共被期刊论文引用了3次
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| 1 | Wood defect detection method with PCA feature fusion and compressed sensing显示文摘We used principal component analysis(PCA) and compressed sensing to detect wood defects from wood plate images. PCA makes it possible to reduce data redundancy and feature dimensions and compressed sensing, used as a classifier, improves identification accuracy. We extracted 25 features, including geometry and regional features, gray-scale texture features, and invariant moment features, from wood board images and then integrated them using PCA, and selected eight principal components to express defects. After the fusion process, we used the features to construct a data dictionary, and realized the classification of defects by computing the optimal solution of the data dictionary in l_1 norm using the least square method. We tested 50 Xylosma samples of live knots, dead knots, and cracks. The average detection time with PCA feature fusion and without were 0.2015 and 0.7125 ms,respectively. The original detection accuracy by SOM neural network was 87 %, but after compressed sensing, it was 92 %. | Yizhuo Zhang Chao Xu Chao Li Huiling Yu Jun Cao | 2015 | Journal of Forestry Research2015，26，3： | 17 |
| 2 | A novel image segmentation approach for wood plate surface defect classification through convex optimization显示文摘Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect images, we used convex optimization(CO) with different weights as a pretreatment method for smoothing and the Otsu segmentation method to obtain the target defect area images. Structural similarity(SSIM) results between original image and defect image were calculated to evaluate the performance of segmentation with different convex optimization weights. The geometric and intensity features of defects were extracted before constructing a classification and regression tree(CART) classifier. The average accuracy of the classifier is 94.1% with four types of defects on Xylosma congestum wood plate surface: pinhole, crack,live knot and dead knot. Experimental results showed that CO can save the edge of target defects maximally, SSIM can select the appropriate weight for CO, and the CART classifier appears to have the advantages of good adaptability and high classification accuracy. | Zhanyuan Chang Jun Cao Yizhuo Zhang | 2018 | Journal of Forestry Research2018，29，6： | 14 |
| 3 | Soft measurement of wood defects based on LDA feature fusion and compressed sensor images显示文摘We proposed a detection method for wood defects based on linear discriminant analysis(LDA) and the use of compressed sensor images. Wood surface images were captured, using a camera Oscar F810C IRF camera,and then the image segmentation was performed, and the defect features were extracted from wood board images. To reduce the processing time, LDA algorithm was used to integrate these features and reduce their dimensions. Features after fusion were used to construct a data dictionary and a compressed sensor was designed to recognize the wood defects types. Of the three major defect types, 50 images live knots, dead knots, and cracks were used to test the effects of this method. The average time for feature fusion and classification was 0.446 ms with the classification accuracy of 94%. | Chao Li Yizhuo Zhang Wenjun Tu Cao Jun Hao Liang Huiling Yu | 2017 | Journal of Forestry Research2017，28，6： | 6 |
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