Search Results for author: Raymond Chan

Found 7 papers, 1 papers with code

Deep Tensor CCA for Multi-view Learning

1 code implementation25 May 2020 Hok Shing Wong, Li Wang, Raymond Chan, Tieyong Zeng

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order.

MULTI-VIEW LEARNING Tensor Decomposition

Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points

no code implementations4 Dec 2019 Zitong Wang, Li Wang, Raymond Chan, Tieyong Zeng

A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties.

Graph structure learning

Dynamic Spectral Residual Superpixels

no code implementations10 Oct 2019 Jianchao Zhang, Angelica I. Aviles-Rivero, Daniel Heydecker, Xiaosheng Zhuang, Raymond Chan, Carola-Bibiane Schönlieb

We consider the problem of segmenting an image into superpixels in the context of $k$-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects.

Superpixels

Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery

no code implementations27 Sep 2018 Chao Wang, Robert Plemmons, Sudhakar Prasad, Raymond Chan, Mila Nikolova

An optical imager that exploits off-center image rotation to encode both the lateral and depth coordinates of point sources in a single snapshot can perform 3D localization and tracking of space debris.

Linkage between piecewise constant Mumford-Shah model and ROF model and its virtue in image segmentation

no code implementations26 Jul 2018 Xiaohao Cai, Raymond Chan, Carola-Bibiane Schonlieb, Gabriele Steidl, Tieyong Zeng

The piecewise constant Mumford-Shah (PCMS) model and the Rudin-Osher-Fatemi (ROF) model are two important variational models in image segmentation and image restoration, respectively.

Image Restoration Semantic Segmentation

A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)

no code implementations30 May 2015 Xiaohao Cai, Raymond Chan, Mila Nikolova, Tieyong Zeng

In this paper, we propose a SLaT (Smoothing, Lifting and Thresholding) method with three stages for multiphase segmentation of color images corrupted by different degradations: noise, information loss, and blur.

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