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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4715 条记录，以下是261-270 订阅

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imageFlowNet: Forecasting Multiscale image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical images

ImageFlowNet: Forecasting Multiscale Image-Level Trajectorie...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chen Liu Ke Xu Liangbo L. Shen Guillaume Huguet Zilong Wang Alexander Tong Danilo Bzdok Jay Stewart Jay C. Wang Lucian V. Del Priore Smita Krishnaswamy Yale University UCSF Mila Univ. de Montréal Mila McGill Yale NorCal Retina Vitreous Associates

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, predictive modeling of such data remains challenging due to high dimensionality, irregular sampling, and data sparsity. To address these issues, we propose imageFlowNet, a novel model designed to forecast disease trajectories from initial images while preserving spatial details. imageFlowNet first learns multiscale joint representation spaces across patients and time points, then optimizes deterministic or stochastic flow fields within these spaces using a position-parameterized neural ODE/SDE framework. The model leverages a UNet architecture to create robust multiscale representations and mitigates data scarcity by combining knowledge from all patients. We provide theoretical insights that support our formulation of ODEs, and motivate our regularizations involving high-level visual features, latent space organization, and trajectory smoothness. We validate imageFlowNet on three longitudinal medical image datasets depicting progression in geographic atrophy, multiple sclerosis, and glioblastoma, demonstrating its ability to effectively forecast disease progression and outperform existing methods. Our contributions include the development of imageFlowNet, its theoretical underpinnings, and empirical validation on real-world datasets.

关键词： Visualization Multiple sclerosis stochastic processes Organizations Predictive models Signal processing Data models Trajectory Speech processing Biomedical imaging

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stochastic Approach to realistic rendering in computer graphics for Virtual Reality (VR)

Stochastic Approach to realistic rendering in computer graph...

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International conference on Networking and Advanced Systems (ICNAS)

作者： Kadi Bouatouch IRISA University of Rennes 1 PERCEPT group

In Virtual Reality, it is important to make the rendering of images displayed on an HMD headset as realistic as possible. In this case, it is a question of taking into account the multiple reflections, refractions and diffusions using stochastic methods (Monte Carlo, Gaussian processes, etc.) or AI (CNN neural networks). These methods can be used in other fields such as vision, crowd simulation, etc. This Keynote Lecture begins by introducing the different processing necessary for obtaining a computer-generated image such as: geometric modeling, camera parameters, color, photometric quantities, etc. Then it presents in detail the various stochastic rendering methods allowing the generation of synthetic images with a high level of realism. Among these methods we can cite: the Monte Carlo method, Bayesian Monte Carlo, Metropolis, spectral Analysis of Quadrature Rules and Fourier Truncation-based methods Applied to the Shading Integral, etc. This lecture will be illustrated with realistic images generated with different rendering methods.

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Optimization of Micro-object Identification Based on the Mellin Transform and the Use of Parallel Computing

Optimization of Micro-object Identification Based on the Mel...

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International Russian Smart Industry conference (SmartIndustryCon)

作者： Isroil I. Jumanov Sunatillo M. Kholmonov Intelligent Systems and Computer Technologies Samarkand State University Samarkand Uzbekistan

Scientific and methodological foundations for the optimal identification of non-stationary objects based on the use of neural networks have been developed. Models and algorithms for detection, extraction of hidden relationships, useful properties and patterns in data, formation of a database and knowledge bases are proposed. Mechanisms have been developed for using the statistical, dynamic and specific characteristics of images, unique features of three, five-layer neural networks and combined models for setting variables with typical recognition and classification tools. Have been developed computational schemes for determining and adjusting the weights of neurons, choosing a suitable activation function, coefficients of synaptic and interneuronal connections, rational neural network architecture, the number of layers and neurons in the layers of the network, a set of functions of nonlinear dependencies "inputs - outputs". Data pre-processing algorithms are implemented that perform the functions of informative features selection, segmentation, object image contour extraction, search based on methods with annealing, prohibition, and stochastic search. Tested neural networks of Hopfield, Hamming, Hebb, Kohonen, bidirectional associative memory were tested. Schemes for two and three-dimensional image reconstruction based on the synthesis of tools for calculating Mellin transform functions, initial values of centroids, and the formation of a suboptimal set of variables are proposed. The identification software package in C++ was developed and implemented in the CUDA parallel computing environment.

关键词： image segmentation image recognition Heuristic algorithms Neurons Artificial neural networks Transforms Parallel processing

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FAST BAYESIAN MODEL SELECTION IN IMAGING INVERSE PROBLEMS USING RESIDUALS 21

FAST BAYESIAN MODEL SELECTION IN IMAGING INVERSE PROBLEMS US...

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IEEE statistical Signal processing Workshop (SSP)

作者： Vidal, Ana Fernandez Pereyra, Marcelo Durmus, Alain Giovannelli, Jean-Francois Heriot Watt Univ Sch Math & Comp Sci Edinburgh Midlothian Scotland Maxwell Inst Math Sci Edinburgh Midlothian Scotland Univ Paris Saclay CNRS CMLA Ecole Normale Super Paris Saclay F-94235 Cachan France IMS Univ Bordeaux CNRS BINP UMR 5218 Talence France

ISBN: (纸本)9781728157672

This paper presents a fast heuristic for comparing Bayesian models to solve inverse problems related to signal processing. We focus on problems that are convex w.r.t. the unknown signal and where no ground truth is available. The proposed heuristic is very computationally efficient and does not require the estimation of the model evidence. Instead, the model evidence is used indirectly to set the regularisation parameters that define each competing model by maximum marginal likelihood estimation, followed by a simple likelihood-based or residual-based comparison of the models based on their empirical Bayesian maximum-a-posteriori solutions. The proposed methodology is illustrated with a total-variation image deblurring experiment, where it performs remarkably well.

关键词： Model selection image processing inverse problems empirical Bayes stochastic optimisation Markov chain Monte Carlo methods proximal algorithms

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An Efficient Traffic Sign Board Detection Approach using Convolutional Neural Network in Comparison with Radial Basis Function with Improved Accuracy 14

An Efficient Traffic Sign Board Detection Approach using Con...

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14th International conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)

作者： Prashanth, Ravilla Subramanyam Devi, T. Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Comp Sci & Engn Chennai 602105 Tamil Nadu India

ISBN: (纸本)9781665460712

Aim: Identifying and classifying traffic signs board images is accomplished through image recognition and determine whether the image is a speed sign or stop sign using Convolutional Neural Network (CNN) comparison with Radial Basis Function (RBF). Materials and methods: We can determine the accuracy of stop and speed signs based on the size dataset collected for each from 4,470 image files. Convolutional Neural Networks (CNN) provide an optimal starting point for detection where the sample size taken is 23 and with comparison algorithm Radial basis function (RBF) the sample size is 23, CNN is regarded as more powerful than RBF since RBF offers fewer features than CNN. Results: Using the dataset Traffic sign board images, CNN detects when inputs are introduced and produces accurate results with accuracy rate 96%. where in RBF the image detecting pression is 73% and accuracy given is 82%. It shows a statistical significance of (p<0.002) from the Independent Sample T-test. Conclusion: CNN in terms of accuracy and precision, performs better than RBF.

关键词： Classification Convolutional Neural Network image processing Machine Learning Novel image Classification Recognition Radial Basis Function

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An α-Divergence Approach To Robust Canonical Correlation Analysis

An α-Divergence Approach To Robust Canonical Correlation An...

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IEEE International conference on image processing

作者： Wenjing Yang Abd-Krim Seghouane Pavel Krupskiy School of Mathematics and Statistics The University of Melbourne Australia

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Canonical correlation analysis (CCA) is a widely used mutivariate statistical technique for exploring the relationship between two multivariable datasets. It extracts existing relationship information by finding pairs of linear combinations from the two sets of variables with maximum correlation. In some applications however, the observed datasets may be contaminated by outliers and the standard CCA methods are sensitive to the presence of outliers in the datasets. In this paper, a robust CCA (RCCA) algorithm is presented. It is obtained using the interpretation of CCA as a latent variable model with two Gaussian random vectors and a robust loss function derived from the $\alpha$-divergence as an alternative to maximum likelihood. Compared to existing robust CCA approaches, the proposed loss has the advantage of belonging to class of redescending M-estimators, guaranteeing inference stability for large deviation from the Gaussian nominal noise model. Experimental results on simulated and real datasets show that the proposed RCCA outperforms some existing robust and standard CCA methods.

关键词： Maximum likelihood estimation Correlation image processing Noise Vectors Stability analysis Data mining

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Training stochastic stabilized supralinear networks by dynamics-neutral growth 36

Training stochastic stabilized supralinear networks by dynam...

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36th conference on Neural Information processing Systems (NeurIPS)

作者： Soo, Wayne W. M. Lengyel, Mate Univ Cambridge Dept Engn Cambridge England Cent European Univ Dept Cognit Sci Vienna Austria

ISBN: (纸本)9781713871088

There continues to be a trade-off between the biological realism and performance of neural networks. Contemporary deep learning techniques allow neural networks to be trained to perform challenging computations at (near) human-level, but these networks typically violate key biological constraints. More detailed models of biological neural networks can incorporate many of these constraints but typically suffer from subpar performance and trainability. Here, we narrow this gap by developing an effective method for training a canonical model of cortical neural circuits, the stabilized supralinear network (SSN), that in previous work had to be constructed manually or trained with undue constraints. SSNs are particularly challenging to train for the same reasons that make them biologically realistic: they are characterized by strongly-connected excitatory cells and expansive firing rate non-linearities that together make them prone to dynamical instabilities unless stabilized by appropriately tuned recurrent inhibition. Our method avoids such instabilities by initializing a small network and gradually increasing network size via the dynamics-neutral addition of neurons during training. We first show how SSNs can be trained to perform typical machine learning tasks by training an SSN on MNIST classification. We then demonstrate the effectiveness of our method by training an SSN on the challenging task of performing amortized Markov chain Monte Carlo-based inference under a Gaussian scale mixture generative model of natural image patches with a rich and diverse set of basis functions - something that was not possible with previous methods. These results open the way to training realistic cortical-like neural networks on challenging tasks at scale.

关键词： stochastic systems

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Intelligent image Recognition and Classification Technology in Digital Media 1st

Intelligent Image Recognition and Classification Technology ...

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1st International conference on Pattern Analysis and Machine Intelligence, ICPAMI 2024

作者： Liang, Huan Guangzhou International Economics College Guangzhou510540 China

ISBN: (纸本)9789819633487

With the increasing popularity of digital media along with the fact that massive image data has already gained popularity, the automatic image recognition and classification have become a very critical and urgent problem, this study investigates and tests intelligent image recognition and classification techniques for digital media based on major methods such as CNN, SVM and RNN. Different algorithms and models are applied to training and test image data to evaluate the performance of various methods in terms of accuracy and processing time. The experimental results shows that the CNN method can achieve high accuracy in image classification task and the multi-layer convolution and pooling operations can effectively extract image features. The SVM method is better than the CNN in terms of processing time but have slightly lower accuracy. The RNN method is ranked between the CNN and SVM in terms of accuracy and processing time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image representation

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Extending Phrase Grounding with Pronouns in Visual Dialogues

Extending Phrase Grounding with Pronouns in Visual Dialogues

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2022 conference on Empirical methods in Natural Language processing, EMNLP 2022

作者： Lu, Panzhong Zhang, Xin Zhang, Meishan Zhang, Min School of New Media and Communication Tianjin University China Institute of Computing and Intelligence Harbin Institute of Technology Shenzhen China

Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently, sole noun phrase grounding is not enough for cross-modal visual language understanding. Here we extend the task by considering pronouns as well. First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions. Based on the dataset, we test the performance of phrase grounding by using a state-of-the-art literature model of this line. Then, we enhance the baseline grounding model with coreference information which should help our task potentially, modeling the coreference structures with graph convolutional networks. Experiments on our dataset, interestingly, show that pronouns are easier to ground than noun phrases, where the possible reason might be that these pronouns are much less ambiguous. Additionally, our final model with coreference information can significantly boost the grounding performance of both noun phrases and pronouns. © 2022 Association for Computational Linguistics.

关键词： statistical tests

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Multi-Tissue Derived Windowing Technology Based on statistical Features and Its Colorization Application 21

Multi-Tissue Derived Windowing Technology Based on Statistic...

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3rd International conference on Intelligent Medicine and image processing (IMIP)

作者： Liu, Jiaji Jiang, Huiyan Zhang, Zexu Lu, Guoxiu Northeastern Univ Shenyang Liaoning Peoples R China Gen Hosp Northern Theater Command Shenyang Liaoning Peoples R China

ISBN: (纸本)9781450390057

Traditional CT data processing methods often employ the inherent window to clip the data and store the image. For some DICOM-type data processing software, although the window can be adjusted dynamically, it still requires users' the medical knowledge and familiarity in medical images. In this work, we propose a derivative windowing process method (multi-tissue derived windowing technology) for multiple tissues and construct organs based on the statistical features of CT images. Then, we design a transformation function to convert the density value of CT image into gray value. Finally, we construct the automatic colorization network using Dense U-Net as the generator of Generation Adversarial Network(GAN) model, making the multiple organs of the human body colored in a globally optimized way to highlight the color texture features of them. In this way the quality of color images can be improved significantly. In the test study, we evaluate the proposed method using many image quality evaluation methods and subjective visual evaluation on the 3Dircadb data set. The experimental results demonstrate the effectiveness of the proposed method, which indeed improves the quality of the image.

关键词： Windowing technology Colorization Medical image GAN

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