Sep 22, 2022 · This work proposed OACNNs that enabled CNNs to extract orientation invariant features even without data augmentation.
Jan 1, 2022 · OACNNs are compared with state-of-the-art methods and consistently outperform them in various experiments. OACNNs demonstrate a better ...
OACNNs: Orientation adaptive convolutional neural networks
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This paper proposes orientation adaptive kernels (OA kernels) and orientation adaptive max pooling (OA max pooling) that comprise a new topological structure, ...
Abstract. Geometric invariant feature representation plays an indispensable role in the field of image processing and computer vision. Recently, convolution ...
This new adaptive kernel is used to perform a second convolution of the input image generating the output pixel. Adaptive kernels en- able accurate recognition ...
Missing: OACNNs: | Show results with:OACNNs:
A deep learning Convolutional Neural Network was trained to predict crystal orientation ... OACNNs: Orientation adaptive convolutional neural networks. Article.
OACNNs: Orientation adaptive convolutional neural networks · Xiang Ye, Zihang ... CraftNet: A deep learning ensemble to diagnose cardiovascular diseases.
Apr 10, 2019 · We propose a pixel-adaptive convolution (PAC) operation, a simple yet effective modification of standard convolutions, in which the filter weights are ...
Missing: OACNNs: Orientation
OACNNs: Orientation adaptive convolutional neural networks. Xiang Ye, Zihang He, Bohan Li, Yong Li. https://doi.org/10.3233/jifs-213051. 2022, Journal of ...
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Feb 2, 2021 · In this paper, we develop Orientation Convolution Networks (OCNs) for image recognition based on the proposed Landmark Gabor Filters (LGFs)
Missing: OACNNs: adaptive