Academic literature on the topic 'Rotation Invariance'

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Journal articles on the topic "Rotation Invariance"

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Rotation Invariance"

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Pflug, Karen. "Generalized scale invariance, differential rotation and cloud texture." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=61076.

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The standard 2D/3D picture of atmospheric dynamics of two distinct isotropic regimes separated by a "meso-scale gap" has been seriously questioned in recent years. Using satellite cloud images and the formalism of generalized scale invariance (GSI), we test the contrary hypothesis that cloud radiance fields are scaling in the range 1-1000 km.<br>Using a two-dimensional representation of GSI and three new analysis techniques, we test the following relation for each picture: $ langle vert F( lambda sp{ tilde G} vec k) vert sp2 rangle = lambda sp{-s} langle vert F( vec k) vert sp2 rangle$, where
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Liu, Kun [Verfasser], and Olaf [Akademischer Betreuer] Ronneberger. "Fourier based rotation invariance and biological image analysis : rotation invariant description and detection, and cell segmentation = Fourier-Rotationsinvarianz und biologische Bildanalyse : rotationsinvariante Beschreibung und Erkennung, und Zellsegmentierung." Freiburg : Universität, 2014. http://d-nb.info/1114829412/34.

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Mach, Taylor Joseph. "Accurate Prediction of Chiroptical Properties." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64301.

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Accurate theoretical predictions of optical rotation are of substantial utility to the chemical community enabling the determination of absolute configuration without the need for poten- tially lengthy total synthesis. The requirements for robust calculation of gas-phase optical rotation are well understood, but too expensive for routine use. In an effort to reduce this cost we have examined the performance of the LPol and ORP basis sets, created for use in density functional theory calculations of optical rotation, finding that at the coupled cluster level of theory they perform the same or b
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Auquiert, Philippe. "Interpolation de points par des splines L1 régulières." Valenciennes, 2007. http://ged.univ-valenciennes.fr/nuxeo/site/esupversions/dbb6bf8c-16db-468e-9636-4462263ac6b8.

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En 2000, Lavery sur des exemples a remarqué que les splines cubiques L1 interpolantes C1 semblent avoir une aptitude particulière à préserver la forme des données lors de changements brusques d’ordonnées et d’espaces entre les points. Ses méthodes et ses résultats sont le point de départ de cette thèse. Dans un premier temps, nous démontrons que, dans le cas d’une fonction de type Heaviside f, pour au moins trois points appartenant à f situés avant la discontinuité et au moins trois points après, il existe une unique spline cubique L1 interpolante C1. Celle-ci coïncide avec la fonction f sauf
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Poulenard, Adrien. "Structures for deep learning and topology optimization of functions on 3D shapes." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX007.

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Le domaine du traitement de la géométrie suit un cheminement similaire à celui de l'analyse d'images avec l'explosion des publications consacrées à l'apprentissage profond ces dernières années. Un important effort de recherche est en cours pour reproduire les succès de l'apprentissage profond dans le domaine de la vision par ordinateur dans le contexte de l'analyse de formes 3D. Contrairement aux images, les formes 3D peuvent peuvent être représentées de différentes manières comme des maillages ou des nuages de points souvent dépourvus d'une structure canonique. Les algorithmes d'apprentissage
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Vieira, Raissa Tavares. "Descritores robustos à rotação de texturas baseados na abordagem LMP com acréscimo da informação de Magnitude e Sinal." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-04102017-110333/.

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Classificação de imagens de textura, especialmente aquelas com mudanças significativas de rotação, iluminação, escala e ponto de vista, é um problema fundamental e desafiador na área de visão computacional. Esta tese propõe dois descritores de imagem simples, porém eficientes, chamados de Sampled Local Mapped Pattern Magnitude (SLMP_M) e Completed Local Mapped Pattern (CLMP) aplicados na classificação de textura. Os descritores propostos são parte de um aprimoramento do descritor Local Mapped Pattern (LMP) para trabalhar de maneira eficiente com imagens de textura rotacionadas. Os métodos prop
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Mazetti, Cristina Mônica Dornelas. "Metodologia para extração de características invariantes à rotação em imagens de impressões digitais." Universidade de São Paulo, 2006. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-03032007-085126/.

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O objetivo deste trabalho é apresentar algoritmos aplicados para extração de características invariantes à rotação em imagens de impressões digitais. No pré-processamento da imagem utiliza-se detecção de bordas pelo detector de Canny tendo como resultado uma imagem binarizada e afinada. Na extração das minúcias a metodologia adotada é o número de cruzamentos (CN), que extrai os aspectos locais, tais como, as minúcias fim de linha e bifurcações. A direção das cristas locais não é utilizada porque nas imagens rotacionadas a condição de permanência das propriedades biométricas não são satisfeitas
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Minařík, Martin. "Strukturální metody identifikace objektů pro řízení průmyslového robotu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-233840.

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This PhD thesis deals with the use of structural methods of objects identification for industrial robots operation. First, the present state of knowledge in the field is described, i.e. the whole process of objects recognition with the aid of common methods of the syntactic analysis. The main disadvantage of these methods is that is impossible to recognize objects whose digitalized image is corrupted in some ways (due to excessive noise or image disturbances), objects are therefore deformed. Further, other methods for the recognition of deformed objects are described. These methods use structu
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Galante, Annamaria. "Studio di CNNs sferiche per l'apprendimento di descrittori locali su Point Cloud." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18680/.

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Nell'ambito della Computer Vision assume sempre maggiore importanza la 3D Computer Vision. Diversi sono i task e le applicazioni della 3D CV, così come diverse sono le possibili rappresentazioni dei dati. Molti di questi task richiedono la ricerca di corrispondenze tra due o più scene\oggetti 3D. Queste corrispondenze vengono individuate tramite il paradigma di Feature Matching, composto da tre step: detection, description, matching. Le performance della pipe line di feature matching sono strettamente correlate alle tecniche utilizzate in fase di description. La creazione di descriptor c
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Safia, Abdelmounaime. "Développement d’un modèle d’analyse de texture multibande." Thèse, Université de Sherbrooke, 2014. http://hdl.handle.net/11143/5990.

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Résumé : En télédétection, la texture facilite l’identification des classes de surfaces sur des critères de similitude d’organisation spatiale des pixels. Les méthodes d’analyse texturale utilisées en télédétection et en traitement d’image en général sont principalement proposées pour extraire la texture dans une seule bande à la fois. Pour les images multispectrales, ceci revient à extraire la texture dans chaque bande spectrale séparément. Cette stratégie ignore la dépendance qui existe entre la texture des différentes bandes (texture inter-bande) qui peut être une source d’information addit
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Books on the topic "Rotation Invariance"

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Blackledge, J. M. Spatial data representation for rotation invariant correlation. De Montfort University, SERCentre, 1996.

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Bussiere, Colin. Intensity scale invariant motion estimation with rotation and spatial scaling information. National Library of Canada, 1993.

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Shu, Lin. On linear structure and phase rotation invariant properties of block 2[superscript l]-PSK modulation codes. National Aeronautics and Space Administration, 1990.

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Baulieu, Laurent, John Iliopoulos, and Roland Sénéor. Relativistic Invariance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198788393.003.0002.

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The principle of relativistic invariance. Starting from the three-dimensional rotation group, the chapter proceeds with the analysis of the Lorentz group and SL(2,C). The three- and four-dimensional spinorial representations are presented.
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Rajeev, S. G. Euler’s Equations. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805021.003.0002.

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Euler derived the fundamental equations of an ideal fluid, that is, in the absence of friction (viscosity). They describe the conservation of momentum. We can derive from it the equation for the evolution of vorticity (Helmholtz equation). Euler’s equations have to be supplemented by the conservation of mass and by an equation of state (which relates density to pressure). Of special interest is the case of incompressible flow; when the fluid velocity is small compared to the speed of sound, the density may be treated as a constant. In this limit, Euler’s equations have scale invariance in addi
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Translation and Rotation Invariant Multiscale Image Registration. Storming Media, 2002.

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National Aeronautics and Space Administration (NASA) Staff. Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the Registration of Remotely Sensed Imagery. Independently Published, 2018.

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Mercati, Flavio. Best Matching: Technical Details. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789475.003.0005.

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The best matching procedure described in Chapter 4 is equivalent to the introduction of a principal fibre bundle in configuration space. Essentially one introduces a one-dimensional gauge connection on the time axis, which is a representation of the Euclidean group of rotations and translations (or, possibly, the similarity group which includes dilatations). To accommodate temporal relationalism, the variational principle needs to be invariant under reparametrizations. The simplest way to realize this in point–particle mechanics is to use Jacobi’s reformulation of Mapertuis’ principle. The cha
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Deruelle, Nathalie, and Jean-Philippe Uzan. Conservation laws. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786399.003.0045.

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This chapter studies how the ‘spacetime symmetries’ can generate first integrals of the equations of motion which simplify their solution and also make it possible to define conserved quantities, or ‘charges’, characterizing the system. As already mentioned in the introduction to matter energy–momentum tensors in Chapter 3, the concepts of energy, momentum, and angular momentum are related to the invariance properties of the solutions of the equations of motion under spacetime translations or rotations. The chapter explores these in greater detail. It first turns to isometries and Killing vect
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Deruelle, Nathalie, and Jean-Philippe Uzan. Matter in curved spacetime. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786399.003.0043.

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This chapter is concerned with the laws of motion of matter—particles, fluids, or fields—in the presence of an external gravitational field. In accordance with the equivalence principle, this motion will be ‘free’. That is, it is constrained only by the geometry of the spacetime whose curvature represents the gravitation. The concepts of energy, momentum, and angular momentum follow from the invariance of the solutions of the equations of motion under spatio-temporal translations or rotations. The chapter shows how the action is transformed, no longer under a modification of the field configur
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Book chapters on the topic "Rotation Invariance"

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Ziou, Djemel, and Jean Pierre Fabre. "Rotation invariance in edge detection." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57233-3_19.

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Banks, Thomas. "Rotation Invariance and the Hydrogen Atom." In Quantum Mechanics: An Introduction. CRC Press, 2018. http://dx.doi.org/10.1201/9780429438424-7.

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Kanatani, Kenichi. "Coordinate Rotation Invariance of Image Characteristics." In Group-Theoretical Methods in Image Understanding. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-61275-6_2.

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Matsuura, Takenobu, and Kenta Yamazaki. "A Seal Imprint Verification with Rotation Invariance." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11554028_39.

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Godunov, S. K. "Thermodynamics, Conservation Laws and their Rotation Invariance." In Godunov Methods. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-0663-8_39.

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Gräbel, Philipp, Ina Laube, Martina Crysandt, et al. "Rotation Invariance for Unsupervised Cell Representation Learning." In Bildverarbeitung für die Medizin 2021. Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-33198-6_12.

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Yang, Rui, Fei Hu, Zhiye Chen, and George Wang. "ART-InvRec: Acquiring Rotation Invariance of 3D Object Reconstruction via Adversarial Rotation." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3623-2_21.

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Scharr, Hanno, and Joachim Weickert. "An Anisotropic Diffusion Algorithm with Optimized Rotation Invariance." In Informatik aktuell. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59802-9_58.

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Feng, Jinghui, He Wang, Yang Li, and Fu Liu. "Palmprint Feature Extraction Method Based on Rotation-invariance." In Biometric Recognition. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25417-3_26.

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Haken, Hermann. "Simultaneous Invariance with Respect to Translation, Rotation and Scaling." In Springer Series in Synergetics. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10182-7_8.

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Conference papers on the topic "Rotation Invariance"

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Shi, Lu, Qi Cao, Guoqing Zhang, Jin Yi, Yansen Huang, and Yigang Cen. "Feature Rotation Invariance Learning for Point Cloud Analysis." In 2024 IEEE Smart World Congress (SWC). IEEE, 2024. https://doi.org/10.1109/swc62898.2024.00275.

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Zhang, Yuxian, Runheng Zuo, Hao Liu, Zexing Li, Ruoyao Li, and Yafei Wang. "A Descriptor with Rotation Invariance of Environment Expression Using Building Boundary of OpenStreetMap." In 2024 4th International Conference on Robotics, Automation and Intelligent Control (ICRAIC). IEEE, 2024. https://doi.org/10.1109/icraic65937.2024.00038.

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Azam, Mohamed T., Hossam Magdy Balaha, Khadiga M. Ali, et al. "A Novel Vit-Based Multi-Scaled and Rotation-Invariance Approach for Precise Differentiation Between Meningioma and Solitary Fibrous Tumor." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635228.

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Mavandadi, Sam, and Parham Aarabi. "Rotation Invariance in Images." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.365988.

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Chandra Chandra, P. N. R. L. Chandra, P. Surya Prasad, M. Vinodh Kumar, and D. Hari Hara Santosh. "Image retrieval with rotation invariance." In 2011 3rd International Conference on Electronics Computer Technology (ICECT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941683.

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Schils, George F., and Donald W. Sweeney. "Iterative Synthesis of Distortion Invariant Optical Correlation Filters." In Signal Recovery and Synthesis. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/srs.1986.wb3.

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An iterative technique can be used to synthesize pattern recognition filters that are invariant to image distortions. These distortions might be position (location), rotation, scale, and perspective. The image to detect may also appear at different intensities. In this paper, we present an iterative technique based on spectral iteration in angular Fourier space for the synthesis of rotationally, translationally, and intensity invariant optical pattern recognition filters. The iterative method allows full image information to be retained while simultaneously obtaining invariance to rotation and
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Ben-Haim, J., and J. Shamir. "Shift, rotation, and scale invariant pattern recognition by bipolar spatial filtering." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.the3.

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The objective of this work is to identify an object out of a given set of objects. Shift invariance is automatically achieved by employing spatial filters in the Fourier transform plane. To relax the strict alignment and stability requirements of the holographic filter, we use bipolar amplitude filters with minimum information content that is still sufficient for the required pattern recognition. For complete rotation invariance one may use filters with rotation symmetry, while for scale invariance, filters that are independent on the radial coordinate may be employed. Partial invariances comb
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Fei, Jiajun, and Zhidong Deng. "Incorporating Rotation Invariance with Non-invariant Networks for Point Clouds." In 2024 International Conference on 3D Vision (3DV). IEEE, 2024. http://dx.doi.org/10.1109/3dv62453.2024.00070.

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Huerta-Hernandez, Carlos Eduardo, and Hermilo Sanchez-Cruz. "Chain Code Histograms for Rotation Invariance." In 2014 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2014. http://dx.doi.org/10.1109/csci.2014.39.

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Thumwarin, P., C. Prasit, K. Yakoompai, and T. Matsuura. "Firearm identification system with rotation invariance." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5670101.

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Reports on the topic "Rotation Invariance"

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Kanatani, Ken-Ichi. Camera Rotation Invariance of Image Characteristics. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada171615.

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Rowley, Henry A., Shumeet Baluja, and Takeo Kanade. Rotation Invariant Neural Network-Based Face Detection. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada341629.

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