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| 1 | 低秩稀疏分解与显著性度量的医学图像融合显示文摘提出一种低秩稀疏成分分解和显著性相结合的医学图像融合方法。所提方法假设待融合源图像由低秩成分和稀疏成分构成,设计了低秩与稀疏成分分解模型,通过不同的字典对不同成分进行了稀疏表达。在融合过程中采用一种"绝对值"取大的策略对低秩成分融合,以保留源图像的亮度信息;对于稀疏成分,提出一种基于视觉显著性度量的方法来保留显著性特征。实验结果表明,本文方法无论从主观视觉还是客观评价指标上都优于最新的方法。 | 邓志华 李华锋 | 2018 | 光学技术2018，44，4： | 6 |
| 2 | Efficient phase-induced gabor cube selection and weighted fusion for hyperspectral image classification显示文摘Spectral-spatial Gabor filtering(GF),a robust feature extraction tool,has been widely investigated for hyperspectral image(HSI)classification.Recently,a new type of GF method,named phase-induced GF,which showed great potential for HSI feature extraction,was proposed.Although this new type of GF possibly better explores the frequency characteristics of HSIs,with a new parameter added,it generates a much larger amount of features,yielding redundancies and noises,and is therefore risky to severely deteriorate the efficiency and accuracy of classification.To tackle this problem,we fully exploit phase-induced Gabor features efficiently,proposing an efficient phase-induced Gabor cube selection and weighted fusion(EPCS-WF)method for HSI classification.Specifically,to eliminate the redundancies and noises,we first select the most representative Gabor cubes using a newly designed energy-based phase-induced Gabor cube selection(EPCS)algorithm before feeding them into classifiers.Then,a weighted fusion(WF)strategy is adopted to integrate the mutual information residing in different feature cubes to generate the final predictions.Our experimental results obtained on four well-known HSI datasets demonstrate that the EPCS-WF method,while only adopting four selected Gabor cubes for classification,delivers better performance as compared with other Gabor-based methods.The code of this work is available at http://gffzz188fe103f8f1460askn5uu0vv666k60np.ffgz.tsg.suse.edu.cn/cairlin5/EPCS-WF-hyperspectral-image-classification for the sake of reproducibility. | CAI RunLin LIU ChenYing LI Jun | 2022 | Science China(Technological Sciences)2022，65，4： | 2 |
| 3 | A novel unsupervised method for new word extraction显示文摘New words could benefit many NLP tasks such as sentence chunking and sentiment analysis. However, automatic new word extraction is a challenging task because new words usually have no fixed language pattern, and even appear with the new meanings of existing words. To tackle these problems, this paper proposes a novel method to extract new words. It not only considers domain specificity, but also combines with multiple statistical language knowledge. First, we perform a filtering algorithm to obtain a candidate list of new words. Then, we employ the statistical language knowledge to extract the top ranked new words. Experimental results show that our proposed method is able to extract a large number of new words both in Chinese and English corpus, and notably outperforms the state-of-the-art methods. Moreover, we also demonstrate our method increases the accuracy of Chinese word segmentation by 10% on corpus containing new words. | Lili MEI Heyan HUANG Xiaochi WEI Xianling MAO | 2016 | Science China(Information Sciences)2016，59，9： | 0 |
| 4 | 基于改进弹性网格的古民居指纹分类算法显示文摘使用基于面积特征和形状特征的矩形算法可以从卫星图像中分离出民居目标,对于误识别疑似民居区域,需要进一步提取它们的纹理特征加以排除。并且需要设计旋转、放大和裁剪算子,对所有抽取目标进行大小和方向的标准化。弹性网格技术可以选取图像的多个特征行和特征列,而它们的相交把一个图像划分成多个特征子格。计算出每个子格的灰度共生矩阵(gray level co-occurrence matrix,GLCM)的几个经典特征值形成一个特征数组,可以反映子格的局部纹理特征。所有子格的特征数组顺序组合形成一个特征向量,可以反映这个图像的全局特征。基于改进弹性网格划分和子格GLCM特征值的指纹向量能够同时表征一个图像的局部纹理特征和全局统计特征。通过与不同年代的民居样本特征指纹的相似度比较,实现了古民居的精确识别与分类。实验表明,使用矩形算法抽取出民居目标的正确率为86.9%,使用基于弹性网格划分和GLCM特征值的民居指纹算法,古民居初步分类正确率超过97.4%。 | 杨帆 沈来信 | 2015 | 计算机科学与探索2015，9，10： | 0 |
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