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Illumination Invariant Three-Stage Approach for Face Alignment
2006 International Conference on Image Processing, 2006Localization of facial components is very important for many pattern recognition and computer vision applications such as face recognition, tracking of face expressions. This paper addresses the problem of precisely finding facial components, such as the eyes, the mouth, the nose etc.
Fatih Kahraman, Muhittin Gokmen
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Shape Correspondence by Aligning Scale-invariant LBO Eigenfunctions
2020Eurographics Workshop on 3D Object ...
Bracha, Amit, Halim, Oshri, Kimmel, Ron
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Fast Shape Retrieval Based on Invariant Feature Alignment
2005 5th International Conference on Information Communications & Signal Processing, 2006This paper proposes an invariant feature alignment for shape-based image indexing and retrieval. We have found that some recently published systems have employed complex shape matchers to achieve high accuracy, at the cost of relatively high pairwise matching times.
null Han ShuiHua, null Yangshuangyuan
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An invariant alignability factor and its significance
Radio and Electronic Engineer, 1968The sensitivities of the port immittance to small immittance and large reactive changes of the self-immittance at the opposite port of a linear active two-port network are investigated. A new 'invariant alignability factor', S, is defined as the inverse of the maximum modulus of the sensitivity. 6 is an invariant for interchange of the ports, arbitrary
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Illumination Invariant and Component Based Approach for Face Alignment
2006 IEEE 14th Signal Processing and Communications Applications, 2006Localization of facial components is very important for many pattern recognition and computer vision applications such as face recognition, tracking of face expressions. This paper addresses the problem of precisely finding facial components, such as the eyes, the mouth, the nose etc.
F. Kahraman, M. Gokmen
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Discriminative and domain invariant subspace alignment for visual tasks
Iran Journal of Computer Science, 2019Transfer learning and domain adaptation are promising solutions to solve the problem that the training set (source domain) and the test set (target domain) follow different distributions. In this paper, we investigate the unsupervised domain adaptation in which the target samples are unlabeled whereas the source domain is fully labeled.
Samaneh Rezaei, Jafar Tahmoresnezhad
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Discriminative Invariant Alignment for Unsupervised Domain Adaptation
IEEE Transactions on Multimedia, 2022Yuwu Lu +5 more
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Occlusion-Invariant Representation Alignment for Entity Re-Identification
2022 IEEE International Conference on Image Processing (ICIP), 2022Zhanghao Jiang +5 more
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Recognition by combinations of model views: Alignment and invariance
1994A scheme for recognition of 3D objects from single 2D images is introduced. An object is modeled in this scheme by a small set of its views with the correspondence between the views. Novel views of the object are obtained by linearly combining the model views.
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