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Journal articles on the topic 'Rotation Invariance'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Zhang, Dingxin, Jianhui Yu, Chaoyi Zhang, and Weidong Cai. "PaRot: Patch-Wise Rotation-Invariant Network via Feature Disentanglement and Pose Restoration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3418–26. http://dx.doi.org/10.1609/aaai.v37i3.25450.

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Recent interest in point cloud analysis has led rapid progress in designing deep learning methods for 3D models. However, state-of-the-art models are not robust to rotations, which remains an unknown prior to real applications and harms the model performance. In this work, we introduce a novel Patch-wise Rotation-invariant network (PaRot), which achieves rotation invariance via feature disentanglement and produces consistent predictions for samples with arbitrary rotations. Specifically, we design a siamese training module which disentangles rotation invariance and equivariance from patches de

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Liu, Yilin, Xuqiang Shao, and Zhaohui Wu. "Rotation Invariant Predictor-Corrector for Smoothed Particle Hydrodynamics Data Visualization." Symmetry 13, no. 3 (2021): 382. http://dx.doi.org/10.3390/sym13030382.

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In order to extract the vortex features more accurately, a new method of vortex feature extraction on the Smoothed Particle Hydrodynamics data is proposed in the current study by combining rotation invariance and predictor-corrector method. There is a limitation in the original rotation invariance, which can only extract the vortex features that perform equal-speed rotations. The limitation is slightly weakened to a situation that the rotation invariance can be used, given that a specific axis is existed in the fluid to replace the axis needed for it. Therefore, as long as the axis exists, the

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3

Yu, Jianhui, Chaoyi Zhang, and Weidong Cai. "Rethinking Rotation Invariance with Point Cloud Registration." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3313–21. http://dx.doi.org/10.1609/aaai.v37i3.25438.

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Recent investigations on rotation invariance for 3D point clouds have been devoted to devising rotation-invariant feature descriptors or learning canonical spaces where objects are semantically aligned. Examinations of learning frameworks for invariance have seldom been looked into. In this work, we review rotation invariance (RI) in terms of point cloud registration (PCR) and propose an effective framework for rotation invariance learning via three sequential stages, namely rotation-invariant shape encoding, aligned feature integration, and deep feature registration. We first encode shape des

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4

Maes, Koen C., and Bernard De Baets. "Rotation-invariant t-norms: The rotation invariance property revisited." Fuzzy Sets and Systems 160, no. 1 (2009): 44–51. http://dx.doi.org/10.1016/j.fss.2008.07.012.

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5

Kudari, Medha, Shivashankar S., and Prakash S. Hiremath. "Illumination and Rotation Invariant Texture Representation for Face Recognition." International Journal of Computer Vision and Image Processing 10, no. 2 (2020): 58–69. http://dx.doi.org/10.4018/ijcvip.2020040105.

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This article presents a novel approach for illumination and rotation invariant texture representation for face recognition. A gradient transformation is used as illumination invariance property and a Galois Field for the rotation invariance property. The normalized cumulative histogram bin values of the Gradient Galois Field transformed image represent the illumination and rotation invariant texture features. These features are further used as face descriptors. Experimentations are performed on FERET and extended Cohn Kanade databases. The results show that the proposed method is better as com

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6

Lou, Yujing, Zelin Ye, Yang You, et al. "CRIN: Rotation-Invariant Point Cloud Analysis and Rotation Estimation via Centrifugal Reference Frame." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1817–25. http://dx.doi.org/10.1609/aaai.v37i2.25271.

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Various recent methods attempt to implement rotation-invariant 3D deep learning by replacing the input coordinates of points with relative distances and angles. Due to the incompleteness of these low-level features, they have to undertake the expense of losing global information. In this paper, we propose the CRIN, namely Centrifugal Rotation-Invariant Network. CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations via centrifugal reference frames. Aided by centrifugal reference frames, each point corresponds to a discrete rot

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7

Qi, Shuren, Yushu Zhang, Chao Wang, and Rushi Lan. "Representing Blurred Image without Deblurring." Mathematics 11, no. 10 (2023): 2239. http://dx.doi.org/10.3390/math11102239.

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The effective recognition of patterns from blurred images presents a fundamental difficulty for many practical vision tasks. In the era of deep learning, the main ideas to cope with this difficulty are data augmentation and deblurring. However, both facing issues such as inefficiency, instability, and lack of explainability. In this paper, we explore a simple but effective way to define invariants from blurred images, without data augmentation and deblurring. Here, the invariants are designed from Fractional Moments under Projection operators (FMP), where the blur invariance and rotation invar

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8

Lai, Yi Qiang. "Rotation Moment Invariant Feature Extraction Techniques for Image Matching." Applied Mechanics and Materials 721 (December 2014): 775–78. http://dx.doi.org/10.4028/www.scientific.net/amm.721.775.

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In recently years, extracting images invariance features are gaining more attention in image matching field. Various types of methods have been used to match image successfully in a number of applications. But in mostly literatures, the rotation moment invariant properties of these invariants have not been studied widely. In this paper, we present a novel method based on Polar Harmonic Transforms (PHTs) which is consisted of a set of orthogonal projection bases to extract rotation moment invariant features. The experimental results show that the kernel computation of PHTs is simple and image f

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9

Zhang, Yunsheng, Zijing Ren, Zichen Ding, Hong Qian, Haiqiang Li, and Chao Tao. "Co-ECL: Covariant Network with Equivariant Contrastive Learning for Oriented Object Detection in Remote Sensing Images." Remote Sensing 16, no. 3 (2024): 516. http://dx.doi.org/10.3390/rs16030516.

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Contrastive learning allows us to learn general features for downstream tasks without the need for labeled data by leveraging intrinsic signals within remote sensing images. Existing contrastive learning methods encourage invariant feature learning by bringing positive samples defined by random transformations in feature spaces closer, where transformed samples of the same image at different intensities are considered equivalent. However, remote sensing images differ from natural images in their top-down perspective results in the arbitrary orientation of objects and in that the images contain

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Xu, Rui, Yong Gao, and Lijing Shao. "Signatures of Lorentz Violation in Continuous Gravitational-Wave Spectra of Ellipsoidal Neutron Stars." Galaxies 9, no. 1 (2021): 12. http://dx.doi.org/10.3390/galaxies9010012.

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We studied the effects of the Lorentz invariance violation on the rotation of neutron stars (NSs) in the minimal gravitational Standard-Model Extension framework, and calculated the quadrupole radiation generated by them. Aiming at testing Lorentz invariance with observations of continuous gravitational waves (GWs) from rotating NSs in the future, we compared the GW spectra of a rotating ellipsoidal NS under Lorentz-violating gravity with those of a Lorentz-invariant one. The former were found to possess frequency components higher than the second harmonic, which does not happen for the latter

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He, Jianmeng, Xin Jiang, Zhicheng Hao, Ming Zhu, Wen Gao, and Shi Liu. "LPHOG: A Line Feature and Point Feature Combined Rotation Invariant Method for Heterologous Image Registration." Remote Sensing 15, no. 18 (2023): 4548. http://dx.doi.org/10.3390/rs15184548.

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Remote sensing image registration has been a very important research topic, especially the registration of heterologous images. In the research of the past few years, numerous registration algorithms for heterogenic images have been developed, especially feature-based matching algorithms, such as point feature-based or line feature-based matching methods. However, there are few matching algorithms that combine line and point features. Therefore, this study proposes a matching algorithm that combines line features and point features while achieving good rotation invariance. It comprises LSD det

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12

Kumar, Dinesh, and Dharmendra Sharma. "Feature Map Augmentation to Improve Scale Invariance in Convolutional Neural Networks." Journal of Artificial Intelligence and Soft Computing Research 13, no. 1 (2022): 51–74. http://dx.doi.org/10.2478/jaiscr-2023-0004.

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Abstract Introducing variation in the training dataset through data augmentation has been a popular technique to make Convolutional Neural Networks (CNNs) spatially invariant but leads to increased dataset volume and computation cost. Instead of data augmentation, augmentation of feature maps is proposed to introduce variations in the features extracted by a CNN. To achieve this, a rotation transformer layer called Rotation Invariance Transformer (RiT) is developed, which applies rotation transformation to augment CNN features. The RiT layer can be used to augment output features from any conv

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Horrigue, Samah, and Habib Ouerdiane. "Rotation invariance of quantum Laplacians." Stochastics 84, no. 2-3 (2011): 335–45. http://dx.doi.org/10.1080/17442508.2010.528762.

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Hobolth, Asger, and Eva B. Vedel Jensen. "STEREOLOGICAL ANALYSIS OF SHAPE." Image Analysis & Stereology 21, no. 4 (2011): 23. http://dx.doi.org/10.5566/ias.v21.ps23-s29.

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This paper concerns the problem of making stereological inference about the shape variability in a population of spatial particles. Under rotational invariance the shape variability can be estimated from central planar sections through the particles. A simple, but flexible, parametric model for rotation invariant spatial particles is suggested. It is shown how the parameters of the model can be estimated from observations on central sections. The corresponding model for planar particles is also discussed in some detail.

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15

VYAS, VIBHA S., and PRITI P. REGE. "GEOMETRIC TRANSFORM INVARIANT TEXTURE ANALYSIS WITH MODIFIED CHEBYSHEV MOMENTS BASED ALGORITHM." International Journal of Image and Graphics 09, no. 04 (2009): 559–74. http://dx.doi.org/10.1142/s0219467809003587.

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Texture based Geometric invariance, which comprises of rotation scale and translation (RST) invariant is finding application in various areas including industrial inspection, estimation of object range and orientation, shape analysis, satellite imaging, and medical diagnosis. Moments based techniques, apart from being computationally simple as compared to other RST invariant texture operators, are also robust in presence of noise. Zernike moments (ZM) based techniques are one of the well-established methods used for texture identification. As ZM are continuous moments, when discretization is d

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16

Camacho-Bello, César Joel, Lucia Gutiérrez-Lazcano, and Rosa M. Ortega-Mendoza. "Rotation-invariant image classification using a novel 1D CNN and Multichannel Accurate Bessel-Fourier moments." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 10, Especial3 (2022): 1–4. http://dx.doi.org/10.29057/icbi.v10iespecial3.8874.

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This work presents a proposal to use Bessel-Fourier moments as inputs to 1D convolutional neural networks in such a way that they take advantage of the inherent characteristics of moment type descriptors such as rotational invariance and minimal information redundancy. The results presented show that the proposal has a better performance than the deep neural network with rotation invariance.

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17

Qi, Kunlun, Chao Yang, Chuli Hu, Yonglin Shen, Shengyu Shen, and Huayi Wu. "Rotation Invariance Regularization for Remote Sensing Image Scene Classification with Convolutional Neural Networks." Remote Sensing 13, no. 4 (2021): 569. http://dx.doi.org/10.3390/rs13040569.

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Deep convolutional neural networks (DCNNs) have shown significant improvements in remote sensing image scene classification for powerful feature representations. However, because of the high variance and volume limitations of the available remote sensing datasets, DCNNs are prone to overfit the data used for their training. To address this problem, this paper proposes a novel scene classification framework based on a deep Siamese convolutional network with rotation invariance regularization. Specifically, we design a data augmentation strategy for the Siamese model to learn a rotation invarian

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18

Chioda, Laura, and Michael Jansson. "OPTIMAL INVARIANT INFERENCE WHEN THE NUMBER OF INSTRUMENTS IS LARGE." Econometric Theory 25, no. 3 (2009): 793–805. http://dx.doi.org/10.1017/s0266466608090300.

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This paper studies the asymptotic behavior of a Gaussian linear instrumental variables model in which the number of instruments diverges with the sample size. Asymptotic efficiency bounds are obtained for rotation invariant inference procedures and are shown to be attainable by procedures based on the limited information maximum likelihood estimator. The bounds are obtained by characterizing the limiting experiment associated with the model induced by the rotation invariance restriction.

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19

Arsenault, Henri H., Carlos Ferreira, Martin P. Levesque, and Tomasz Szpolik. "Simple filter with limited rotation invariance." Applied Optics 25, no. 18 (1986): 3230. http://dx.doi.org/10.1364/ao.25.003230.

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20

Andrearczyk, Vincent, Julien Fageot, Valentin Oreiller, Xavier Montet, and Adrien Depeursinge. "Local rotation invariance in 3D CNNs." Medical Image Analysis 65 (October 2020): 101756. http://dx.doi.org/10.1016/j.media.2020.101756.

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21

Kanatani, Ken-Ichi. "Camera rotation invariance of image characteristics." Computer Vision, Graphics, and Image Processing 39, no. 3 (1987): 328–54. http://dx.doi.org/10.1016/s0734-189x(87)80185-8.

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22

Lacroix, Vinciane. "Edge detection: what about rotation invariance?" Pattern Recognition Letters 11, no. 12 (1990): 797–802. http://dx.doi.org/10.1016/0167-8655(90)90033-x.

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23

Chidester, Benjamin, Tianming Zhou, Minh N. Do, and Jian Ma. "Rotation equivariant and invariant neural networks for microscopy image analysis." Bioinformatics 35, no. 14 (2019): i530—i537. http://dx.doi.org/10.1093/bioinformatics/btz353.

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Abstract Motivation Neural networks have been widely used to analyze high-throughput microscopy images. However, the performance of neural networks can be significantly improved by encoding known invariance for particular tasks. Highly relevant to the goal of automated cell phenotyping from microscopy image data is rotation invariance. Here we consider the application of two schemes for encoding rotation equivariance and invariance in a convolutional neural network, namely, the group-equivariant CNN (G-CNN), and a new architecture with simple, efficient conic convolution, for classifying micro

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24

Perju, Veaceslav, and Vladislav Cojuhari. "CENTRAL AND LOGARITHMIC CENTRAL IMAGE CHORD TRANSFORMATIONS FOR INVARIANT OBJECT RECOGNITION." Journal of Engineering Science XXVIII (1) (March 15, 2021): 38–46. https://doi.org/10.52326/jes.utm.2021.28(1).03.

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Pattern descriptors invariant to rotation, scaling, and translation represents an important direction in the elaboration of the real time object recognition systems. In this article, the new kinds of object descriptors based on chord transformation are presented. There are described new methods of image presentation - Central and Logarithmic Central Image Chord Transformations (CICT and LCICT). It is shown that the CICT operation makes it possible to achieve invariance to object rotation. In the case of implementation of the LCICT transformation, invariance to changes in the rotation and scale

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ABSHAGEN, J., M. HEISE, Ch HOFFMANN, and G. PFISTER. "Direction reversal of a rotating wave in Taylor–Couette flow." Journal of Fluid Mechanics 607 (June 30, 2008): 199–208. http://dx.doi.org/10.1017/s0022112008002176.

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In Taylor–Couette systems, waves, e.g. spirals and wavy vortex flow, typically rotate in the same direction as the azimuthal mean flow of the basic flow which is mainly determined by the rotation of the inner cylinder. In a combined experimental and numerical study we analysed a rotating wave of a one-vortex state in small-aspect-ratio Taylor–Couette flow which propagates either progradely or retrogradely in the inertial (laboratory) frame, i.e. in the same or opposite direction as the inner cylinder. The direction reversal from prograde to retrograde can occur at a distinct parameter value wh

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Wang, Hong Li, and Yan Hui Liu. "Rotation Invariance for Spatial Dispersion Property of the Elastic Constants." Applied Mechanics and Materials 110-116 (October 2011): 3322–26. http://dx.doi.org/10.4028/www.scientific.net/amm.110-116.3322.

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In this paper, invariance under rotation for the first-order and second-order spatial dispersion properties of the elastic constants was researched based on group theory. Softwares for calculating and judging invariance under arbitrary rotation of various order spatial dispersion tensors of the elastic constants were programmed by means of Mat lab. With the help of the softwares which we programmed, the general forms of the first-order and second-order spatial dispersion tensors of the elastic constants which belong to the group SO (2) were got, and it is judged that the above tensors of cryst

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Hong, Thanh Phuoc, and Ling Guan. "A Scale and Rotational Invariant Key-point Detector based on Sparse Coding." ACM Transactions on Intelligent Systems and Technology 12, no. 3 (2021): 1–19. http://dx.doi.org/10.1145/3452009.

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Most popular hand-crafted key-point detectors such as Harris corner, SIFT, SURF aim to detect corners, blobs, junctions, or other human-defined structures in images. Though being robust with some geometric transformations, unintended scenarios or non-uniform lighting variations could significantly degrade their performance. Hence, a new detector that is flexible with context change and simultaneously robust with both geometric and non-uniform illumination variations is very desirable. In this article, we propose a solution to this challenging problem by incorporating Scale and Rotation Invaria

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Hida, Takeyuki, Ke-Seung Lee, and Sheu-San Lee. "Conformal invariance of white noise." Nagoya Mathematical Journal 98 (June 1985): 87–98. http://dx.doi.org/10.1017/s0027763000021383.

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The remarkable link between the structure of the white noise and that of the infinite dimensional rotation group has been exemplified by various approaches in probability theory and harmonic analysis. Such a link naturally becomes more intricate as the dimension of the time-parameter space of the white noise increases. One of the powerful method to illustrate this situation is to observe the structure of certain subgroups of the infinite dimensional rotation group that come from the diffeomorphisms of the time-parameter space, that is the time change. Indeed, those subgroups would shed light o

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Zhang, Yu, Wenhao Zhang, and Jinlong Li. "Partial-to-Partial Point Cloud Registration by Rotation Invariant Features and Spatial Geometric Consistency." Remote Sensing 15, no. 12 (2023): 3054. http://dx.doi.org/10.3390/rs15123054.

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Point cloud registration is a critical problem in 3D vision tasks, and numerous learning-based point cloud registration methods have been proposed in recent years. However, a common issue with most of these methods is that their feature descriptors are rotation-sensitive, which makes them difficult to converge at large rotations. In this paper, we propose a new learning-based pipeline to address this issue, which can also handle partially overlapping 3D point clouds. Specifically, we employ rotation-invariant local features to guide the point matching task, and utilize a cross-attention mechan

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You, Yang, Yujing Lou, Qi Liu, et al. "Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12717–24. http://dx.doi.org/10.1609/aaai.v34i07.6965.

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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, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point. Our netwo

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Alsing, P. M., and G. Milburn. "Lorentz Invariance of Entanglement." Quantum Information and Computation 2, no. 6 (2002): 487–512. http://dx.doi.org/10.26421/qic2.6-4.

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We study the transformation of maximally entangled states under the action of Lorentz transformations in a fully relativistic setting. By explicit calculation of the Wigner rotation, we describe the relativistic analog of the Bell states as viewed from two inertial frames moving with constant velocity with respect to each other. Though the finite dimensional matrices describing the Lorentz transformations are non-unitary, each single particle state of the entangled pair undergoes an effective, momentum dependent, local unitary rotation, thereby preserving the entanglement fidelity of the bipar

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Xiao, Yu, Tao Wu, Yiwen Li, Xinping Ma, and Yijie Huang. "Direction-of-Arrival Estimation for 2D Coherently Distributed Sources with Nested Array Based on Matrix Reconstruction." Mathematical Problems in Engineering 2020 (May 12, 2020): 1–13. http://dx.doi.org/10.1155/2020/6494967.

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This paper has made proposition of a nested array and an estimation algorithm for direction-of-arrival (DOA) of two-dimensional (2D) coherently distributed (CD) sources. According to the difference coarray concept, double parallel hole-free virtual uniform linear arrays are generated by virtue of vectorization operation on cross-correlation matrices of subarrays. Sensor coordinates of virtual arrays are derived. Rational invariance relationships of virtual arrays are derived. According to the rotational invariance relationships, matrices satisfying rotation invariance are constructed by extrac

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Módis, Márton, and Flórián Kovács. "On rotational invariance of higher-order moments of regular polyhedra." Facta universitatis - series: Architecture and Civil Engineering, no. 00 (2023): 22. http://dx.doi.org/10.2298/fuace230630022m.

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This paper covers aspects of establishing a relationship between the highest-order rotation-invariant moments of inertia and the order of symmetry of Platonic polyhedra. Moments of inertia about arbitrary, but centroidal axes are considered. After an introductory part which summarizes the possible applications of higher-order moments of area and inertia, the revision of the already solved two-dimensional version of this problem is presented, in other words the highest-order rotation-invariant moments of area about the origin of regular m-gons are studied. As a continuation, some aspects of the

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WU Min-yang, 吴敏杨, 季. 锋. JI Feng, and 蒋. 明. JIANG Ming. "Corner detection method based on rotation invariance." Chinese Journal of Liquid Crystals and Displays 33, no. 2 (2018): 150–55. http://dx.doi.org/10.3788/yjyxs20183302.0150.

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MacCallum, M. A. H. "Totally symmetrized spinors and null rotation invariance." Classical and Quantum Gravity 37, no. 19 (2020): 195011. http://dx.doi.org/10.1088/1361-6382/aba844.

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36

Schau, Harvey C. "Shape recognition with scale and rotation invariance." Optical Engineering 31, no. 2 (1992): 268. http://dx.doi.org/10.1117/12.56071.

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Tian, Fei Ran, and Giovani L. Vasconcelos. "Rotation invariance for steady Hele–Shaw flows." Physics of Fluids A: Fluid Dynamics 5, no. 8 (1993): 1863–65. http://dx.doi.org/10.1063/1.858811.

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Hone, A. N. W. "Solutions of Abreu's Equation with Rotation Invariance." Methods and Applications of Analysis 11, no. 1 (2004): 41–64. http://dx.doi.org/10.4310/maa.2004.v11.n1.a4.

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39

Wang, Xu-Ming. "Incoherent pattern recognition with limited rotation invariance." Journal of Optics 21, no. 5 (1990): 217–21. http://dx.doi.org/10.1088/0150-536x/21/5/004.

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Kuckert, Bernd. "Spin, Statistics, and Reflections I. Rotation Invariance." Annales Henri Poincaré 6, no. 5 (2005): 849–62. http://dx.doi.org/10.1007/s00023-005-0226-8.

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Lu, Wei. "Image Retrieval Based on Contour and Relevance Feedback." Applied Mechanics and Materials 182-183 (June 2012): 1771–75. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1771.

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In this paper an algorithm is proposed to retrieve images based on contour moment invariants of image and relevance feedback. Firstly, the contour of each query image is extracted and its contour moment invariant is computed. Then according to Euclid Distance between the query image and each image in the image database, the most similar images to the query image can be found. Finally, the relevance feedback algorithm based on support vector machine (SVM) is applied to improve retrieval precision. Experimental results show that the algorithm is more accurate and efficient to retrieve images wit

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Wiskott, Laurenz, and Terrence J. Sejnowski. "Slow Feature Analysis: Unsupervised Learning of Invariances." Neural Computation 14, no. 4 (2002): 715–70. http://dx.doi.org/10.1162/089976602317318938.

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Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decor-related features, which are ordered by their degree of invariance. SFA can be applied hierarchi

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Chui, Charles K., Shao-Bo Lin, and Ding-Xuan Zhou. "Deep neural networks for rotation-invariance approximation and learning." Analysis and Applications 17, no. 05 (2019): 737–72. http://dx.doi.org/10.1142/s0219530519400074.

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Based on the tree architecture, the objective of this paper is to design deep neural networks with two or more hidden layers (called deep nets) for realization of radial functions so as to enable rotational invariance for near-optimal function approximation in an arbitrarily high-dimensional Euclidian space. It is shown that deep nets have much better performance than shallow nets (with only one hidden layer) in terms of approximation accuracy and learning capabilities. In particular, for learning radial functions, it is shown that near-optimal rate can be achieved by deep nets but not by shal

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Wood, Justin N., and Samantha M. W. Wood. "The development of newborn object recognition in fast and slow visual worlds." Proceedings of the Royal Society B: Biological Sciences 283, no. 1829 (2016): 20160166. http://dx.doi.org/10.1098/rspb.2016.0166.

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Object recognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant object recognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant object recognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks ( Gallus gallus ) require visual experience with slowly changing objects to develop invariant object recognition abilities. When newborn chicks w

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Hao, Zongbo, Tao Zhang, Mingwang Chen, and Zou Kaixu. "RRL: Regional Rotate Layer in Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 826–33. http://dx.doi.org/10.1609/aaai.v36i1.19964.

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Convolutional Neural Networks (CNNs) perform very well in image classification and object detection in recent years, but even the most advanced models have limited rotation invariance. Known solutions include the enhancement of training data and the increase of rotation invariance by globally merging the rotation equivariant features. These methods either increase the workload of training or increase the number of model parameters. To address this problem, this paper proposes a module that can be inserted into the existing networks, and directly incorporates the rotation invariance into the fe

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Zhu, Nan, Shiman Yang, and Zhongxun Wang. "Heterogenous Image Matching Fusion Based on Cumulative Structural Similarity." Electronics 14, no. 13 (2025): 2693. https://doi.org/10.3390/electronics14132693.

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To solve the problem of the limited capability of multimodal image feature descriptors constructed by gradient information and the phase consistency principle, a method of cumulative structure feature descriptor construction with rotation invariance is proposed in this paper. Firstly, we extract the direction of multi-scale and multi-direction feature point edges using the Log-Gabor odd-symmetric filter and calculate the amplitude of pixel edges based on the phase consistency principle. Then, the main direction of the key points is determined based on the edge direction feature map, and the co

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Ziegler, Franz, and Piotr Borejko. "The Method of Generalized Ray-Revisited." Journal of Mechanics 16, no. 2 (2000): 125–26. http://dx.doi.org/10.1017/s1727719100001696.

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In Section 2, ROTATION OF COORDINATES, the Authors derived the emittance functions in the Weyl-Sommerfeld representation of the wave potentials for a horizontal instantaneous single force from those known for a vertical force from conditions of invariance of the phase and amplitude of plane waves under coordinate rotation, Eqs. (10) ∼ (13) and (18) ∼ (20). That transformation implies the validity of the commonly applied identity for the (force) vector components when rotating the vector in the opposite sense to the coordinate rotation. Further, in the three-dimensional case, the vertical force

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Li, X. G., C. Ren, T. X. Zhang, Z. L. Zhu, and Z. G. Zhang. "UNMANNED AERIAL VEHICLE IMAGE MATCHING BASED ON IMPROVED RANSAC ALGORITHM AND SURF ALGORITHM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 67–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-67-2020.

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Abstract. A UAV image matching method based on RANSAC (Random Sample Consensus) algorithm and SURF (speeded up robust features) algorithm is proposed. The SURF algorithm is integrated with fast operation and good rotation invariance, scale invariance and illumination. The brightness is invariant and the robustness is good. The RANSAC algorithm can effectively eliminate the characteristics of mismatched point pairs. The pre-verification experiment and basic verification experiment are added to the RANSAC algorithm, which improves the rejection and running speed of the algorithm. The experimenta

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Magrofuoco, Nathan, Paolo Roselli, and Jean Vanderdonckt. "µV: An Articulation, Rotation, Scaling, and Translation Invariant (ARST) Multi-stroke Gesture Recognizer." Proceedings of the ACM on Human-Computer Interaction 6, EICS (2022): 1–25. http://dx.doi.org/10.1145/3532200.

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Finger-based gesture input becomes a major interaction modality for surface computing. Due to the low precision of the finger and the variation in gesture production, multistroke gestures are still challenging to recognize in various setups. In this paper, we present µV, a multistroke gesture recognizer that addresses the properties of articulation, rotation, scaling, and translation invariance by combining $P+'s cloud-matching for articulation invariance with !FTL's local shape distance for RST-invariance. We evaluate µV against five competitive recognizers on MMG, an existing gesture set, an

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Qu, Zhong, and Zheng Yong Wang. "The Improved Algorithm of Scale Invariant Feature Transform on Palmprint Recognition." Advanced Materials Research 186 (January 2011): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.186.565.

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This paper presents a new method of palmprint recognition based on improved scale invariant feature transform (SIFT) algorithm which combines the Euclidean distance and weighted sub-region. It has the scale, rotation, affine, perspective, illumination invariance, and also has good robustness to the target's motion, occlusion, noise and other factors. Simulation results show that the recognition rate of the improved SIFT algorithm is higher than the recognition rate of SIFT algorithm.

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