Relevant bibliographies by topics / Object invariants

Academic literature on the topic 'Object invariants'

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Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Object invariants"

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Samad, Saleha, Anam Haq, and Shoab A. Khan. "Orientation Invariant Object Recognitions Using Geometric Moments Invariants and Color Histograms." International Journal of Computer and Electrical Engineering 7, no. 2 (2015): 101–8. http://dx.doi.org/10.17706/ijcee.2015.v7.876.

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NGUYEN, THU-TRANG, NINH-THUAN TRUONG, and VIET-HA NGUYEN. "VERIFYING JAVA OBJECT INVARIANTS AT RUNTIME." International Journal of Software Engineering and Knowledge Engineering 21, no. 04 (June 2011): 605–19. http://dx.doi.org/10.1142/s0218194011005281.

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An object invariant consisting of a set of properties that must hold for all instances of a class at any time is usually used in object-oriented design. However, verifying object invariants at runtime is always a challenging task in software verification. This paper proposes a method for verifying invariants of Java objects at runtime using AOP. Suppose that a software application is designed using UML models and its constraints are specified in OCL expressions, the software is then implemented, by default, using the UML design. They propose to construct verifiable aspects which are automatically generated from OCL constraints. These aspects can be woven into Java code to check whether object invariants are violated at runtime. Benefiting from AOP in separation of crosscutting concerns and weaving mechanisms, generated aspects can do the verification task whenever values of objects' attributes are changed. A Verification Aspect Generator (VAG) tool has been developed allowing the automatic generation of verifying aspects from the UML/OCL constraints.
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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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Chang, Bor-Yuh Evan, K. Rustan, and M. Leino. "Inferring Object Invariants." Electronic Notes in Theoretical Computer Science 131 (May 2005): 63–74. http://dx.doi.org/10.1016/j.entcs.2005.01.023.

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Pagano, Christopher C., and Michael T. Turvey. "Eigenvectors of the Inertia Tensor and Perceiving the Orientations of Limbs and Objects." Journal of Applied Biomechanics 14, no. 4 (November 1998): 331–59. http://dx.doi.org/10.1123/jab.14.4.331.

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We report several experiments directed at the ability of humans to perceive the spatial orientation of occluded objects, to position an occluded limb relative to targets or directions in the environment, and to match the spatial orientations of occluded contralateral limbs. Results suggest that each of these abilities is lied to the inertial eigenvectors of each object or limb, which correspond to the object's or limb's principal axes of rotational inertia. Discussion focuses on the dynamic nature of proprioception, the importance of physical invariants for perception, and the relation of invariants to hypothesized frames of reference for proprioception and motor control. It is suggested that the detection of invariants revealed through movement is a major mechanism in kinesthetic perception involving intact limbs, neuropathic or anesthetized limbs, prosthetic devices, and hand-held tools and implements. The inertia tensor is identified as one such invariant.
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LASENBY, JOAN, and EDUARDO BAYRO-CORROCHANO. "ANALYSIS AND COMPUTATION OF PROJECTIVE INVARIANTS FROM MULTIPLE VIEWS IN THE GEOMETRIC ALGEBRA FRAMEWORKS." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 08 (December 1999): 1105–21. http://dx.doi.org/10.1142/s0218001499000628.

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A central task of computer vision is to automatically recognize objects in real-world scenes. The parameters defining image and object spaces can vary due to lighting conditions, camera calibration and viewing positions. It is therefore desirable to look for geometric properties of the object which remain invariant under such changes. In this paper we present geometric algebra as a complete framework for the theory and computation of projective invariants formed from points and lines in computer vision. We will look at the formation of 3D projective invariants from multiple images, show how they can be formed from image coordinates and estimated tensors (F, fundamental matrix and T, trilinear tensor) and give results on simulated and real data.
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Rivlin, Ehud, and Isaac Weiss. "Deformation Invariants in Object Recognition." Computer Vision and Image Understanding 65, no. 1 (January 1997): 95–108. http://dx.doi.org/10.1006/cviu.1996.0478.

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Weiss, Isaac. "Geometric invariants and object recognition." International Journal of Computer Vision 10, no. 3 (June 1993): 207–31. http://dx.doi.org/10.1007/bf01539536.

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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 with monotonous background and clear object and meet the invariance on shift, rotation and scale transform of objects.
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Shan, J. "Photogrammetric object description with projective invariants." ISPRS Journal of Photogrammetry and Remote Sensing 52, no. 5 (October 1997): 222–28. http://dx.doi.org/10.1016/s0924-2716(97)00015-4.

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Dissertations / Theses on the topic "Object invariants"

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Self, T. Benjamin (Thomas Benjamin) 1977. "Expression and localization of object invariants." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86498.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (leaf 23).
by T. Benjamin Self.
S.B.and M.Eng.
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Vinther, Sven. "Active 3D object recognition using geometric invariants." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362974.

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Beis, Jeffrey S. "Indexing without invariants in model-based object recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq25014.pdf.

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Zhu, Yonggen. "Feature extraction and 2D/3D object recognition using geometric invariants." Thesis, King's College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362731.

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Soysal, Medeni. "Joint Utilization Of Local Appearance Descriptors And Semi-local Geometry For Multi-view Object Recognition." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614313/index.pdf.

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Novel methods of object recognition that form a bridge between today&rsquo
s local feature frameworks and previous decade&rsquo
s strong but deserted geometric invariance field are presented in this dissertation. The rationale behind this effort is to complement the lowered discriminative capacity of local features, by the invariant geometric descriptions. Similar to our predecessors, we first start with constrained cases and then extend the applicability of our methods to more general scenarios. Local features approach, on which our methods are established, is reviewed in three parts
namely, detectors, descriptors and the methods of object recognition that employ them. Next, a novel planar object recognition framework that lifts the requirement for exact appearance-based local feature matching is presented. This method enables matching of groups of features by utilizing both appearance information and group geometric descriptions. An under investigated area, scene logo recognition, is selected for real life application of this method. Finally, we present a novel method for three-dimensional (3D) object recognition, which utilizes well-known local features in a more efficient way without any reliance on partial or global planarity. Geometrically consistent local features, which form the crucial basis for object recognition, are identified using affine 3D geometric invariants. The utilization of 3D geometric invariants replaces the classical 2D affine transform estimation /verification step, and provides the ability to directly verify 3D geometric consistency. The accuracy and robustness of the proposed method in highly cluttered scenes with no prior segmentation or post 3D reconstruction requirements, are presented during the experiments.
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Wilhelm, Hedwig. "A Neural Network Model of Invariant Object Identification." Doctoral thesis, Universitätsbibliothek Leipzig, 2010. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-62050.

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Invariant object recognition is maybe the most basic and fundamental property of our visual system. It is the basis of many other cognitive tasks, like motor actions and social interactions. Hence, the theoretical understanding and modeling of invariant object recognition is one of the central problems in computational neuroscience. Indeed, object recognition consists of two different tasks: classification and identification. The focus of this thesis is on object identification under the basic geometrical transformations shift, scaling, and rotation. The visual system can perform shift, size, and rotation invariant object identification. This thesis consists of two parts. In the first part, we present and investigate the VisNet model proposed by Rolls. The generalization problems of VisNet triggered our development of a new neural network model for invariant object identification. Starting point for an improved generalization behavior is the search for an operation that extracts images features that are invariant under shifts, rotations, and scalings. Extracting invariant features guarantees that an object seen once in a specific pose can be identified in any pose. We present and investigate our model in the second part of this thesis.
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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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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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Books on the topic "Object invariants"

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

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

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

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Kao, Chang-Lung. Affine invariant matching of noisy objects. Monterey, Calif: Naval Postgraduate School, 1989.

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

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

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

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

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

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

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Book chapters on the topic "Object invariants"

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Leino, K. Rustan M., and Peter Müller. "Object Invariants in Dynamic Contexts." In ECOOP 2004 – Object-Oriented Programming, 491–515. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24851-4_22.

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Rothwell, Charles A. "Hierarchical object description using invariants." In Applications of Invariance in Computer Vision, 397–414. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58240-1_21.

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Jackson, Daniel. "Object models as heap invariants." In Monographs in Computer Science, 247–68. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21798-7_12.

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Muselet, Damien, and Brian Funt. "Color Invariants for Object Recognition." In Advanced Color Image Processing and Analysis, 327–76. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-6190-7_10.

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Balzer, Stephanie, and Thomas R. Gross. "Verifying Multi-object Invariants with Relationships." In Lecture Notes in Computer Science, 358–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22655-7_17.

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Nair, Sreeja S., Gustavo Petri, and Marc Shapiro. "Proving the Safety of Highly-Available Distributed Objects." In Programming Languages and Systems, 544–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44914-8_20.

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AbstractTo provide high availability in distributed systems, object replicas allow concurrent updates. Although replicas eventually converge, they may diverge temporarily, for instance when the network fails. This makes it difficult for the developer to reason about the object’s properties, and in particular, to prove invariants over its state. For the subclass of state-based distributed systems, we propose a proof methodology for establishing that a given object maintains a given invariant, taking into account any concurrency control. Our approach allows reasoning about individual operations separately. We demonstrate that our rules are sound, and we illustrate their use with some representative examples. We automate the rule using Boogie, an SMT-based tool.
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Gopinathan, Madhu, and Sriram K. Rajamani. "Runtime Monitoring of Object Invariants with Guarantee." In Runtime Verification, 158–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89247-2_10.

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Naumann, David A. "Assertion-Based Encapsulation, Object Invariants and Simulations." In Formal Methods for Components and Objects, 251–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11561163_11.

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Huizing, Kees, and Ruurd Kuiper. "Verification of Object Oriented Programs Using Class Invariants." In Fundamental Approaches to Software Engineering, 208–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46428-x_15.

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Lau, K. L., W. C. Siu, and N. F. Law. "Improved Scheme for Object Searching Using Moment Invariants." In Advances in Multimedia Information Processing — PCM 2002, 783–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36228-2_97.

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Conference papers on the topic "Object invariants"

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Leino, K. Rustan M., and Angela Wallenburg. "Class-local object invariants." In the 1st conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1342211.1342225.

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Kautsky, Jaroslav, Jan Flusser, and Filip Sroubek. "Implicit Invariants and Object Recognition." In 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dicta.2007.4426833.

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Fahndrich, Manuel, and Songtao Xia. "Establishing object invariants with delayed types." In the 22nd annual ACM SIGPLAN conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1297027.1297052.

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Summers, Alexander J., Sophia Drossopoulou, and Peter Müller. "The need for flexible object invariants." In International Workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1562154.1562160.

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Xiao, Bai, Richard Wilson, and Edwin Hancock. "Object recognition using graph spectral invariants." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761245.

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Gong, Weibo. "Invariants in Object Deformation and Concept Abstraction." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619352.

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Tham, Jie Sheng, Yong-Shen Chen, Mohammad Faizal Ahmad Fauzi, and Yoong Choon Chang. "Depth image object recognition using moment invariants." In 2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). IEEE, 2016. http://dx.doi.org/10.1109/icce-tw.2016.7520900.

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Rahtu, E., M. Salo, J. Heikkil, and J. Flusser. "Generalized affine moment invariants for object recogn." In 18th International Conference on Pattern Recognition (ICPR'06). IEEE, 2006. http://dx.doi.org/10.1109/icpr.2006.599.

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Jannson, Tomasz P. "Manifold geometric invariants and object-centric approach." In International Symposium on Optical Science and Technology, edited by Bruno Bosacchi, David B. Fogel, and James C. Bezdek. SPIE, 2002. http://dx.doi.org/10.1117/12.453564.

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Bayro-Corrochano, E., and C. Lopez-Franco. "Invariants and omnidirectional vision for robot object recognition." In 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2005. http://dx.doi.org/10.1109/iros.2005.1545165.

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Reports on the topic "Object invariants"

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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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Weiss, Isaac, and Manjit Ray. Recognizing Articulated Objects in Range Images Using Invariants. Fort Belvoir, VA: Defense Technical Information Center, February 2002. http://dx.doi.org/10.21236/ada408100.

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Keren, David, Ehud Rivlin, Han Shimshoni, and Isaac Weiss. Recognizing 3D Objects Using Tactile Sensing and Curve Invariants. Fort Belvoir, VA: Defense Technical Information Center, July 1997. http://dx.doi.org/10.21236/ada353693.

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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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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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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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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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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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