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检索条件"任意字段=Symposium on Probabilistic Models in Computer Vision"

共 61 条记录，以下是31-40 订阅

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Diffusion probabilistic models for Underwater Image Super-Resolution 3rd

Diffusion Probabilistic Models for Underwater Image Super-R...

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3rd BenchCouncil International symposium on Intelligent computers, Algorithms, and Applications, IC 2023

作者： Wang, Kai Zhong, Guoqiang College of Computer Science and Technology Ocean University of China Qingdao China

ISBN: (纸本)9789819700646

In recent years, single image super-resolution (SISR) has been extensively employed in the realm of underwater machine vision. However, the unique challenges posed by the underwater environment, including various types of noise, blurring effects, and insufficient illumination, have rendered the recovery of detailed information from underwater images a complex task for most existing methodologies. In this paper, we address and propose solutions to these challenges inherent in the application of super-resolution techniques in underwater machine vision. We introduce a novel underwater SISR diffusion probability model, termed as DiffUSR. This marks the first instance of utilizing a diffusion probability model in the domain of underwater SISR. Our innovative model enhances the data likelihood by employing a unique variant of variational constraints. Notably, DiffUSR is capable of providing diverse and realistic super-resolution (SR) predictions by progressively transforming Gaussian noise into SR images based on low-resolution (LR) inputs via a Markov Chain process. This approach represents a significant advancement in the field of underwater image super-resolution. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diffusion

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A probabilistic approach to roof extraction and reconstruction

A probabilistic approach to roof extraction and reconstructi...

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2002 International symposium of ISPRS Commission III on Photogrammetric computer vision, PCV 2002

作者： Scholze, S. Moons, T. Van Gool, L. Zurich Switzerland Leuven Belgium Katholieke Universiteit Brussel Group of Exact Sciences Brussel Belgium

This paper investigates into the model-based reconstruction of complex polyhedral building roofs. A set of 3D line segments, obtained from multiview correspondence analysis of high resolution colour imagery, is used as input data. The 3D line segments are grouped into planes by means of a Bayesian model selection procedure. In the resulting planes, models for polygonal patches are then instantiated. Driven by the Maximum Expected Utility principle, the algorithm chooses the optimal patch and plane configuration non-deterministically. Roof reconstruction is completed by further reasoning steps which are guided by the semantic interpretation of the intermediate patch configuration. Several successfully reconstructed complex roof structures corroborate the potential of the approach. © 2002 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Antennas

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Automated core fracture characterization by computer vision and image analytics of CT images

Automated core fracture characterization by computer vision ...

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SPE Oklahoma City Oil and Gas symposium 2019, OKOG 2019

作者： Antle, Ryan Baker Hughes a GE Company United States

ISBN: (纸本)9781613996591

computer vision (CV) techniques were applied to X-ray computed tomographic (CT) images of reservoir cores to evaluate their potential for rapidly identifying fractures and other lithologic/geologic characteristics. The analyses utilized feature-labeled CT cross sectional images, themselves distributed submillimeter voxels of density- and atomic number-sensitive CT Numbers, as inputs to a Fully Convolutional Neural Network (FCN) for semantic segmentation of reservoir core features. In FCN, an image is interrogated using a series of sliding windows at various scales to create weighted filters to reduce error between classes in training images. These networks of filter layers were used to assign probabilities of classes, which were upscaled back to the original image dimensions resulting in probabilistic class assignments onto each pixel. FCN model accuracy, defined by its ability to replicate manually-assigned labels in the raw (unannotated) training image stack, was at least 80% and generally improved with the size of the training set. Once the labels were assigned, the underlying feature frequency, orientation, and size were measured in 3D volume reconstructions using algorithms modified from standard image analysis software. This method allowed users to endow a classification model with subject matter knowledge for further, autonomous label prediction. Thus, while initial image annotation was labor intensive, subsequent images were rapidly classified once the model was built. The classified labels were analyzed for abundance, orientation, and size of fractures were calculated to characterize spatial information of these features. FCN combined with fracture labeling improved knowledge capture and automation of fracture identification. models trained by high quality 3D datasets can greatly reduce the time needed to describe subsequent core. The method demonstrated is not limited to fractures, other lithologic/geologic features could be trained using the same method,

关键词： Fracture

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Exemplar-based tracking and recognition of arm gestures

Exemplar-based tracking and recognition of arm gestures

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International symposium on Image and Signal Processing and Analysis

作者： A. Elgammal V. Shet Y. Yacoob L.S. Davis Computer Vision Laboratory University of Maryland College Park MD USA

This paper presents a probabilistic exemplar-based framework for recognizing gestures. The approach is based on representing each gesture as a sequence of learned body poses. The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constrains between different poses. Matching individual poses to image data is performed using a probabilistic formulation for edge matching to obtain a likelihood measurement for each individual pose. The paper introduces a correspondence-free weighted matching scheme for edge templates that emphasize discriminating features in the matching. The weighting does not require establishing correspondences between the different pose models. The probabilistic framework also imposes temporal constrains between different pose through a learned hidden Markov model (HMM) of each gesture.

关键词： Hidden Markov models Performance evaluation Vehicle driving computer vision Laboratories Educational institutions Human computer interaction Virtual reality Human robot interaction Prototypes

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Color correction for onboard multi-camera systems using 3D Gaussian Mixture models

Color correction for onboard multi-camera systems using 3D G...

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IEEE symposium on Intelligent Vehicle

作者： Miguel Oliveira Angel D. Sappa Vitor Santos Department of Mechanical Engineering University of Aveiro Aveiro Portugal Computer Vision Center Edifici O Bellaterra Barcelona Spain

The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.

关键词： Image color analysis Image segmentation Transfer functions Cameras probabilistic logic Sensors computer vision

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A probabilistic Combination of CNN and RNN Estimates for Hand Gesture Based Interaction in Car 16

A Probabilistic Combination of CNN and RNN Estimates for Han...

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16th IEEE International symposium on Mixed and Augmented Reality (ISMAR)

作者： Tewari, Aditya Taetz, Bertram Grandidier, Frederic Stricker, Didier TU Kaiserslautern Kaiserslautern Germany IEE SA Contern Luxembourg German Res Ctr Artificial Intelligence Kaiserslautern Germany

ISBN: (纸本)9780769563275

Hand Gesture Recognition is completed on top-view hand images observed by a Time of Flight(ToF) camera in a car. The work attempts to solve two important problems of touchless interactions inside a car. First, low latency identification of the gestures which are unobtrusive for the driver. Second, reducing the labelled data required to train learning based solutions, this is particularly important because labelling of gesture sequences is expensive and exigent. This work attempts to improve the fast detection of hand-gestures by correcting the probability estimate of a Long Short Term Memory (LSTM) network by pose prediction made by a Convolutional Neural Network(CNN). Weak models for hand gesture classes based on five hand poses are designed to assist in the prediction-correction scheme. A training procedure to reduce the labelled data required for hand pose classification is also introduced. This method tries to utilise the statistical property of the dataset to identify a good initialization of weights for the CNN, here we demonstrate this using the Principal Component Analysis(PCA) embedding of non-labelled hand pose sequences. While solving a nine class hand gesture problem we demonstrate an accuracy of 89.50% which is better than existing systems. We also show that a PCA embedding based initialization improves the classification performance of the CNN based pose classifier.

关键词： I.5.4 [computer vision] [H.5.2]: Interaction styles H.m [Hand Gestures]: Hand Pose I.4.m [Activity recognition and understanding] [I.5.1]: Neural nets-LSTM, CNN

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Survey on Diffusion models

Survey on Diffusion Models

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Systems, Advanced Technologies and Knowledge (ISSATK), International symposium of

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

ISBN: (数字)9798331540173

ISBN: (纸本)9798331540180

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.

关键词： Surveys computer vision Reviews Computational modeling Noise reduction Scattering Diffusion models Mathematical models Probability distribution Data models

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Generalized formulation and hypercube algorithms for relaxation labeling

Generalized formulation and hypercube algorithms for relaxat...

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International symposium on Parallel Processing

作者： E. Leung Xiaobo Li Department of Computer Science Keyano College Fort McMurray AB Canada Department of Computing Science University of Alberta Edmonton AB Canada

Presents a generalized formulation for several well-known approaches to relaxation labeling, including discrete, fuzzy, linear probabilistic models and several nonlinear probabilistic modes. Based on this generalized framework, two parallel algorithms for SIMD hypercube computers with different numbers of processors are proposed and analyzed. The algorithms achieve minimal time complexity.< >

关键词： Hypercubes Labeling Parallel algorithms Filtering Concurrent computing Algorithm design and analysis Fuzzy sets computer science Educational institutions computer vision

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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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International symposium on Wearable computers

作者： T. Jebara C. Eyster J. Weaver T. Starner A. Pentland Massachusetts Institute of Technology Cambridge MA US Media Laboratory Massachusetts Institute of Technology Cambridge MA USA

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： Wearable computers computer vision Augmented reality Application software computer displays Games Head Physics computing Laboratories Cameras

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Optimal color space based probabilistic foreground detector for video surveillance systems

Optimal color space based probabilistic foreground detector ...

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IEEE International symposium on Industrial Electronics (ISIE)

作者： Ajmal Shahbaz Danilo Cáceres Hernández Kang-Hyun Jo Intelligent Systems Laboratory University of Ulsan Ulsan Korea University of Ulsan Ulsan KR

ISBN: (纸本)9781509014132

Foreground detection is one of the well and widely studied research topic in the field of computer vision. However, it still fails to cope with the many practical issues such as illumination changes, dynamic backgrounds, and shadow. This paper proposes optimal color space based probabilistic foreground detector. The intuition is to employ two most widely used color spaces (RGB and YCbCr) one at a time to model background. A decision criteria to select optimal color space is based on mean squared error (MSE). Initial frames (say 100) without any foreground information are used to compute MSE for both color spaces. Color space with minimum MSE is selected as optimal color space (OCS). Afterwards, OCS is used to model background and detect moving information. Gaussian Mixture models (GMM) based foreground detector is used for the purpose. Furthermore, foreground mask is cleaned from undesirable noise using morphological operations. The proposed method is tested using change detection dataset. It shows promising results and outperforms conventional GMM.

关键词： Color Colored noise Lighting Mathematical model Detectors probabilistic logic computer vision

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