191 research outputs found
3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
We present a neural-network-based architecture for 3D point cloud denoising
called neural projection denoising (NPD). In our previous work, we proposed a
two-stage denoising algorithm, which first estimates reference planes and
follows by projecting noisy points to estimated reference planes. Since the
estimated reference planes are inevitably noisy, multi-projection is applied to
stabilize the denoising performance. NPD algorithm uses a neural network to
estimate reference planes for points in noisy point clouds. With more accurate
estimations of reference planes, we are able to achieve better denoising
performances with only one-time projection. To the best of our knowledge, NPD
is the first work to denoise 3D point clouds with deep learning techniques. To
conduct the experiments, we sample 40000 point clouds from the 3D data in
ShapeNet to train a network and sample 350 point clouds from the 3D data in
ModelNet10 to test. Experimental results show that our algorithm can estimate
normal vectors of points in noisy point clouds. Comparing to five competitive
methods, the proposed algorithm achieves better denoising performance and
produces much smaller variances
Algebraic Signal Processing Theory: Cooley-Tukey Type Algorithms for Polynomial Transforms Based on Induction
A polynomial transform is the multiplication of an input vector x\in\C^n by
a matrix \PT_{b,\alpha}\in\C^{n\times n}, whose -th element is
defined as for polynomials p_\ell(x)\in\C[x] from a list
and sample points \alpha_k\in\C from a list
. Such transforms find applications in
the areas of signal processing, data compression, and function interpolation.
Important examples include the discrete Fourier and cosine transforms. In this
paper we introduce a novel technique to derive fast algorithms for polynomial
transforms. The technique uses the relationship between polynomial transforms
and the representation theory of polynomial algebras. Specifically, we derive
algorithms by decomposing the regular modules of these algebras as a stepwise
induction. As an application, we derive novel general-radix
algorithms for the discrete Fourier transform and the discrete cosine transform
of type 4.Comment: 19 pages. Submitted to SIAM Journal on Matrix Analysis and
Application
Robust hyperspectral image classification with rejection fields
In this paper we present a novel method for robust hyperspectral image
classification using context and rejection. Hyperspectral image classification
is generally an ill-posed image problem where pixels may belong to unknown
classes, and obtaining representative and complete training sets is costly.
Furthermore, the need for high classification accuracies is frequently greater
than the need to classify the entire image.
We approach this problem with a robust classification method that combines
classification with context with classification with rejection. A rejection
field that will guide the rejection is derived from the classification with
contextual information obtained by using the SegSALSA algorithm. We validate
our method in real hyperspectral data and show that the performance gains
obtained from the rejection fields are equivalent to an increase the dimension
of the training sets.Comment: This paper was submitted to IEEE WHISPERS 2015: 7th Workshop on
Hyperspectral Image and Signal Processing: Evolution on Remote Sensing. 5
pages, 1 figure, 2 table
Towards a Culturally Grounded Typology of Magical Realism
Among the recent publications which seek to offer yet another re-definition of magical realism, Christopher Warnes' study Magical Realism and the Postcolonial Novel: Between Faith and Irreverence accomplishes a double feat: while bringing into dialogue the development of both the term 'magical realism' and the mode itself, Warnes develops two paradigms representing two major structural and functional tendencies in magical realism. One is based on an attitude of faith towards the cultural values represented by the supernatural in the texts, the other is characterized by an irreverent stance towards both the real and the supernatural. Applied to some of the key works of magical realism by Borges, Carpentier, Asturias, García Márquez, Rushdie, and Okri, the two paradigms produce acute readings of the cultural and political functions of the deployment of the mode in each individual work. The result is not a redefinition of magical realism, but a thought provoking re-contextualization which offers a typology capable of spanning the varieties of the mode
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