Results 31 to 40 of about 1,031,506 (314)
Optimization of the 2P fifth degree convolution kernel in the spectral domain [PDF]
Bulletin of Natural Sciences Research, 2023 The first part of the paper describes a two-parameter (2P) fifth-order interpolation kernel, r. After that, from the 2P kernel, the kernel components were created.
Savić Nataša +2 more
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International Statistical Review / Revue Internationale de Statistique, 1999 SummaryA general convolution theorem within a Bayesian framework is presented. Consider estimation of the Euclidean parameter θ by an estimator T within a parametric model. Let W be a prior distribution for θ and define G as the W‐average of the distribution of T ‐ θ under θ.
van den Heuvel, E.R., Klaassen, C.A.J.
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IET Computer Vision, 2021 Three‐dimensional (3D) shape reconstruction of objects requires multiple scans and complex reconstruction algorithms. An alternative approach is to infer the 3D shape of an object from a single depth image (i.e. single depth view).
Edwin Valarezo Añazco +2 more
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Collocating Convolutions [PDF]
Mathematics of Computation, 1995 An explicit method is derived for collocating either of the convolution integrals p ( x ) = ∫ a x f ( x − t ) g ( t ) d t p(x) = \smallint _a^xf(x - t)g(t)dt or q (
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PSTNet: Point Spatio-Temporal Convolution on Point Cloud Sequences [PDF]
International Conference on Learning Representations, 2022 Point cloud sequences are irregular and unordered in the spatial dimension while exhibiting regularities and order in the temporal dimension. Therefore, existing grid based convolutions for conventional video processing cannot be directly applied to ...
Hehe Fan +4 more
semanticscholar +1 more source
SIAM Journal on Optimization, 2019 The Moreau envelope is one of the key convexity-preserving functional operations in convex analysis, and it is central to the development and analysis of many approaches for convex optimization. This paper develops the theory for an analogous convolution operation, called the polar envelope, specialized to gauge functions.
Michael P. Friedlander +2 more
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Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions [PDF]
Computer Vision and Pattern Recognition, 2020 Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e.
Ricard Durall +2 more
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Multipliers of Banach valued weighted function spaces
International Journal of Mathematics and Mathematical Sciences, 2000 We generalize Banach valued spaces to Banach valued weighted function spaces and study the multipliers space of these spaces. We also show the relationship between multipliers and tensor product of Banach valued weighted function spaces.
Serap Öztop
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Enhanced CNN for image denoising
CAAI Transactions on Intelligence Technology, 2019 Owing to the flexible architectures of deep convolutional neural networks (CNNs) are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train.
Chunwei Tian +5 more
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Convolution Inference via Synchronization of a Coupled CMOS Oscillator Array
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020 Oscillator neural networks (ONNs) are a promising hardware option for artificial intelligence. With an abundance of theoretical treatments of ONNs, few experimental implementations exist to date.
Dmitri E. Nikonov +8 more
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