An "augmentation-free" rotation invariant classification scheme on point-cloud and its application to neuroimaging
Recent years have witnessed the emergence and increasing popularity of 3D medical imaging techniques with the development of 3D sensors and technology. However, achieving geometric invariance in the processing of 3D medical images is computationally expensive but nonetheless essential due to the presence of possible errors caused by rigid registration techniques. An alternative way to analyze medical imaging is by understanding the 3D shapes represented in terms of point-cloud. Though in the medical imaging community, 3D point-cloud processing is not a "go-to" choice, it is a canonical way to preserve rotation invariance. Unfortunately, due to the presence of discrete topology, one can not use the standard convolution operator on point-cloud. To the best of our knowledge, the existing ways to do "convolution" can not preserve the rotation invariance without explicit data augmentation. Therefore, we propose a rotation invariant convolution operator by inducing topology from hypersphere. Experimental validation has been performed on publicly available OASIS dataset in terms of classification accuracy between subjects with (without) dementia, demonstrating the usefulness of our proposed method in terms of model complexity, classification accuracy, and last but most important invariance to rotations.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationGeneral ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
POIRot: A rotation invariant omni-directional pointnet
Point-cloud is an efficient way to represent 3D world. Analysis of point-cloud deals with understanding the underlying 3D geometric structure. But due to the lack of smooth topology, and hence the lack of neighborhood st…
Data AugmentationPoint Cloud SegmentationS2P-Net: A Spectral-Spatial Polar Network for Rotation-Invariant Object Recognition in Low-Data Regimes
We present S2P-Net (Spectral-Spatial Polar Network), a compact deep learning architecture that achieves mathematically guaranteed rotation invariance without data augmentation. In this Paper, we also made a comparison to…
Object RecognitionData AugmentationEffective Rotation-invariant Point CNN with Spherical Harmonics kernels
We present a novel rotation invariant architecture operating directly on point cloud data. We demonstrate how rotation invariance can be injected into a recently proposed point-based PCNN architecture, at all layers of t…
Data AugmentationPointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution
Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, P…
3D Feature MatchingData AugmentationRIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network
In recent years, convolutional neural network has shown good performance in many image processing and computer vision tasks. However, a standard CNN model is not invariant to image rotations. In fact, even slight rotatio…
Data Augmentation