Z
Zhen Kang
Researcher at Wuhan Polytechnic University
Publications - 6
Citations - 148
Zhen Kang is an academic researcher from Wuhan Polytechnic University. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 2, co-authored 2 publications receiving 110 citations.
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Image retrieval using spatiograms of colors quantized by Gaussian Mixture Models
TL;DR: A novel image representation method that characterizes an image as a spatiogram--a generalized histogram--of colors quantized by Gaussian Mixture Models (GMMs) is proposed, which employs Gaussian color components instead of discrete color bins.
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Image segmentation using spectral clustering of Gaussian mixture models
TL;DR: A novel image segmentation method that combines spectral clustering and Gaussian mixture models is presented in this paper and the experimental evaluation on the IRIS dataset and the real-world image segmentsation problem demonstrates the effectiveness of the proposed approach.
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A Method for Detection of Corn Kernel Mildew Based on Co-Clustering Algorithm with Hyperspectral Image Technology
TL;DR: The experimental results demonstrated that the proposed algorithm could describe the complex structure of mildew distribution in corn kernels and exhibits higher stability, better anti-interference ability, generalization ability, and accuracy than the supervised classification model.
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Self-supervised anomaly pattern detection for large scale industrial data
TL;DR: In this article , a composite semantic augmentation encoder (CSAE) is proposed to detect high-level semantic patterns in industrial data and implement quick detection of anomalies in industrial application environments.
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An Enhanced Spectral Fusion 3D CNN Model for Hyperspectral Image Classification
TL;DR: A new classification model called the enhanced spectral fusion network (ESFNet), which contains an optimized multi-scale fused spectral attention module (FsSE) and a 3D convolutional neural network (3D CNN) based on the fusion of different spectral strides (SSFCNN).