Dr. Wei Zhou Profile

Dr. Wei Zhou

at Shenyang Aerospace Univ

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Publications (2)

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Proceedings Article | 16 February 2022 Paper

Robust adaptive graph learning with manifold constraints for subspace clustering

Wei Zhou, Yugen Yi, Yuhan Peng

Proceedings Volume 12083, 1208304 (2022) https://doi.org/10.1117/12.2623254

KEYWORDS: Scientific research

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Subspace clustering aims at grouping data into a number of partitions, which has become one of the most powerful tools to analyze and interpret big data, particularly for clustering high-dimensional data. Although a series of related approaches have been proposed, real data are often corrupted leading to the learned graph is inexact or unreliable. Meanwhile, constructing an informative graph is one of the most important steps, which can greatly affect the final performance of clustering approaches. To learn a robust graph, this paper presents a novel data representation approach called Robust Adaptive Graph Learning with Manifold Constraints (RAGLMC) for subspace clustering. RAGLMC introduces the l2,1-norm constraint on sparse coding coefficients for ensuring the learned coding coefficients would be optimal as adaptive graph weights. Moreover, in order to reduce the influence of noise points on the local structure in graph construction, we combine l2,1-norm with manifold constraints on the coding coefficients to learn a locality and smoothness representation. Therefore, the proposed approach can estimate the graph from data alone by using manifold constraint on coding coefficients, which is independent of a prior affinity matrix. Extensive experiments verify the effectiveness of the proposed approach over other state-of-the-art approaches.

Proceedings Article | 3 January 2020 Paper

Automatic optic disc location via adaptive multi-scale Gaussian filtering

Wei Zhou, Yang Pei, Jining Bao, Jie Luo, Yugen Yi

Proceedings Volume 11373, 113731W (2020) https://doi.org/10.1117/12.2559282

KEYWORDS: Databases, Adaptive optics, Optical filters, Gaussian filters, Blood vessels, Digital filtering, RGB color model, Image filtering, Image enhancement, Aerospace engineering

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Optic disc is one of the main features in retinal images. Its location plays an important role for automated screening systems. A novel approach for automatic optic disc location is proposed in this paper. It consists of the following three stages, in the first stage, preprocessing is applied for correcting the uneven illumination and improving the low contrast. In the second stage, a series of key points can be extracted using alternative sequential filters and regional maxima. In the third stage, the key point that has the maximum correlation coefficient calculated by adaptive multi-scale template matching method is located as the center of the optic disc. The proposed algorithm is tested on two publicly available databases including DRIVE and DIRATEDB1. And our experiment results indicate that the proposed algorithm gives better performance than the state-of-the-art approaches.

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