Relevant bibliographies by topics / Image-invariant

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Academic literature on the topic 'Image-invariant'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Image-invariant"

Miller, J. W. V., J. B. Farison, and Y. Shin. "Spatially invariant image sequences." IEEE Transactions on Image Processing 1, no. 2 (1992): 148–61. http://dx.doi.org/10.1109/83.136592.

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El Mir, Ghina, Karim Youssef, and Chady El Mir. "Conformal Image Viewpoint Invariant." Mathematics 12, no. 16 (2024): 2551. http://dx.doi.org/10.3390/math12162551.

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In this paper, we introduce an invariant by image viewpoint changes by applying an important theorem in conformal geometry stating that every surface of the Minkowski space R3,1 leads to an invariant by conformal transformations. For this, we identify the domain of an image to the disjoint union of horospheres ∐αHα of R3,1 by means of the powerful tools of the conformal Clifford algebras. We explain that every viewpoint change is given by a planar similarity and a perspective distortion encoded by the latitude angle of the camera. We model the perspective distortion by the point at infinity of

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Kejia Wang, Kejia Wang, Ziliang Ping Ziliang Ping, and and Yunlong Sheng and Yunlong Sheng. "Development of image invariant moments—a short overview." Chinese Optics Letters 14, no. 9 (2016): 091001–91011. http://dx.doi.org/10.3788/col201614.091001.

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Wang, Mei, E. Ye Wang, and Guo Hua Pan. "Image Quality Assessment Based on Invariant Moments Similarity." Advanced Materials Research 546-547 (July 2012): 565–69. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.565.

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To resolve the problems of the image quality assessment issue and the algorithm adaptability for different image size and deformation, this paper proposes a image quality assessment algorithm based on Invariant Moments Similarity. Firstly, Hu invariant moments values of original image and evaluated image are computed. Secondly the invariant moments distance is completed between original image and evaluated image. At last, the method assess the restoration image quality depend on the invariant moment distance. The experimental result shows that the algorithm result is better than MSE, PSNR, SSI

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Levkovitz, J., E. Oron, and M. Tur. "Position-invariant, rotation-invariant, and scale-invariant process for binary image recognition." Applied Optics 36, no. 14 (1997): 3035. http://dx.doi.org/10.1364/ao.36.003035.

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Zhang, Li, Gong-bin Qian, Wei-wei Xiao, and Zhen Ji. "Geometric invariant blind image watermarking by invariant Tchebichef moments." Optics Express 15, no. 5 (2007): 2251. http://dx.doi.org/10.1364/oe.15.002251.

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Rodríguez, Mariano, Julie Delon, and Jean-Michel Morel. "Fast Affine Invariant Image Matching." Image Processing On Line 8 (September 24, 2018): 251–81. http://dx.doi.org/10.5201/ipol.2018.225.

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Dinggang Shen and H. H. S. Ip. "Generalized affine invariant image normalization." IEEE Transactions on Pattern Analysis and Machine Intelligence 19, no. 5 (1997): 431–40. http://dx.doi.org/10.1109/34.589203.

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Xie, Lingxi, Jingdong Wang, Weiyao Lin, Bo Zhang, and Qi Tian. "Towards Reversal-Invariant Image Representation." International Journal of Computer Vision 123, no. 2 (2016): 226–50. http://dx.doi.org/10.1007/s11263-016-0970-x.

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Singh, Chandan, and Sukhjeet K. Ranade. "Rotation invariant moments and transforms for geometrically invariant image watermarking." Journal of Electronic Imaging 22, no. 1 (2013): 013034. http://dx.doi.org/10.1117/1.jei.22.1.013034.

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Dissertations / Theses on the topic "Image-invariant"

Zheng, Dong. "RST invariant digital image watermarking." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/26554.

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Digital image watermarking has been proposed as a method to ensure the copyright protection and credibility of images by means of embedding a known piece of digital data into host images. To be useful, a good watermarking scheme should be robust against all known attacks to a certain degree based on requirements. In this thesis, we propose a novel digital image watermarking scheme that is invariant to rotation, scaling, and translation (RST). We embed watermark in the log-polar mapping (LPM) of the Fourier magnitude spectrum of original image, and use the phase correlation between the LPM of t

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Shao, Ling. "Invariant salient regions based image retrieval." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.497094.

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Reiss, T. H. "Recognizing objects using invariant image features." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260553.

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Fedorov, Vadim. "Affine invariant image comparison and its applications." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/398002.

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La comparación de imágenes es un ingrediente fundamental en muchos problemas de procesamiento de imagen y visión por computador. Esta tesis aborda el problema de la comparación de entornos locales en imágenes, o patches, por medio de medidas de similitud (o funciones distancia). En particular, estudiamos el problema de la comparación invariante afín de imágenes a partir de sus patches, lo cual abre la puerta a un análisis más profundo de la estructura de similitud y auto-similitud existente en imágenes naturales. Nuestro trabajo parte de una aproximación axiomática reciente a las medidas de si

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Yuan, Xiao Chen. "Robust and geometric invariant digital image watermarking." Thesis, University of Macau, 2010. http://umaclib3.umac.mo/record=b2182875.

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Cuesta, Contreras Angel. "Geometric and photometric affine invariant image registration." Thesis, Heriot-Watt University, 2009. http://hdl.handle.net/10399/2228.

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This thesis aims to present a solution to the correspondence problem for the registration of wide-baseline images taken from uncalibrated cameras. We propose an affine invariant descriptor that combines the geometry and photometry of the scene to find correspondences between both views. The geometric affine invariant component of the descriptor is based on the affine arc-length metric, whereas the photometry is analysed by invariant colour moments. A graph structure represents the spatial distribution of the primitive features; i.e. nodes correspond to detected high-curvature points, whereas a

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Dahmen, Jörg. "Invariant image object recognition using Gaussian mixture densities." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=964586940.

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Liu, Yan. "Rotation, scaling and translation invariant digital image watermarking." Thesis, University of Ottawa (Canada), 2004. http://hdl.handle.net/10393/26699.

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Many digital image watermarking schemes have been proposed and are robust to common signal processing, such as compression and filtering. Geometric distortions, such as rotation, scaling, and translation (RST), are considered the most challenging attacks. This thesis is on RST invariant digital image watermarking. In this thesis, we introduce the fundamental theories and techniques necessary for RST invariant digital image watermarking, and briefly review the existing RST invariant image watermarking schemes. Then, we propose an image rectification scheme for RST invariant digital image waterm

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Kovesi, Peter. "Invariant measures of image features from phase information." University of Western Australia. Dept. of Computer Science, 1996. http://theses.library.uwa.edu.au/adt-WU2003.0006.

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If reliable and general computer vision techniques are to be developed it is crucial that we find ways of characterizing low-level image features with invariant quantities. For example, if edge significance could be measured in a way that was invariant to image illumination and contrast, higher-level image processing operations could be conducted with much greater confidence. However, despite their importance, little attention has been paid to the need for invariant quantities in low-level vision for tasks such as feature detection or feature matching. This thesis develops a number of

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Badri, Hicham. "Sparse and Scale-Invariant Methods in Image Processing." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0139/document.

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Dans cette thèse, on présente de nouvelles approches à base de parcimonie et d'invariance d' échelle pour le développement de techniques rapides et efficaces en traitement d'images. Au lieu d'utiliser la norme l1 pour imposer la parcimonie, on exploite plutôt des pénalités non-convexes qui encouragent plus la parcimonie. On propose une approche de premier ordre pour estimer une solution d'un opérateur proximal non-convexe, ce qui permet d'exploiter facilement la non-convexité. On étudie aussi le problème de pluri-parcimonie quand le problème d'optimisation est composé de plusieurs termes parci

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Books on the topic "Image-invariant"

Hermans, F. J. J. Invariant methods for image recognition. UMIST, 1993.

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Reiss, Thomas H., ed. Recognizing Planar Objects Using Invariant Image Features. Springer-Verlag, 1993. http://dx.doi.org/10.1007/bfb0017553.

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Reiss, Thomas H. Recognizing planar objects using invariant image features. Springer-Verlag, 1993.

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Tuytelaars, Tinne. Local invariant feature detectors: A survey. Now Publishers, 2008.

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Grebneva, Valentina. "A self-organizing person." Psychology of interaction in higher education. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1865830.

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The monograph examines the features of psychological interaction in higher education, based on the systemic principles of the functioning of social phenomena. From the point of view of a person-centered approach, concepts, principles and methods are revealed, defined: the structure, patterns, conditions and mechanisms of interaction in the "man — university" system. The idea of a new image of a "self-organizing person" is put forward. Theoretically, two self-organizing models, invariant to each other, are substantiated: a person and an educational environment. The results of an empirical study

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Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. IOS Press, 2003.

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Translation and Rotation Invariant Multiscale Image Registration. Storming Media, 2002.

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Dimai, Alexander. Invariant scene representations for preattentive similarity assessment: Content based image retrieval exploring low and intermediate level image information. 1999.

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M¨uhlherr, Bernhard, Holger P. Petersson, and Richard M. Weiss. Totally Wild Quadratic Forms of Type E7. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691166902.003.0015.

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This chapter assumes that (K, L, q) is a totally wild quadratic space of type E₇. The goal is to prove the proposition that takes into account Λ‎ of type E₇, D as the quaternion division algebra over K whose image in Br(K) is the Clifford invariant of q, and the trace and trace map. The chapter also considers two other propositions: the first states that if the trace map is not equal to zero, then the Moufang residues R₀ and R₁ are not indifferent; the second states that if the trace map is equal to zero, then the Moufang residues R₀ and R₁ are both indifferent.

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Invariance, déformations et reconnaissance de formes (Mathématiques et Applications). Springer, 2003.

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Book chapters on the topic "Image-invariant"

Miller, John W. V., and Malayappan Shridhar. "Illumination-Invariant Image Processing." In Machine Vision Handbook. Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_13.

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Kim, Bum-Soo, Jae-Gark Choi, and Kil-Houm Park. "Image Normalization Using Invariant Centroid for RST Invariant Digital Image Watermarking." In Digital Watermarking. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36617-2_17.

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Wang, Yibo, Ou Qi, and Xiaoyan Gao. "Image Projection Space Invariant Technology." In Application of Intelligent Systems in Multi-modal Information Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51556-0_100.

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Bascle, Benedicte, Olivier Bernier, and Vincent Lemaire. "Illumination-Invariant Color Image Correction." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_38.

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Miller, J. W. V., and M. Shridhar. "Methods for Illumination-Invariant Image Processing." In Image Technology. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-58288-2_14.

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Stasiak, Bartłomiej. "FONN-Based Affine-Invariant Image Recognition." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16295-4_34.

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Ghodsi, Ali, Jiayuan Huang, and Dale Schuurmans. "Transformation-Invariant Embedding for Image Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24673-2_42.

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Bronstein, Alexander M., and Michael M. Bronstein. "Spatially-Sensitive Affine-Invariant Image Descriptors." In Computer Vision – ECCV 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15552-9_15.

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Woo, Chaw-Seng, Jiang Du, and Binh Pham. "Geometric Invariant Domain for Image Watermarking." In Digital Watermarking. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11922841_24.

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Di Ruberto, Cecilia, Andrea Loddo, and Lorenzo Putzu. "Histological Image Analysis by Invariant Descriptors." In Image Analysis and Processing - ICIAP 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_31.

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Conference papers on the topic "Image-invariant"

Hossain, Tonmoy, Jing Ma, Jundong Li, and Miaomiao Zhang. "Invariant Shape Representation Learning for Image Classification." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00420.

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Li Xin-Wei, Guo Bao-Long, and Chen Long. "RST Invariant Image Watermarking Based on Invariant Contents." In 2010 Fourth International Conference on Genetic and Evolutionary Computing (ICGEC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icgec.2010.172.

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Bhalerao, A., and R. Wilson. "Affine Invariant Image Segmentation." In British Machine Vision Conference 2004. British Machine Vision Association, 2004. http://dx.doi.org/10.5244/c.18.18.

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Haibin Ling and D. W. Jacobs. "Deformation invariant image matching." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.67.

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Arsenault, H. H., V. François, G. April, and U. Laval. "New invariant image representation." In OSA Annual Meeting. Optica Publishing Group, 1989. http://dx.doi.org/10.1364/oam.1989.thm2.

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A new 2-D image representation of the form with is introduced, and its use for image compression and classification is considered. The image is entirely represented by its invariant coefficients a nk . The representation is a generalization of the circular harmonic decomposition, but the advantage is that any image is represented by a set of complex constants ank. The normalized coefficients are invariant under changes of scale or of orientation of the object represented by the coefficients. Experiments on grey scale imagery of aircraft show that images may be represented by a relatively small

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Wernick, Miles N., Thomas A. Isberg, and G. Michael Morris. "Rotation-invariant image classification." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.tha5.

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In a recent publication Wu and Stark1 demonstrated a method for rotation-invariant pattern classification using optimum feature extraction. Here we present an alternative method for rotation-invariant classification. First, a filter function that is useful for classification of registered images (e.g., a maximum likelihood filter or an average filter) is determined from training sets consisting of objects from each of two classes. Circular-harmonic components of the filter are then computed. An input image is cross correlated with these circular-harmonic components to form a rotation-invariant

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Wang, Jing-Wein, Tzu-Hsiung Chen, and Chia-Nan Wang. "Image Sampling for Invariant Face Recognition." In 2012 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2012. http://dx.doi.org/10.1109/is3c.2012.127.

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Chen, Yufeng, Bo Zhang, Xuying Zhao, and Zhixuan Li. "Invariant Information Learning for Image Recognition." In 2017 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, 2017. http://dx.doi.org/10.1109/icvrv.2017.00015.

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Duanmu, Xiaoyin. "Image Retrieval Using Color Moment Invariant." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.231.

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Sadat, Rafi Md Najmus, Md Abdul Mottalib, Naurin Afrin, and Zinat Sayeeda. "Multimodal and Mirror Invariant image description." In 2011 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2011. http://dx.doi.org/10.1109/icme.2011.6011899.

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Reports on the topic "Image-invariant"

Dickinson, Sven J., and Suzanne Stevenson. Viewpoint - Invariant Indexing for Content Based Image Retreival. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada391690.

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