Group theoretical methods in image processing
Group theoretical methods in image processing
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regular Article: Computing Fourier Transforms and Convolutions on the 2-Sphere
Advances in Applied Mathematics
Machine Learning
Machine Learning
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
SMI '04 Proceedings of the Shape Modeling International 2004
Spherical Diffusion for 3D Surface Smoothing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Invariant kernel functions for pattern analysis and machine learning
Machine Learning
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Learning Spatial Context: Using Stuff to Find Things
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Rotational Invariance Based on Fourier Analysis in Polar and Spherical Coordinates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Fast Keypoint Recognition Using Random Ferns
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D invariants with high robustness to local deformations for automated pollen recognition
Proceedings of the 29th DAGM conference on Pattern recognition
Spherical Correlation of Visual Representations for 3D Model Retrieval
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orientation invariant 3D object classification using hough transform based methods
Proceedings of the ACM workshop on 3D object retrieval
Hough transform and 3D SURF for robust three dimensional classification
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Local Rotation Invariant Patch Descriptors for 3D Vector Fields
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
SHOG: spherical HOG descriptors for rotation invariant 3D object detection
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Multiresolution circular harmonic decomposition
IEEE Transactions on Signal Processing
Fast Rotation Invariant 3D Feature Computation Utilizing Efficient Local Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivariant Holomorphic Filters for Contour Denoising and Rapid Object Detection
IEEE Transactions on Image Processing
Computing Steerable Principal Components of a Large Set of Images and Their Rotations
IEEE Transactions on Image Processing
2D/3D rotation-invariant detection using equivariant filters and kernel weighted mapping
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Learning rotation-aware features: From invariant priors to equivariant descriptors
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Learning equivariant structured output SVM regressors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
SHREC'09 track: generic shape retrieval
EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
International Journal of Computer Vision
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The histogram of oriented gradients (HOG) is widely used for image description and proves to be very effective. In many vision problems, rotation-invariant analysis is necessary or preferred. Popular solutions are mainly based on pose normalization or learning, neglecting some intrinsic properties of rotations. This paper presents a method to build rotation-invariant HOG descriptors using Fourier analysis in polar/spherical coordinates, which are closely related to the irreducible representation of the 2D/3D rotation groups. This is achieved by considering a gradient histogram as a continuous angular signal which can be well represented by the Fourier basis (2D) or spherical harmonics (3D). As rotation-invariance is established in an analytical way, we can avoid discretization artifacts and create a continuous mapping from the image to the feature space. In the experiments, we first show that our method outperforms the state-of-the-art in a public dataset for a car detection task in aerial images. We further use the Princeton Shape Benchmark and the SHREC 2009 Generic Shape Benchmark to demonstrate the high performance of our method for similarity measures of 3D shapes. Finally, we show an application on microscopic volumetric data.