Results 61 to 70 of about 1,028,974 (354)
ResUNet+: A New Convolutional and Attention Block-Based Approach for Brain Tumor Segmentation
IEEE Access, 2023 The number of brain tumor cases has increased in recent years. Therefore, accurate diagnosis and treatment of brain tumors are extremely important. Accurate detection of tumor regions is difficult, even for experts, because brain tumor images are low ...
Sedat Metlek, Halit Cetiner
doaj +1 more source
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions
, 2018 Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN). We suggest that these models can be considered as special cases of a generalized convolution operation, named ...
Zhang, Dong-Qing
core +1 more source
Cubic convolution interpolation for digital image processing
, 1981 Cubic convolution interpolation is a new technique for resampling discrete data. It has a number of desirable features which make it useful for image processing. The technique can be performed efficiently on a digital computer.
R. Keys
semanticscholar +1 more source
Convolution powers in the operator-valued framework
, 2011 We consider the framework of an operator-valued noncommutative probability space over a unital C*-algebra B. We show how for a B-valued distribution \mu one can define convolution powers with respect to free additive convolution and with respect to ...
Anshelevich, Michael +3 more
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A New Model for Tensor Completion: Smooth Convolutional Tensor Factorization
IEEE Access, 2023 Tensor completion is the problem of filling-in missing parts of multidimensional data using the values of the reference elements. Recently, Multiway Delay-embedding Transform (MDT), which considers a low-dimensional space in a delay-embedded space with ...
Hiromu Takayama, Tatsuya Yokota
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Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Despite the remarkable progress, weakly supervised segmentation approaches are still inferior to their fully supervised counterparts. We obverse the performance gap mainly comes from their limitation on learning to produce high-quality dense object ...
Yunchao Wei +5 more
semanticscholar +1 more source
Stochastic Processes and their Applications, 1982 AbstractIn proving limit theorems for some stochastic processes, the following classes of distribution functions were introduced by Chover—Ney—Wainger and Chistyakov F belongs to S(λ) if and only if: 1.(i)limx→∞F̄(2)(x)F̄(x) = c
Charles M. Goldie, Paul Embrechts
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IPCONV: Convolution with Multiple Different Kernels for Point Cloud Semantic Segmentation
Remote Sensing, 2023 The segmentation of airborne laser scanning (ALS) point clouds remains a challenge in remote sensing and photogrammetry. Deep learning methods, such as KPCONV, have proven effective on various datasets. However, the rigid convolutional kernel strategy of
Ruixiang Zhang +3 more
doaj +1 more source
Video Frame Interpolation via Adaptive Separable Convolution [PDF]
IEEE International Conference on Computer Vision, 2017 Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames ...
Simon Niklaus, Long Mai, Feng Liu
semanticscholar +1 more source
Convolutional Goppa codes [PDF]
IEEE Transactions on Information Theory, 2006 We define Convolutional Goppa Codes over algebraic curves and construct their corresponding dual codes. Examples over the projective line and over elliptic curves are described, obtaining in particular some Maximum-Distance Separable (MDS) convolutional codes.
J.A. Domínguez Pérez +3 more
openaire +5 more sources