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An Observation-Constrained Generative Approach for Probabilistic Classification of Image Regions

机译：一种观测约束的图像区域概率分类生成方法

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Sanjiv Kumar; Alexander C. Loui; Martial Hebert;
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年度 2003
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1. An observation-constrained generative approach for probabilistic classification of image regions [J] . Sanjiv Kumar, Alexander C. Loui, Martial Hebert Image and Vision Computing . 2003,第1期

机译：图像区域概率分类的观察约束生成方法
2. A Data-Efficient Approach for Automated Classification of OCT Images Using Generative Adversarial Network [J] . Vineeta Das, Samarendra Dandapat, Prabin Kumar Bora IEEE Sensors Letters . 2020,第1期

机译：一种使用生成对抗网络自动分类OCT图像的数据有效方法
3. Automatic classification of medical X-ray images: hybrid generative-discriminative approach [J] . Zare M.R., Mueen A., Awedh M., Image Processing, IET . 2013,第5期

机译：医学X射线图像的自动分类：混合生成-判别方法
4. Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification [C] . Zilong Zhong, Jonathan Li AAAI Conference on Artificial Intelligence;Innovative Applications of Artificial Intelligence Conference;Symposium on Educational Advances in Artificial Intelligence . 2018

机译：用于高光谱图像分类的生成对抗网络和概率图模型
5. Generative Models as a Robust Alternative for Image Classification: Progress and Challenge [D] . Ju, An. 2021

机译：生成模型作为图像分类的强大替代方案：进度和挑战
6. Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks [O] . Atsushi Teramoto, Tetsuya Tsukamoto, Ayumi Yamada, 2020

机译：深层学习方法肺细胞学图像分类：使用实际和合成图像的两步训练通过逐步生长的生长生长育种网络
7. Adaptive Weighting Feature Fusion Approach Based on Generative Adversarial Network for Hyperspectral Image Classification [O] . Hongbo Liang, Wenxing Bao, Xiangfei Shen 2021

机译：基于生成对抗网络的自适应加权特征融合方法进行高光谱图像分类
8. Best Merge Region Growing with Integrated Probabilistic Classification for Hyperspectral Imagery [R] . Tarabalka, Y., Tilton, J. C. 2011

机译：利用高光谱图像的综合概率分类增长最佳合并区域

An Observation-Constrained Generative Approach for Probabilistic Classification of Image Regions

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