Efficient Spherical Parametrization Using Progressive Optimization

Wan, Shenghua; Ye, Tengfei; Li, Maoqing; Zhang, Hongchao; Li, Xin

doi:10.1007/978-3-642-34263-9_22

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…Moreover, the loss function of Adaboost will be modified to consider the classification costs. Moreover, we will also investigate the application of the proposed algorithm to information security [41,43,53,42,41,52,18], bioinformatics [40,24,57], medial imaging [56,55,54], computer vision [38,23,22,36,37,19,17,46,44,45,15], reinforcement learning [27,28], cloud computing [50,51] and microprocessor reliability modeling [8,6,7,5,59,61,60].…”

Section: Discussionmentioning

confidence: 99%

Online classifier adaptation for cost-sensitive learning

Zhang

García

2015

Neural Comput & Applic

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In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given a input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them one classification performances, but also requires significantly less running time.

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Section: Discussionmentioning

confidence: 99%

Online classifier adaptation for cost-sensitive learning

Zhang

García

2015

Neural Comput & Applic

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“…Local angle and area distortions were minimized during vertex splitting and projection. Similarly, Wan et al [13] developed a hierarchical optimization scheme to minimize both local and global distortion energy.…”

Section: Progressive Methodsmentioning

confidence: 99%

“…Kernel points are generated for each vertex in its spherical kernel, and the most favorable kernel point is chosen as the new position and used for computing disp (see lines 6-10). The displacement is propagated to neiDisps of neighbor vertices (see lines [11][12][13]. During the optimizing process, we maintain a maximum displacement (maxDisp) of all vertices.…”

Section: Optimizationmentioning

confidence: 99%

Fast mapping and morphing for genus-zero meshes with cross spherical parameterization

Peng

Timalsena

2016

Computers & Graphics

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a b s t r a c tWe introduce an efficient approach to establish surface correspondences between genus-zero triangle meshes and animate a morph between them. We employ the concept of mesh simplification and refinement to progressively parameterize meshes using a novel kernel sampling algorithm. Our two-step feature alignment method optimizes the distribution of vertices in a parametric domain. A new mesh is generated after merging mesh topologies, and it represents the shapes of all input meshes and their transitions in-between. Our approach merges topological structures rather than mapping surface properties. Complex meshes with tens of thousands of vertices can be parameterized and merged in a few minutes. The advantages of our approach are demonstrated through 3D morphing applications. Comparing to existing approaches, our solution advances in performance while maintaining high-quality parameterization and morphing.Published by Elsevier Ltd.1

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“…Wan et al proposed an efficient computational method for spherical harmonic function using progressive optimization in [36].…”

Section: Spherical Harmonic Functionmentioning

confidence: 99%

Deterministic Greedy Routing with Guaranteed Delivery in 3D Wireless Sensor Networks

Xia

Yin

et al. 2014

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With both computational complexity and storage space bounded by a small constant, greedy routing is recognized as an appealing approach to support scalable routing in wireless sensor networks. However, significant challenges have been encountered in extending greedy routing from 2D to 3D space. In this research, we develop decentralized solutions to achieve greedy routing in 3D sensor networks. Our proposed approach is based on a unit tetrahedron cell (UTC) mesh structure. We propose a distributed algorithm to realize volumetric harmonic mapping (VHM) of the UTC mesh under spherical boundary condition. It is a one-to-one map that yields virtual coordinates for each node in the network without or with one internal hole. Since a boundary has been mapped to a sphere, node-based greedy routing is always successful thereon. At the same time, we exploit the UTC mesh to develop a face-based greedy routing algorithm and prove its success at internal nodes. To deliver a data packet to its destination, face-based and node-based greedy routing algorithms are employed alternately at internal and boundary UTCs, respectively. For networks with multiple internal holes, a segmentation and tunnel-based routing strategy is proposed on top of VHM to support global end-to-end routing. As far as we know, this is the first work that realizes truly deterministic routing with constant-bounded storage and computation in general 3D wireless sensor networks.

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Efficient Spherical Parametrization Using Progressive Optimization

Cited by 13 publications

References 6 publications

Online classifier adaptation for cost-sensitive learning

Online classifier adaptation for cost-sensitive learning

Fast mapping and morphing for genus-zero meshes with cross spherical parameterization

Deterministic Greedy Routing with Guaranteed Delivery in 3D Wireless Sensor Networks

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