Complex Sci & Engn Bunkyo Ku Tokyo 1130033 Japan

In this paper, we propose a pixel-based method for texture synthesis with non-parametric sampling. On top of the general framework of pixel-based approaches, our method has three distinguishing features: window size estimation, seed point planting, and iterative refinement. The size of a window is estimated to capture the structural components of the dominant scale embedded in the texture sample. To guide the pixel sampling process at the initial iteration, a grid of seed points are sampled from the example texture. Finally, an iterative refinement scheme is adopted to diffuse the non-stationarity artifact over the entire texture. Our objective is to enhance texture quality as much as possible with a minor sacrifice in efficiency in order to support our conjecture that the pixel-based approach would yield high quality images. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： texture synthesis image processing non-parametric sampling

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Visio-lization: Generating Novel Facial Images

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ACM TRANSACTIONS ON GRAPHICS 2009年第3期28卷 1-8页

作者： Mohammed, Umar Prince, Simon J. D. Kautz, Jan UCL London WC1E 6BT England

Our goal is to generate novel realistic images of faces using a model trained from real examples. This model consists of two components: First we consider face images as samples from a texture with spatially varying statistics and describe this texture with a local non-parametric model. Second, we learn a parametric global model of all of the pixel values. To generate realistic faces, we combine the strengths of both approaches and condition the local non-parametric model on the global parametric model. We demonstrate that with appropriate choice of local and global models it is possible to reliably generate new realistic face images that do not correspond to any individual in the training data. We extend the model to cope with considerable intra-class variation ( pose and illumination). Finally, we apply our model to editing real facial images: we demonstrate image in-painting, interactive techniques for improving synthesized images and modifying facial expressions.

关键词： face texture synthesis non-parametric sampling

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Visio-lization: Generating Novel Facial Images

Visio-lization: Generating Novel Facial Images

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ACM SIGGRAPH Conference 2009

作者： Mohammed, Umar Prince, Simon J. D. Kautz, Jan UCL London WC1E 6BT England

关键词： face texture synthesis non-parametric sampling

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Visio-lization: generating novel facial images 09

Visio-lization: generating novel facial images

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ACM SIGGRAPH 2009 papers

作者： Umar Mohammed Simon J. D. Prince Jan Kautz University College London

ISBN: (纸本)9781605587264

Our goal is to generate novel realistic images of faces using a model trained from real examples. This model consists of two components: First we consider face images as samples from a texture with spatially varying statistics and describe this texture with a local non-parametric model. Second, we learn a parametric global model of all of the pixel values. To generate realistic faces, we combine the strengths of both approaches and condition the local non-parametric model on the global parametric model. We demonstrate that with appropriate choice of local and global models it is possible to reliably generate new realistic face images that do not correspond to any individual in the training data. We extend the model to cope with considerable intra-class variation (pose and illumination). Finally, we apply our model to editing real facial images: we demonstrate image in-painting, interactive techniques for improving synthesized images and modifying facial expressions.

关键词： non-parametric sampling face texture synthesis

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