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In order to conquer the difficulty of simultaneously recognizing the multiple defects of potatoes samples placed randomly, this paper proposed a non-destructive detection method which combined manifold learning dimension reduction algorithm based on hyperspectral information fusion and extreme learning machine (ELM) to simultaneously distinguish the multiple defects of potatoes. In this paper, 367 potatoes were picked which were made up of 111 sprouting potatoes, 90 green rind potatoes, 46 blackheart potatoes and 120 normal potatoes. The hyperspectral image acquisition system contained imaging spectroradiometer (SPECIM, V10E, Finland), data acquisition box, lighting system, electric moving stage and objective table. The reflection hyperspectral information of all those potatoes was acquired by using the hyperspectral image acquisition system, whose spectral wavelength ranged from 390 to 1040 nm. The hyperspectral information included the spectral information from 520 wave bands and the imaging information from 520 gray images. After the correction to hyperspectral data with the standard black and white board, hyperspectral data in the range of 450-990 nm, which had high signal-to-noise ratio (SNR), were selected as original spectrum for subsequent processing. To deal with the spectral information, the average spectrum was abstracted from the region of interests (ROI) on every potato sample by using the environment for visualizing images (ENVI). Comparing several data preprocessing methods, detrend was determined as the optimal spectral preprocessing method. Diffusion maps (DM), locally linear embedding (LLE) and hessian locally linear embedding (HLLE) were respectively utilized for the purpose of cutting down the dimension of spectrum data after the spectral preprocessing named detrend. To deal with the hyperspectral imaging information, every pseudo-color image of potatoes was morphologically processed before extracting 84 image texture characteristics based on gray level co-occurrence matrix (GLCM). By using successive projections algorithm (SPA), 10 texture features were properly selected, which consisted of homogeneity of 45° GLCM in R gray image, energy of 90° GLCM in R gray image, correlation of 90° GLCM in R gray image, contrast of 135° GLCM in R gray image, correlation of 0° GLCM in G gray image, contrast of 0° GLCM in G gray image, correlation of 90° GLCM in G gray image, homogeneity of 45° GLCM in B gray image, correlation of 90° GLCM in B gray image and contrast of 135° GLCM in B gray image. After the fusion of the spectral characteristics and image features, the 367 potato samples were divided into calibration set and test set. The calibration set had 250 potato samples including 74 sprouting potatoes, 59 green rind potatoes, 28 blackheart potatoes and 89 normal potatoes. The whole number of samples in test set was 117 which contained 37 sprouting potatoes, 31 green rind potatoes, 18 blackheart potatoes and 31 normal potatoes. Six models were respectively established based on support vector machine (SVM) and ELM, which were DM-SVM, LLE-SVM, HLLE-SVM, DM-ELM, LLE-ELM and HLLE-ELM. Comparing and analyzing the results of the 6 models and the spending time, we discovered that the DM was the best manifold learning dimension reduction algorithm to deduce the spectral information. We alsofound that the DM-ELM model was the best model because of the better recognition rate for recognizing the multiple defects of potatoes and spending less time. For the DM-ELM model, the single recognition rate of sprouting potatoes, green rind potatoes, blackheart potatoes and normal potatoes respectively reached 97.30%, 93.55%, 94.44% and 100%, and the mixed recognition rate reached 96.58%. It only cost 0.11 s to build the DM-ELM model. The results indicate that combining hyperspectral information fusion with manifold learning dimension reduction algorithm can simultaneously distinguish the multiple defects of potatoes. [ABSTRACT FROM AUTHOR]

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In the machine vision-based intelligent system for recognizing female or male silkworm pupa, much spatially-varying blur appears in silkworm pupa images and it could give rise to the loss of images textures and structures to a great extent due to the irregular ellipsoid shape of silkworm pupa and the limited depth of field of optical imaging system. This brings a challenge for an intelligent system to identify silkworm pupa’s gender. Shen’s method is supposed to be one of the state-of-the-art methods, but the PSF(point spread function) is estimated on a per pixel basis and parameter enumeration is required to meet the optimization criterion, which leads to a prohibitively large computation efforts. To solve this problem, we presented an effective method that the complicated restoration of spatially-varying blur silkworm pupa images was decomposed into the simple restoration of multiple images which have the same level blur and were part of original image. In this work, according to the variation of Tang’s method, the blur standard deviation at every pixel was estimated to construct a full defocus map of silkworm pupa image. The approximate blur standard deviations in defocus map were automatically sorted via the fuzzy C-means algorithm, and the original blur silkworm pupa image, based on this classification, was naturally segmented into several sub images possessing similar level blur. Then, each sub image was deblurred by using Lucy-Richardson (LR), and was merged to form a full silkworm pupa image. Eventually, Bilateral Filtering was used to eliminate the errors arising in the merging stage, and the high-quality silkworm pupa image was then obtained. To test this method, experiments (including both female and male silkworm pupa images) were conducted on the platform configured with CPU i5-2430 M, 2.4 GHz, memory 2 G, 32 bit operation system, matlab 2012 and VC++6.0. We set iteration steps of LR de-convolution as eight in real data experiments. Total variation Mean (TVM) was used to estimate the quality of the restored results. The experimental results showed that the performance of the proposed algorithm was better than Shen’s method. The method successfully removed spatially-varying blur and enhanced the image quality, which was verified in both qualitative (or visual) and quantitative ways. It can be seen that in real data experiments, our method effectively improved silkworm pupa images from which the spatially-varying blur was eliminated to a great extent, more image texture details were increased and sharpness contrast was much better. Meanwhile, in term of quantitation estimation, the TVM values of our method’ results were bigger than Shen’s results, which was further proof of our method’s good performance. It was noted that our method can also be conveniently extended to improve the quality of other vegetable images suffering from the spatially-varying blur, such as marrow and pumpkin, as shown in the experiments. After silkworm pupa image restoration, we achieved high accuracy of 92.3% in identifying male and female silkworm pupa. The proposed method can have a wide application of machine vision technologies. [ABSTRACT FROM AUTHOR]

Recently, it's still difficult to entirely replace the artificial visual interpretation for the computer automatic classification, which is used to extract land cover types' information from the remote sensing imagery, because the automatic method needs more efforts to improve the precision of the classification results. Furthermore, this problem has become the key joint of the automatic classification extraction. How to extract land cover types' information in western area of Jilin, is one of the important problems, and the confused land cover types needs to be distinguished. The aim of this study is to deepen the application of remote sensing classification method that is used to extract land cover information automatically and quickly from the satellite imagery. The western area of Jilin is selected as the main research area. A new total solution to extract land cover information, based on the spatial variation theory, has been designed for the convenient automatic classification with the remote sensing technology. The remote sensing classification scheme is carried out by coding the R language algorithm and operating the remote sensing software ERDAS platform. The land cover types in Zhenlai County in the western area of Jilin, have been extracted and monitored through the combined utilization of 4 indices, including semivariance value of normalized difference vegetation index (NDVI) dataset, local variance of image texture, modified soil-adjusted vegetation index and normalized difference water index, which have significant meaning for the land cover types in the transition zone between cropping area and nomadic area. These variances have definite physical meaning (including vegetation, water, and soil drought conditions), so that the phenological information was used to build a multi-dimensional feature space classification data set. The results indicated that: 1) A total of 11 land cover types are extracted, using the multi-temporal remote sensing information to build a multidimensional classification characteristics data set based on the Landsat 8 data. The overall classification accuracy of the algorithm is 95.50%; the Kappa coefficient of classification is 0.9504. The automatic extracting approach implemented obtains a comparatively ideal classification result; 2) The introduction of 3 characteristic variables of the classification in the scheme significantly improves the separability of the confused land cover types. Considering the vegetation classification, the vegetation growth information has practical life-activity significance, and is a real-time dynamic method for the vegetation change monitoring; 3) Improving the land cover classification accuracy is not to introduce more characteristic parameters of the classification, but to effectively combine multiple appropriate classification variables. The new method can broaden the application vision and the scope of the ecological remote sensing investigation of surface vegetation. Moreover, the introduction of new variables not only makes the macro monitoring more convenient, but also improves the accuracy of classification of remote sensing interpretation. It's noted that the extracted classification has obvious regional feature, and the regional feature is consistent with the farming cultivation characteristics on the Northeast Plain. In a word, the results can provide a credible approach and valuable example for extracting and monitoring land cover type in farming-pastoral transitional zone. It is feasible to use the spatial variation theory to extract and monitor land cover type by combining the several evaluation indices. [ABSTRACT FROM AUTHOR]