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Invariants for Non-Hierarchical Object Structures

Ronald Middelkoop, Cornelis Huizing, Ruurd Kuiper, Erik J. Luit
2008 Electronical Notes in Theoretical Computer Science  
It allows invariants over non-hierarchical object structures, in which update patterns that span several objects and methods occur frequently.  ...  We argue that the additional flexibility offered by inc is essential in the use of invariants over non-hierarchical object structures.  ...  preserved by methods of C when the structure is hierarchical.  ... 
doi:10.1016/j.entcs.2007.08.034 fatcat:pi44nfbxubgbzen7aaoxhl6xca

A hierarchical classifier using new support vector machines for automatic target recognition

David Casasent, Yu-Chiang Wang
2005 Neural Networks  
A binary hierarchical classifier is proposed for automatic target recognition.  ...  We also require rejection of non-object (non-target) inputs, which are not seen during training or validation, thus producing a very difficult problem.  ...  We also sincerely thank Clare Walters and Anita Burrell from NVESD for providing us the TRIM-2 database.  ... 
doi:10.1016/j.neunet.2005.06.033 pmid:16087318 fatcat:o6mercsj3jgp7apzau4zu56ife

Hierarchical invariant sparse modeling for image analysis

Leah Bar, Guillermo Sapiro
2011 2011 18th IEEE International Conference on Image Processing  
Promising results are obtained for three applications -2D shapes classification, texture recognition and object detection.  ...  The invariant sparse representation of patterns here presented-can be used in different object recognition tasks.  ...  The superscript 1 designates the bottom level of the hierarchical structure.  ... 
doi:10.1109/icip.2011.6116125 dblp:conf/icip/BarS11 fatcat:sf7abzro5nccdk6wcwofv6i3d4

Disambiguating the recognition of 3D objects

G. Guerra-Filho
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
A hierarchical structure is constructed to organize the objects in terms of shared primitives and relations between different primitives in the same object.  ...  This algorithm uses geometric invariants to compute relations for subsets of points in a specific object. Each relation is stored in a hash table according to the invariant value.  ...  In this paper, we proposed a novel Invariant Hough Transform for the recognition of 3D objects. We use a hierarchical structure that is augmented with intra-relations.  ... 
doi:10.1109/cvprw.2009.5206683 fatcat:kqtimlaxnrhtnbfnmwbkagcnuq

Disambiguating the recognition of 3D objects

Gutemberg Guerra-Filho
2009 2009 IEEE Conference on Computer Vision and Pattern Recognition  
A hierarchical structure is constructed to organize the objects in terms of shared primitives and relations between different primitives in the same object.  ...  This algorithm uses geometric invariants to compute relations for subsets of points in a specific object. Each relation is stored in a hash table according to the invariant value.  ...  In this paper, we proposed a novel Invariant Hough Transform for the recognition of 3D objects. We use a hierarchical structure that is augmented with intra-relations.  ... 
doi:10.1109/cvpr.2009.5206683 dblp:conf/cvpr/Guerra-Filho09 fatcat:xjn7dgwezjfv5bdg74lihsmdny

Bag of Hierarchical Co-occurrence Features for Image Classification

Takumi Kobayashi, Nobuyuki Otsu
2010 2010 20th International Conference on Pattern Recognition  
We propose a bag-of-hierarchical-co-occurrencefeatures method incorporating hierarchical structures for image classification.  ...  For extracting descriptors, we employ two types of features hierarchically: narrow (local) descriptors, like SIFT [1], and broad descriptors based on co-occurrence features.  ...  These descriptors are useful for extracting co-occurrences of various kinds of components which form hierarchical structures in the target objects. II.  ... 
doi:10.1109/icpr.2010.945 dblp:conf/icpr/KobayashiO10a fatcat:h2ex5xv5gzgjlmowp6ckhs3yfu

ClusterNet: Deep Hierarchical Cluster Network With Rigorously Rotation-Invariant Representation for Point Cloud Analysis

Chao Chen, Guanbin Li, Ruijia Xu, Tianshui Chen, Meng Wang, Liang Lin
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
We employ hierarchical clustering to explore and exploit the geometric structure of point cloud, which is embedded in a hierarchical structure tree.  ...  Current neural networks for 3D object recognition are vulnerable to 3D rotation.  ...  For example, in 3D object classification task, the category label of an object is invariant against arbitrary rotation transformation in majority situations.  ... 
doi:10.1109/cvpr.2019.00513 dblp:conf/cvpr/ChenLXC0L19 fatcat:zeh7r4zcmzfabkzetpinolleyi

Why The Brain Separates Face Recognition From Object Recognition

Joel Z. Leibo, Jim Mutch, Tomaso A. Poggio
2011 Neural Information Processing Systems  
to downstream viewpoint-invariant identity-specific cells [1] .  ...  when applied to other object classes.  ...  Sections 3 and 4 describe an extension to an existing hierarchical model of object recognition to include invariances for class-specific transformations.  ... 
dblp:conf/nips/LeiboMP11 fatcat:op6l3paz6jhwxpxxnmfw2an2ly

Depiction Inviariant Object Matching

Anupriya Balikai, Peter Hall
2012 Procedings of the British Machine Vision Conference 2012  
We use structure at a global level, which is combined with simple non-photometric descriptors at a local level. There is no need for any prior learning.  ...  Here, we propose a more general approach for describing objects invariant to depictive style.  ...  The importance of structure has been underlined as a basis for generic representation of objects [1, 9, 10, 21] , and has previously been used for matching and detection of objects, albeit not invariant  ... 
doi:10.5244/c.26.56 dblp:conf/bmvc/BalikaiH12 fatcat:g5dw72uidrdabatemglxf4gtrm

Modular Specification of Encapsulated Object-Oriented Components [chapter]

Arnd Poetzsch-Heffter, Jan Schäfer
2006 Lecture Notes in Computer Science  
Boxes: Hierarchical Structured Object Systems box boundary box environment owner boundary object 2.  ...  Specification Technique: Overview Structure of specifications: • Specification and checking techniques for encapsulation • Specification techniques for boxes: -state -invariants -method behavior  ...  (2) • Invariants may only depend on the fields of the box. • Invariants have to hold whenever the thread is outside the box. • This helps to solve the modularity problem.  ... 
doi:10.1007/11804192_15 fatcat:74hko2jxincn7n6zm5nn2j47m4

Parameterized deformation sparse coding via tree-structured parameter search

Brandon Burdge, Kenneth Kreutz-Delgado, Joseph Murray
2010 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers  
deformation function that captures invariant structure.  ...  We consider a method by which prior knowledge about the structure of such invariances can be exploited using a novel algorithm for sparse coding across a learned dictionary of atoms combined with a parameterized  ...  Algorithm 1 1 Tree-Structured Parameter Search for PDSC Require: k = 1, r 0 = y,ŷ 0 = 0, I 0 = [∅], A 0 = [∅], θ 0 = [∅], S 0 = [∅] Tree Structure: for layers l = 1, ..., L with B l branches, Φ l = {φ  ... 
doi:10.1109/acssc.2010.5757904 fatcat:xlojbm3fxng73fzjnxztgisfga

Attention can improve a simple model for object recognition

E. Bermudez-Contreras, H. Buxton, E. Spier
2008 Image and Vision Computing  
A model that lately has gained attention is the HMAX model, which describes a feedforward hierarchical structure. This model shows a degree of scale and translation invariance.  ...  Our work explores and compares the HMAX model with a simpler model for object recognition emulating simple cells in the primary visual cortex, V1.  ...  Could it be that a simple non-hierarchical model for object recognition could show some degree of translation and scale invariance when used with the same attentional mechanism?  ... 
doi:10.1016/j.imavis.2007.08.014 fatcat:nqphp2q5wbg5nm4m2kariuaqga

Bayesian Inference in Model-Based Machine Vision [article]

Thomas O. Binford, Tod S. Levitt, Wallace B. Mann
2013 arXiv   pre-print
We pursue a thorough integration of hierarchical Bayesian inference with comprehensive physical representation of objects and their relations in a system for reasoning with geometry, surface materials  ...  Bayesian inference provides a framework for accruing_ probabilities to rank order hypotheses.  ...  If surfaces are non-coincident or non-smooth, these are quasi-invariant observables that are important for segmentation.  ... 
arXiv:1304.2720v1 fatcat:rue7tk3fcncbdojhq4wrslec7u

Mathieu beams as versatile light moulds for 3D micro particle assemblies

C. Alpmann, R. Bowman, M. Woerdemann, M. Padgett, C. Denz
2010 Optics Express  
The powerful method is demonstrated for the class of propagation invariant beams, where we introduce the use of Mathieu beams as light moulds with non-rotationally-symmetric structure.  ...  We present tailoring of three dimensional light fields which act as light moulds for elaborate particle micro structures of variable shapes.  ...  Among propagation invariant beams, the class of Mathieu beams are especially well-suited due to their variety of transverse, non-diffracting structure that allows a variety of 3D applications for moulding  ... 
doi:10.1364/oe.18.026084 pmid:21164957 fatcat:ymtruhhoobfjplrdu3bb73pkee

Simultaneous micromanipulation in multiple planes using a self-reconstructing light beam

V. Garcés-Chávez, D. McGloin, H. Melville, W. Sibbett, K. Dholakia
2002 Nature  
The powerful method is demonstrated for the class of propagation invariant beams, where we introduce the use of Mathieu beams as light moulds with non-rotationally-symmetric structure.  ...  We present tailoring of three dimensional light fields which act as light moulds for elaborate particle micro structures of variable shapes.  ...  Among propagation invariant beams, the class of Mathieu beams are especially well-suited due to their variety of transverse, non-diffracting structure that allows a variety of 3D applications for moulding  ... 
doi:10.1038/nature01007 pmid:12226659 fatcat:77tmrv7aifgzrpx6mh4djphbhu
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