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| 1 | Apple leaf disease identification using genetic algorithm and correlation based feature selection method显示文摘Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective. | Zhang Chuanlei Zhang Shanwen Yang Jucheng Shi Yancui Chen Jia | 2017 | International Journal of Agricultural and Biological Engineering2017，10，2： | 7 |
| 2 | A two‐branch network with pyramid‐based local and spatial attention global feature learning for vehicle re‐identification显示文摘In recent years,vehicle re‐identification has attracted more and more attention.How to learn the discriminative information from multi‐view vehicle images becomes one of the challenging problems in vehicle re‐identification field.For example,when the viewpoint of the image changes,the features extracted from one image may be lost in another image.A two‐branch network with pyramid‐based local and spatial attention global feature learning(PSA)is proposed for vehicle re‐identification to solve this issue.Specifically,one branch learns local features at different scales by building pyramid from coarse to fine and the other branch learns attentive global features by using spatial attention module.Subsequently,pooling operation by using global maximum pooling(GMP)for local features and global average pooling(GAP)for global feature is performed.Finally,local feature vectors and global feature vector extracted from the last pooling layer,respectively,are employed for identity re‐identification.The experimental results demonstrate that the proposed method achieves state‐of‐the‐art results on the VeRi‐776 dataset and VehicleID dataset. | Jucheng Yang Di Xing Zhiqiang Hu Tong Yao | 2021 | CAAI Transactions on Intelligence Technology2021，6，1： | 2 |
| 3 | Text Categorization Algorithms Using Semantic Approaches, Corpus-based Thesaurus and WordNet显示文摘 | Li Chenghua Yang Jucheng Park S C | 2012 | Expert Systems with Applications2012，39，1： | 1 |
| 4 | Fingerprint matching based on extreme learning machine显示文摘 | Jucheng Yang Shanjuan Xie Sook Yoon Dongsun Park Zhijun Fang Shouyuan Yang | 2013 | Neural Computing and Applications2013，，3： | 1 |
| 5 | TGF-13-de- pendent SMAD2 phosphorylation and inhibition of MEE prolif- eration during palatal fusion 显示文摘 | Xiao-Mei Cui Yang Chai Jucheng Chen | 2003 | Developmental Dyn2003，227，3： | 1 |
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