Готові списки джерел за темами / Invariant Object Recognition

Добірка наукової літератури з теми "Invariant Object Recognition"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Invariant Object Recognition"

1

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 (April 27, 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 were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant object recognition. These results indicate that invariant object recognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world.
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2

Isik, Leyla, Ethan M. Meyers, Joel Z. Leibo, and Tomaso Poggio. "The dynamics of invariant object recognition in the human visual system." Journal of Neurophysiology 111, no. 1 (January 1, 2014): 91–102. http://dx.doi.org/10.1152/jn.00394.2013.

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The human visual system can rapidly recognize objects despite transformations that alter their appearance. The precise timing of when the brain computes neural representations that are invariant to particular transformations, however, has not been mapped in humans. Here we employ magnetoencephalography decoding analysis to measure the dynamics of size- and position-invariant visual information development in the ventral visual stream. With this method we can read out the identity of objects beginning as early as 60 ms. Size- and position-invariant visual information appear around 125 ms and 150 ms, respectively, and both develop in stages, with invariance to smaller transformations arising before invariance to larger transformations. Additionally, the magnetoencephalography sensor activity localizes to neural sources that are in the most posterior occipital regions at the early decoding times and then move temporally as invariant information develops. These results provide previously unknown latencies for key stages of human-invariant object recognition, as well as new and compelling evidence for a feed-forward hierarchical model of invariant object recognition where invariance increases at each successive visual area along the ventral stream.
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3

DiCarlo, James J., and David D. Cox. "Untangling invariant object recognition." Trends in Cognitive Sciences 11, no. 8 (August 2007): 333–41. http://dx.doi.org/10.1016/j.tics.2007.06.010.

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4

Stejskal, Tomáš. "2D-Shape Analysis Using Shape Invariants." Applied Mechanics and Materials 613 (August 2014): 452–57. http://dx.doi.org/10.4028/www.scientific.net/amm.613.452.

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High efficiency detection of two-dimensional objects is achieved by an appropriate choice of object invariants. The aim is to show an example of the construction of an algorithm for rapid identification also for highly complex objects. The program structure works in a similar way as animal systems in nature. Differentiating runs from whole to details. They are used to shape invariants. The program algorithm is specifically used a surfaces invariant, which represents a whole. Then was used a boundary length invariant around the object. Finally, the chord distribution code was used, which represent a detail of object recognition. The actual computational algorithms are not software-intensive and easy to debug. System uses the redundancy of uncertain information about the shape. In principle, chosen a certain balance between the confidence level of recognition and repetition of shape recognition by various methods.
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Schurgin, Mark, and Jonathan Flombaum. "Invariant object recognition enhanced by object persistence." Journal of Vision 15, no. 12 (September 1, 2015): 239. http://dx.doi.org/10.1167/15.12.239.

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6

Cox, David D., Philip Meier, Nadja Oertelt, and James J. DiCarlo. "'Breaking' position-invariant object recognition." Nature Neuroscience 8, no. 9 (August 7, 2005): 1145–47. http://dx.doi.org/10.1038/nn1519.

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7

Rolls, Edmund T., and Simon M. Stringer. "Invariant visual object recognition: A model, with lighting invariance." Journal of Physiology-Paris 100, no. 1-3 (July 2006): 43–62. http://dx.doi.org/10.1016/j.jphysparis.2006.09.004.

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CHAN, LAI-WAN. "NEURAL NETWORKS FOR COLLECTIVE TRANSLATIONAL INVARIANT OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 06, no. 01 (April 1992): 143–56. http://dx.doi.org/10.1142/s0218001492000084.

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A novel method using neural networks for translational invariant object recognition is described in this paper. The objective is to enable the recognition of objects in any shifted position when the objects are presented to the network in only one standard location during the training procedure. With the presence of multiple or overlapped objects in the scene, translational invariant object recognition is a very difficult task. Noise corruption of the image creates another difficulty. In this paper, a novel approach is proposed to tackle this problem, using neural networks with the consideration of multiple objects and the presence of noise. This method utilizes the secondary responses activated by the backpropagation network. A confirmative network is used to obtain the object identification and location, based on these secondary responses. Experimental results were used to demonstrate the ability of this approach.
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9

Sufi karimi, Hiwa, and Karim Mohammadi. "Rotational invariant biologically inspired object recognition." IET Image Processing 14, no. 15 (December 2020): 3762–73. http://dx.doi.org/10.1049/iet-ipr.2019.1621.

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Kim, Kye-Kyung, Jae-Hong Kim, and Jae-Yun Lee. "Illumination and Rotation Invariant Object Recognition." Journal of the Korea Contents Association 12, no. 11 (November 28, 2012): 1–8. http://dx.doi.org/10.5392/jkca.2012.12.11.001.

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Дисертації з теми "Invariant Object Recognition"

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Srestasathiern, Panu. "View Invariant Planar-Object Recognition." The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420564069.

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Tonge, Ashwini Kishor. "Object Recognition Using Scale-Invariant Chordiogram." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984116/.

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This thesis describes an approach for object recognition using the chordiogram shape-based descriptor. Global shape representations are highly susceptible to clutter generated due to the background or other irrelevant objects in real-world images. To overcome the problem, we aim to extract precise object shape using superpixel segmentation, perceptual grouping, and connected components. The employed shape descriptor chordiogram is based on geometric relationships of chords generated from the pairs of boundary points of an object. The chordiogram descriptor applies holistic properties of the shape and also proven suitable for object detection and digit recognition mechanisms. Additionally, it is translation invariant and robust to shape deformations. In spite of such excellent properties, chordiogram is not scale-invariant. To this end, we propose scale invariant chordiogram descriptors and intend to achieve a similar performance before and after applying scale invariance. Our experiments show that we achieve similar performance with and without scale invariance for silhouettes and real world object images. We also show experiments at different scales to confirm that we obtain scale invariance for chordiogram.
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3

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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Booth, Michael C. A. "Temporal lobe mechanisms for view-invariant object recognition." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299094.

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Hsu, Tao-i. "Affine invariant object recognition by voting match techniques." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22865.

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Approved for public release; distribution is unlimited
This thesis begins with a general survey of different model based systems for object recognition. The advantage and disadvantage of those systems are discussed. A system is then selected for study because of its effective Affine invariant matching [Ref. 1] characteristic. This system involves two separate phases, the modeling and the recognition. One is done off-line and the other is done on-line. A Hashing technique is implemented to achieve fast accessing and voting. Different test data sets are used in experiments to illustrate the recognition capabilities of this system. This demonstrates the capabilities of partial match, recognizing objects under similarity transformation applied to the models, and the results of noise perturbation. The testing results are discussed, and related experiences and recommendations are presented.
http://archive.org/details/affineinvarianto00hsut
Captain, Taiwan Republic of China Army
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6

Robinson, Leigh. "Invariant object recognition : biologically plausible and machine learning approaches." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/83167/.

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Understanding the processes that facilitate object recognition is a task that draws on a wide range of fields, integrating knowledge from neuroscience, psychology, computer science and mathematics. The substantial work done in these fields has lead to two major outcomes: Firstly, a rich interplay between computational models and biological experiments that seek to explain the biological processes that underpin object recognition. Secondly, engineered vision systems that on many tasks are approaching the performance of humans. This work first highlights the importance of ensuring models which are aiming for biological relevance actually produce biologically plausible representations that are consistent with what has been measured within the primate visual cortex. To accomplish this two leading biologically plausible models, HMAX and VisNet are compared on a set of visual processing tasks. The work then changes approach, focusing on models that do not explicitly seek to model any biological process, but rather solve a particular vision task with the goal being increased performance. This section explores the recently discovered problem convolution networks being susceptible to adversarial exemplars. An extension of previous work is shown that allows state-of-the-art networks to be fooled to classify any image as any label while leaving that original image visually unchanged. Secondly an efficient implementation of applying dropout in a batchwise fashion is introduced that approximately halves the computational cost, allowing models twice as large to be trained. Finally an extension to Deep Belief Networks is proposed that constrains the connectivity of the a given layer to that of a topologically local region of the previous one.
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7

Allan, Moray. "Sprite learning and object category recognition using invariant features." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/2430.

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This thesis explores the use of invariant features for learning sprites from image sequences, and for recognising object categories in images. A popular framework for the interpretation of image sequences is the layers or sprite model of e.g. Wang and Adelson (1994), Irani et al. (1994). Jojic and Frey (2001) provide a generative probabilistic model framework for this task, but their algorithm is slow as it needs to search over discretised transformations (e.g. translations, or affines) for each layer. We show that by using invariant features (e.g. Lowe’s SIFT features) and clustering their motions we can reduce or eliminate the search and thus learn the sprites much faster. The algorithm is demonstrated on example image sequences. We introduce the Generative Template of Features (GTF), a parts-based model for visual object category detection. The GTF consists of a number of parts, and for each part there is a corresponding spatial location distribution and a distribution over ‘visual words’ (clusters of invariant features). We evaluate the performance of the GTF model for object localisation as compared to other techniques, and show that such a relatively simple model can give state-of- the-art performance. We also discuss the connection of the GTF to Hough-transform-like methods for object localisation.
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8

Bone, Peter. "Fully invariant object recognition and tracking from cluttered scenes." Thesis, University of Sussex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444109.

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Banarse, D. S. "A generic neural network architecture for deformation invariant object recognition." Thesis, Bangor University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362146.

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Sim, Hak Chuah. "Invariant object matching with a modified dynamic link network." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/256269/.

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Книги з теми "Invariant Object Recognition"

1

Object recognition through invariant indexing. Oxford: Oxford University Press, 1995.

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2

Lamdan, Yehezkel. Object recognition by affine invariant matching. New York: Courant Institute of Mathematical Sciences, New York University, 1988.

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3

Grace, Alan Edward. Adaptive segmentation for aspect invariant object recognition. Birmingham: Universityof Birmingham, 1993.

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4

Hsu, Tao-i. Affine invariant object recognition by voting match techniques. Monterey, Calif: Naval Postgraduate School, 1988.

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5

Kyrki, Ville. Local and global feature extraction for invariant object recognition. Lappeenranta, Finland: Lappeenranta University of Technology, 2002.

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6

Soucek, Branko. Fast learning and invariant object recognition: The sixth-generation breakthrough. New York: Wiley, 1992.

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7

Group, IRIS, ed. Fast learning and invariant object recognition: The sixth-generation breakthrough. New York: Wiley, 1992.

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8

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

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9

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

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10

Rothwell, C. a. Object Recognition Through Invariant Indexing. Oxford University Press, 1995.

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Частини книг з теми "Invariant Object Recognition"

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Rodrigues, João, and J. M. Hans du Buf. "Invariant Multi-scale Object Categorisation and Recognition." In Pattern Recognition and Image Analysis, 459–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72847-4_59.

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2

Bart, Evgeniy, Evgeny Byvatov, and Shimon Ullman. "View-Invariant Recognition Using Corresponding Object Fragments." In Lecture Notes in Computer Science, 152–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24671-8_12.

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3

Ben-Arie, Jezekiel, and Zhiqian Wang. "Gabor kernels for affine—invariant object recognition." In Gabor Analysis and Algorithms, 409–26. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-2016-9_14.

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4

Villamizar, Michael, Alberto Sanfeliu, and Juan Andrade-Cetto. "Orientation Invariant Features for Multiclass Object Recognition." In Lecture Notes in Computer Science, 655–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11892755_68.

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Wechsler, Harry. "Network Representations and Match Filters for Invariant Object Recognition." In Pattern Recognition Theory and Applications, 269–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_21.

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6

Yang, Mingqiang, Kidiyo Kpalma, and Joseph Ronsin. "Shape-Based Invariant Feature Extraction for Object Recognition." In Advances in Reasoning-Based Image Processing Intelligent Systems, 255–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24693-7_9.

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Li, Zhenxiao, and Liqing Zhang. "Affine Invariant Topic Model for Generic Object Recognition." In Advances in Neural Networks - ISNN 2010, 152–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13318-3_20.

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Teo, Choon Hui, and Yong Haur Tay. "Invariant Object Recognition Using Circular Pairwise Convolutional Networks." In Lecture Notes in Computer Science, 1232–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_167.

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Chen, Guangyi, Tien Dai Bui, Adam Krzyzak, and Yongjia Zhao. "Invariant Object Recognition Using Radon and Fourier Transforms." In Advances in Neural Networks – ISNN 2013, 650–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39065-4_78.

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Patekar, Rahul, and Abhijeet Nandedkar. "Distance Invariant RGB-D Object Recognition Using DSMS System." In Communications in Computer and Information Science, 135–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6315-7_11.

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Тези доповідей конференцій з теми "Invariant Object Recognition"

1

Jouan, Alexandre, and Henri H. Arsenault. "Invariant principal components for pattern recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1987. http://dx.doi.org/10.1364/oam.1987.ma1.

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Methods for rotation-invariant pattern recognition using matched filters can be based on recognizing some invariant feature from the object of interest. Various techniques using circular harmonic components as the invariant features have been proposed. A new approach introduced here is to find a set of angular principal components q k (θ) for the object of interest f(r, θ). The principal components q k (θ) are found by defining vectors v rk (θ) which are the values along constant radii r k of the object and finding their principal components. Matched filters derived from those principal components may be used for pattern recognition that is invariant under rotations and changes of scale. If the object itself is used as the input to the filter, the scale invariance does not function well. Scale invariance is obtained when the projection of the object on the eigenvectors is used as the input to the principal component matched filter. This avoids calculation of the principal components for each target of interest, a much more time-consuming task than calculating the projections. Experimental results are shown.
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2

Srestasathiern, Panu, and Alper Yilmaz. "View invariant object recognition." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761238.

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3

Yu, Francis T. S., Xiaoyang Li, Eddy Tam, and Don A. Gregory. "Joint transformation correlation implementation of the circular harmonic expansion for pattern recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.mv7.

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A real-value implementation of circular harmonic expansion by a joint transformation correlation scheme to achieve rotation-invariant pattern recognition is presented. Both the real and imaginary parts of one component of the circular harmonic expansion of a template are used as reference functions. Based on the measurements of the two peaks of the correlation between input object and the two reference functions, rotation-invariant patten recognition is achieved. The rotation angle of the input object with respect to the template is recovered. By using a ¼ waveplate, the rotation invariance based on only one correlation peak is obtained while sacrificing shift invariance. The proper circular harmonic expansion center plays an important role in the circular harmonic expansion-based rotation-invariant pattern recognition. An efficient and effective way of finding the proper center is discussed. The ability of the reference functions to discriminate the object which contains the template from other objects is analyzed. Experimental results are included.
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4

Lejeune, Claude, Young Sheng, and Henri H. Arsenault. "Optoneural system for invariant pattern recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.mii2.

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An optoneural system, which consists of an optical correlator and a neural network, is developed for invariant pattern recognition. The correlator uses Fourier-Mellin spatial filters (FMF) for feature extraction. The impulse response of a FMF is equal to the kernel function of the circular-Fourier and radial-Mellin transform. The filter itself contains no object information and yields an unique output for each input object. The features used as input to the neural network are the geometrical parameters of the 2-D pattern of the output local peaks. The neural network used is a multilayer feed forward net with a back propagation learning rule. The advantages of this approach are that a FMF may be used for all input objects without the need for training or updating the filter, and that the number of the extracted features is small, making it possible to use a small neural network. This Fourier-Mellin optoneural system shows multiple object recognition, which is invariant not only to rotation and scale changes but also to translations of the input objects.
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5

Stiller, Peter F. "Global invariant methods for object recognition." In International Symposium on Optical Science and Technology, edited by Longin J. Latecki, David M. Mount, Angela Y. Wu, and Robert A. Melter. SPIE, 2001. http://dx.doi.org/10.1117/12.447278.

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Sheng, Yunlong, and Henri H. Arsenault. "Shift invariant Fourier-Mellin features for pattern recognition." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.fp6.

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The scale-invariant and rotation-invariant Fourier-Mellin transform depends on the position of the object. This limits its field of use. We propose filters of the form rs–2 exp(jmθ) with complex-valued s = v + jw. The filter contains no object information. The correlation function of the filter with an input object at every point (x', y') in the output plane is the Fourier-Mellin transform of the object developed about the origin (x', y') of a polar coordinate system. The shift-invariant features are then extracted: these are the distances between the correlation maxima from the filters with different orders s and m and the intensities of those maxima. The logpolar coordinate transform normally used for the optical Fourier-Mellin transform is not required. The method is invariant under changes of position, scale, orientation, rotation, and intensity. It also allows input containing multiple objects simultaneously. The invariant features can be used for image classification or as inputs to neural networks for invariant associative and adaptive pattern recognition.
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7

Li, Bingcheng. "Mask size independent and orientation invariant object finding." In Automatic Target Recognition XXVIII, edited by Firooz A. Sadjadi and Abhijit Mahalanobis. SPIE, 2018. http://dx.doi.org/10.1117/12.2305571.

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Raytchev, Bisser, Tetsuya Mino, Toru Tamaki, and Kazufumi Kaneda. "View-Invariant Object Recognition with Visibility Maps." In 2010 20th International Conference on Pattern Recognition (ICPR). IEEE, 2010. http://dx.doi.org/10.1109/icpr.2010.260.

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Urolagin, Siddhaling, K. V. Prema, and N. V. Subba Reddy. "Rotation invariant object recognition using Gabor filters." In 2010 5th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2010. http://dx.doi.org/10.1109/iciinfs.2010.5578669.

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Ganesharajah, B., S. Mahesan, and U. A. J. Pinidiyaarachchi. "Robust invariant descriptors for visual object recognition." In 2011 IEEE 6th International Conference on Industrial and Information Systems (ICIIS). IEEE, 2011. http://dx.doi.org/10.1109/iciinfs.2011.6038059.

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Звіти організацій з теми "Invariant Object Recognition"

1

Nagao, Kenji, and Eric Grimson. Object Recognition by Alignment Using Invariant Projections of Planar Surfaces. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada279841.

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2

Voils, Danny. Scale Invariant Object Recognition Using Cortical Computational Models and a Robotic Platform. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.632.

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3

Kim, Dae-Shik. Predictive Coding Strategies for Invariant Object Recognition and Volitional Motion Control in Neuromorphic Agents. Fort Belvoir, VA: Defense Technical Information Center, September 2015. http://dx.doi.org/10.21236/ada626818.

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4

Serre, Thomas, and Maximilian Riesenhuber. Realistic Modeling of Simple and Complex Cell Tuning in the HMAX Model, and Implications for Invariant Object Recognition in Cortex. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada459692.

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5

Weiss, Isaac. Geometric Invariants and Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, August 1992. http://dx.doi.org/10.21236/ada255317.

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6

Logothetis, Nikos K., Thomas Vetter, Anya Hurlbert, and Tomaso Poggio. View-Based Models of 3D Object Recognition and Class-Specific Invariance. Fort Belvoir, VA: Defense Technical Information Center, April 1994. http://dx.doi.org/10.21236/ada279858.

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7

Kokurina, O. Yu. VIABILITY AND RESILIENCE OF THE MODERN STATE: PATTERNS OF PUBLIC-LEGAL ADMINISTRATION AND REGULATION. Kokurina O.Yu., February 2022. http://dx.doi.org/10.12731/kokurina-21-011-31155.

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Анотація:
The modern understanding of the state as a complex social system allows us to assert that its resilience is based on ensuring systemic homeostasis as a stabilizing dynamic mechanism for resolving contradictions arising in society associated with the threat of losing control over the processes of public administration and legal regulation. Public administration is a kind of social management that ensures the organization of social relations and processes, giving the social system the proper coordination of actions, the necessary orderliness, sustainability and stability. The problem of state resilience is directly related to the resilience of state (public) administration requires a «breakthrough in traditional approaches» and recognition of «the state administration system as an organic system, the constituent parts and elements of which are diverse and capable of continuous self-development». Within the framework of the «organizational point of view» on the control methodology, there are important patterns and features that determine the viability and resilience of public administration and regulation processes in the state and society. These include: W. Ashby's cybernetic law of required diversity: for effective control, the degree of diversity of the governing body must be no less than the degree of diversity of the controlled object; E. Sedov’s law of hierarchical compensations: in complex, hierarchically organized and networked systems, the growth of diversity at the top level in the structure of the system is ensured by a certain limitation of diversity at its lower levels; St. Beer’s principle of invariance of the structure of viable social systems. The study was supported by the RFBR and EISI within the framework of the scientific project No. 21-011-31155.
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