Is Rotation a Nuisance in Shape Recognition?

By Qiuhong Ke, Yi Li. 2014

26 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

A New Representation of Skeleton Sequences for 3D Action Recognition (2017, background): “ In addition, it is also difﬁcult to construct deep LSTM to extract high-level features [35, 30]. Convolutional neural networks (CNNs) [24] nowadays have achieved great success in image classiﬁcation [2, 3, 23, 38, 39, 50, 21]. However, for video action recognition, it lacks the capacity to model th”
Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification (2016, background): “Interestingly, due to the intrinsic complexity of shape geometry, the convolution operator has been applied in a variety of spaces, including multi-view projections [3, 66, 75], 3D volumes [79], curvature space [31] and meshed graphs [7].”
Localizing and Orienting Street Views Using Overhead Imagery (2016, background): “Similarly, Ke at al [16] studied the problem of shape recognition without explicit alignment.”
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction (2016, background): “CNN pre-trained models have been shown to be transferable across domains [29], and have achieved a better performance than hand-crafted features in a variety of visual recognition tasks [30], [31], [32], [33], [34].”
Representation learning for mammography mass lesion classification with convolutional neural networks (2016, methodology): “CNNs have been successfully applied in shape recognition problems [32] as well as medical diagnosis that involved texture as a discriminant feature [11].”