منابع مشابه
Rotation Invariance
This chapter discusses the issue of rotational invariance of a texture analysis system: i.e. one desires that the outcome of the analysis is not aaected by the orientation of the input image. We argue that the orthogonal DWT (section 3.4) is very impractical for such an analysis due to its separable nature in 2 dimensions. We therefore employ the non-separable wavelet frames (section 3.3). We d...
متن کاملImproving Rotation Invariance of the Volume Local Binary Pattern Operator
Dynamic texture is an extension of texture to the temporal domain. Recently, a powerful method for dynamic texture recognition based on volume local binary patterns (VLBP) was proposed. In this paper, we investigate improvements of the original VLBP operator. A proof on the relation of the two rotation invariant VLBP patterns is given. Methods for obtaining rotation invariance are experimentall...
متن کاملDigging Deep into the Layers of CNNs: In Search of How CNNs Achieve View Invariance
This paper is focused on studying the view-manifold structure in the feature spaces implied by the different layers of Convolutional Neural Networks (CNN). There are several questions that this paper aims to answer: Does the learned CNN representation achieve viewpoint invariance? How does it achieve viewpoint invariance? Is it achieved by collapsing the view manifolds, or separating them while...
متن کاملLearning Steerable Filters for Rotation Equivariant CNNs
In many machine learning tasks it is desirable that a model’s prediction transforms in an equivariant way under transformations of its input. Convolutional neural networks (CNNs) implement translational equivariance by construction; for other transformations, however, they are compelled to learn the proper mapping. In this work, we develop Steerable Filter CNNs (SFCNNs) which achieve joint equi...
متن کاملRotation Invariance Neural Network
Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Medical Image Analysis
سال: 2020
ISSN: 1361-8415
DOI: 10.1016/j.media.2020.101756