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Bayesian and non-Bayesian probabilistic models for medical image analysis

IMAGE AND vision COMPUTING 2003年第10期21卷 851-864页

作者： Bromiley, PA Thacker, NA Scott, MLJ Pokric, M Lacey, AJ Cootes, TF Univ Manchester Sch Med Imaging Sci & Biomed Engn Div Manchester M13 9PT Lancs England

Bayesian approaches to data analysis are popular in machine vision, and yet the main advantage of Bayes theory, the ability to incorporate prior knowledge in the form of the prior probabilities, may lead to problems in some quantitative tasks. In this paper we demonstrate examples of Bayesian and non-Bayesian techniques from the area of magnetic resonance image (MRI) analysis. Issues raised by these examples are used to illustrate difficulties in Bayesian methods and to motivate an approach based on frequentist methods. We believe this approach to be more suited to quantitative data analysis, and provide a general theory for the use of these methods in learning (Bayes risk) systems and for data fusion. Proofs are given for the more novel aspects of the theory. We conclude with a discussion of the strengths and weaknesses, and the fundamental suitability, of Bayesian and non-Bayesian approaches for MRI analysis in particular, and for machine vision systems in general. (C) 2003 Elsevier B.V. All rights reserved.

关键词： Bayesian and non-Bayesian probabilistic models magnetic resonance image analysis expectation maximisation algorithm

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probabilistic Local Equivalence Certification for Robustness Evaluation 18th

Probabilistic Local Equivalence Certification for Robustness...

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18th International symposium on Visual Computing (ISVC)

作者： Bond, Jacob Gupta, Siddhartha Elvitigala, Thanura Gen Motors Co Warren MI 48092 USA

ISBN: (纸本)9783031479687;9783031479694

To evaluate the robustness of non-classifier models, we propose probabilistic local equivalence, based on the notion of randomized smoothing, as a way to quantitatively evaluate the robustness of an arbitrary function. For a given function f, probabilistic local equivalence evaluates whether, when sampling a normally-distributed point x' in a neighborhood of a point x, there is a probability > 0.5 that f(x') is equivalent to f(x), according to a user-defined notion of equivalence. We use probabilistic local equivalence to evaluate the effect of data augmentation methods for improving robustness, including adversarial training, on a model's performance. We also use probabilistic local equivalence to evaluate the effect on robustness of model architecture, number of parameters, pre-training, quantization, and other model properties.

关键词： computer vision Robustness Evaluation

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Bayesian and non-Bayesian probabilistic models for medical image analysis

Bayesian and non-Bayesian probabilistic models for medical i...

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symposium on probabilistic models in computer vision

作者： Bromiley, PA Thacker, NA Scott, MLJ Pokric, M Lacey, AJ Cootes, TF Univ Manchester Sch Med Imaging Sci & Biomed Engn Div Manchester M13 9PT Lancs England

关键词： Bayesian and non-Bayesian probabilistic models magnetic resonance image analysis expectation maximisation algorithm

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Stochasticks: Augmenting the billiards experience with probabilistic vision and wearable computers

Stochasticks: Augmenting the billiards experience with proba...

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1st International symposium on Wearable computers

作者： Jebara, T Eyster, C Weaver, J Starner, T Pentland, A Massachusetts Inst of Technology Cambridge United States

ISBN: (纸本)0818681926

We propose a practical application of wearable computing and augmented reality which enhances the game of billiards. A vision algorithm is implemented which operates in interactive-time with the user to assist planning and aiming. probabilistic color models and symmetry operations are used to localize the table, pockets and balls through a video camera near the user's eye. Classification of the objects of interest is performed and each possible shot is ranked in order to determine its relative usefulness. The system allows the user to proceed through a regular pool game while it automatically determines strategic shots. The resulting trajectories are rendered as graphical overlays on a head mounted live video display. The wearable video output and the computer vision system provide an integration of real and virtual environments which enhances the experience of playing and learning the game of billiards without encumbering the player.

关键词： Interactive devices

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A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences

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IMAGE AND vision COMPUTING 2003年第10期21卷 879-889页

作者： de Trazegnies, C Urdiales, C Bandera, A Sandoval, F Univ Malaga ETSI Telecommun Dpto Tecnol Elect E-29071 Malaga Spain

This paper presents a new technique for planar object recognition based on Hidden Markov models. First, the contour of the object is processed to extract a sequence of high curvature points. These points are extracted from a new adaptively extracted curvature function which is resistant against noise and transformations. Each corner is characterized by its subtended angle and its distance to the next corner. Then, corner sequences are analyzed by using HMMs. The method has been successfully tested for different databases. Its main advantage is that it can deal with incomplete and distorted corner sequences. (C) 2003 Elsevier B.V. All rights reserved.

关键词： 2D object recognition Hidden Markov models

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MULTIMODAL CLASSIFICATION WITH DEFORMABLEPART-BASED models FOR URBAN CARTOGRAPHY

MULTIMODAL CLASSIFICATION WITH DEFORMABLEPART-BASED MODELS F...

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IEEE Joint International Geoscience and Remote Sensing symposium (IGARSS) / 35th Canadian symposium on Remote Sensing

作者： Randrianarivo, Hicham Le Saux, Bertrand Ferecatu, Marin Off Natl Etud & Rech Aerosp F-91761 Palaiseau France

ISBN: (纸本)9781479957750

Data from satellite and aerial images are now widely used by everyone. These images contain information from different frequency bands that help to characterize areas of interest. In this paper we study a framework for object detection in aerial image based on discriminatively-trained models trained on multimodal data. Specifically, we investigate a method to merge outputs of large margin classifiers trained on images from different sensors: we use the ranking ability of these classifiers to learn a probabilistic model.

关键词： Calibration computer vision Data models Deformable models Detectors Remote sensing Support vector machines

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Report on the probabilistic language scheme

Report on the probabilistic language scheme

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2007 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages and Applications, OOPSLA - 2007 Dynamic Languages symposium, DLS

作者： Radul, Alexey Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge MA 02139

ISBN: (纸本)9781595938688

Reasoning with probabilistic models is a widespread and successful technique in areas ranging from computer vision, to natural language processing, to bioinformatics. Currently, these reasoning systems are either coded from scratch in general-purpose languages or use formalisms such as Bayesian networks that have limited expressive power. In both cases, the resulting systems are difficult to modify, maintain, compose, and interoperate with. This work presents probabilistic Scheme, an embedding of probabilistic computation into Scheme. This gives programmers an expressive language for implementing modular probabilistic models that integrate naturally with the rest of Scheme. © 2007 ACM.

关键词： probabilistic logics

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Robust classification of multivariate time series by imprecise hidden Markov models

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2015年第PartB期56卷 249-263页

作者： Antonucci, Alessandro De Rosa, Rocco Giusti, Alessandro Cuzzolin, Fabio Ist Dalle Molle Studi Intelligenza Artificiale Manno Switzerland Univ Milan Dipartimento Sci Informaz Milan Italy Oxford Brookes Univ Dept Comp Oxford OX3 0BP England

A novel technique to classify time series with imprecise hidden Markov models is presented. The learning of these models is achieved by coupling the EM algorithm with the imprecise Dirichlet model. In the stationarity limit, each model corresponds to an imprecise mixture of Gaussian densities, this reducing the problem to the classification of static, imprecise-probabilistic, information. Two classifiers, one based on the expected value of the mixture, the other on the Bhattacharyya distance between pairs of mixtures, are developed. The computation of the bounds of these descriptors with respect to the imprecise quantification of the parameters is reduced to, respectively, linear and quadratic optimization tasks, and hence efficiently solved. Classification is performed by extending the k-nearest neighbors approach to interval-valued data. The classifiers are credal, meaning that multiple class labels can be returned in the output. Experiments on benchmark datasets for computer vision show that these methods achieve the required robustness whilst outperforming other precise and imprecise methods. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Multivariate time series Credal classification Hidden Markov models Gaussian mixtures Imprecise probability Bhattacharyya distance

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A computer vision Method to Measure Distribution of Crack Characteristics in Reinforced Concrete Elements 15th

A Computer Vision Method to Measure Distribution of Crack Ch...

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15th fib International PhD symposium in Civil Engineering, 2024

作者： Hagh, Morteza Foster, Stephen Pour, Hamid Vali School of Civil & Environmental Engineering University of New South Wales SydneyNSW2052 Australia

ISBN: (纸本)9782940643240

Analysing cracking behaviour is essential for assessing damage and durability of reinforced concrete (RC) structures. The cracking behaviour of RC elements is probabilistic, making it challenging to mathematically model its key characteristics such as crack spacing and inclination. The need to consider tolerance for distribution of crack spacing and inclination has been largely neglected in previously proposed crack prediction models. This is partly attributed to a lack of suitable measurement methods that accurately capture the key crack characteristics required to develop reliable probabilistic models. The research presented in this paper aims to address this issue by developing a reliable computer vision (CV)-based method that accurately measures the essential crack parameters, including spacing and inclination, from images taken during tests. The capability of the method presented in this paper is evaluated against a database of panel elements. The results indicate that an R2 correlation coefficient of 0.93 and a coefficient of variation (CoV) of 0.19 are attained by the proposed method in extracting crack spacing from panel images. © Fédération Internationale du Béton–International Federation for Structural Concrete.

关键词： Cracks

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Survey on Diffusion models

Survey on Diffusion Models

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2024 International symposium of Systems, Advanced Technologies and Knowledge, ISSATK 2024

作者： Sallami, Mohamed Nidhal Fredj, Ouissem Ben University of kairouan ISSAT Kairouan Kairouan Tunisia University of sousse Prince Research Lab ISITCom Tunisia

ISBN: (纸本)9798331540173

Recently, computer vision has focused on denoising diffusion models, which have produced amazing outcomes for generative modeling. The idea of modeling the joint probability distribution of input and output data is the basis of generative diffusion models. They generate new data using random samples at each step using an iterative process. This paper is a survey on diffusion models. It introduces the mathematical background behind diffusion models especially the denoising diffusion probabilistic models and the different categories of diffusion models. © 2024 IEEE.

关键词： Generative adversarial networks

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