基于鳞片纹图特征的羊绒识别及其含量确定

毛发的鳞片纹图具有特征性,是人们区别不同毛发的重要特征之一,也是目前对羊绒/羊毛混合体鉴别及其含量测量的唯一标准方法.由于识别与计数为人工目测,易存在主观误差,故采用自制的光学显微成像系统获得图像进行图像处理与分析,提取羊绒和羊毛的纤维直径、鳞片高度、周长、面积、鳞片相对面积、鳞片相对周长、径高比和鳞片方形度共8个特征参数,并对各特征参数的相关性及分布特征进行研究讨论.试验结果表明,相对独立的特征参数为纤维直径、鳞片高度和方形度;羊绒与羊毛各参数理论分布曲线间的交叠面积最小为鳞片相对面积,这成为特征参数选择和判识概率计算中权系数确定的依据.最后采用多参数贝叶斯分类模型实测3种试样中羊绒和羊毛纤维含量,结果显示与试样的名义混纺比基本一致,从而证明此方法具有实用、精准、快速的特点.

Feature-based sequential partial vision measurement method for large scale machine parts

To realize the high-precision vision measurement for large scale machine parts, a new vision measurement method based on dimension features of sequential partial images is proposed. Instead of mosaicking the partial images, extracting the dimension features of the sequential partial images and deriving the part size according to the relationships between the sequential images is a novel method to realize the high- precision and fast measurement of machine parts. To overcome the corresponding problems arising from the relative rotation between two sequential partial images, a rectifying method based on texture features is put forward to effectively improve the processing speed. Finally, a case study is provided to demonstrate the analysis procedure and the effectiveness of the proposed method. The experiments show that the relative error is less than 0. 012% using the sequential image measurement method to gauge large scale straight-edge parts. The measurement precision meets the needs of precise measurement for sheet metal parts.

X-ray Comparative Analysis of Rhubarb in Different Production Areas of Qinghai

[Objective] The aim of this study was to provide a basis for distinguishing quality of rhubarb in different production areas. [Method ] X-ray diffraction patterns of rhubarbs in different production areas of Qinghai were obtained by X-ray diffraction analysis, and then its similarity analysis was also investigated. [ Result] The content of chemical components in rhubarbs from different production areas had differences, but its diffraction patterns and diffraction peaks had certain fingerprint characteristics. [ Conclusion] X-ray diffraction method is a fast and effective method for identifying rhubarb and other Chinese herbal medicines in different production areas.

Case study on the extraction of land cover information from the SAR image of a coal mining area

In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.

Chemical fingerprinting of Su-He-Xiang-Wan and attribution of major characteristic peaks for its quality control by GC-MS

A simple and facile gas chromatography-mass spectrometer （GC-MS） fingerprint of Su-He-Xiang-Wan （SHXW） was developed, the similarity analysis was conducted, and attribution of the major characteristic peaks was identified for SHXW quality control. GC-MS analysis was performed on a QP2010 instrument （Shimadzu, Japan） equipped with a capillary column of RTX-5MS. The column temperature was initiated at 50℃, held for 5 min, increased at the rate of 3 ℃/min to 120 ℃, held for 2 min, and then increased at the rate of 4 ℃/min to 220℃, held for 10 min. Helium carrier gas was used at a constant flow rate of 1.3 mL/min. Mass conditions were ionization voltage, 70 eV; injector temperature, 250℃; ion source temperature, 250 ℃; splitting ratio, 30：1; full scan mode in the 40-500 Da mass ranges with rate of 0.2 s per scan. Attribution of the major characteristic peaks was identified for SHXW by comparing the chemical standards, references of Chinese herbal medicines and the negative controls of prescription samples （NC） of SHXW. With the help of the temperature-programmed retention indices （PTRIs） used together with mass spectra and chemical standards, 25 major characteristic peaks have been identified. Nine volatile medicinal materials were identified in the prescription of SHXW by attributing to the 27 major characteristic peaks. The results demonstrate that the proposed method is a powerful approach to quality control of complex herbal medicines.

An Improved Medical Image Fusion Algorithm for Anatomical and Functional Medical Images

In recent years,many medical image fusion methods had been exploited to derive useful information from multimodality medical image data,but,not an appropriate fusion algorithm for anatomical and functional medical images.In this paper,the traditional method of wavelet fusion is improved and a new fusion algorithm of anatomical and functional medical images,in which high-frequency and low-frequency coefficients are studied respectively.When choosing high-frequency coefficients,the global gradient of each sub-image is calculated to realize adaptive fusion,so that the fused image can reserve the functional information;while choosing the low coefficients is based on the analysis of the neighborbood region energy,so that the fused image can reserve the anatomical image's edge and texture feature.Experimental results and the quality evaluation parameters show that the improved fusion algorithm can enhance the edge and texture feature and retain the function information and anatomical information effectively.

SAR Image Classification Based on Its Texture Features

SAR images not only have the characteristics of all-ay, all-eather, but also provide object information which is different from visible and infrared sensors. However, SAR images have some faults, such as more speckles and fewer bands. The authors conducted the experiments of texture statistics analysis on SAR image features in order to improve the accuracy of SAR image interpretation. It is found that the texture analysis is an effective method for improving the accuracy of the SAR image interpretation.

A Method of Soil Salinization Information Extraction with SVM Classification Based on ICA and Texture Features

Salt-affected soils classification using remotely sensed images is one of the most common applications in remote sensing,and many algorithms have been developed and applied for this purpose in the literature.This study takes the Delta Oasis of Weigan and Kuqa Rivers as a study area and discusses the prediction of soil salinization from ETM +Landsat data.It reports the Support Vector Machine(SVM) classification method based on Independent Component Analysis(ICA) and Texture features.Meanwhile,the letter introduces the fundamental theory of SVM algorithm and ICA,and then incorporates ICA and texture features.The classification result is compared with ICA-SVM classification,single data source SVM classification,maximum likelihood classification(MLC) and neural network classification qualitatively and quantitatively.The result shows that this method can effectively solve the problem of low accuracy and fracture classification result in single data source classification.It has high spread ability toward higher array input.The overall accuracy is 98.64%,which increases by10.2% compared with maximum likelihood classification,even increases by 12.94% compared with neural net classification,and thus acquires good effectiveness.Therefore,the classification method based on SVM and incorporating the ICA and texture features can be adapted to RS image classification and monitoring of soil salinization.

Three-Dimensional Reconstruction of Live Fish through the Moire Technique

Currently, in fish farms, the controls of the physical characteristics of the fishes, for example, size and mass are made by means of the operation of fish removal, in which the tanks are emptied to capture the animals and perform the biometry. This operation demands large volumes of water and generates effluent containing high concentrations of organic matter and nutrients that can contribute to the deterioration of water quality in the recipient bodies. Therefore, the development of technologies that use digital image processing, such as the moird technique and image analysis, can be important allies for the preservation of environmental quality by avoiding the fish removal and the discharge of effluents, increasing productivity due to optimization of the time and still the saving of water. To obtain the images a 9-liter glass aquarium, a support for notebook and light projector, a digital camera brand Samsung Galaxy Camera 2 were used. The objective of this work was to obtain the three-dimensional reconstruction of live fish in aquariums. In the future, the technique can be developed to obtain the mass and the volume of the fish in fish tanks, replacing the fish removal, allowing the preservation of water resources.

Texture Classification of 3D Surface Textures Via Directional Quincunx Lifting

This thesis presents a new approach to classify 3D surface textures by using lifting transform with quincunx subsampling. Feature vectors are generated from eight different lifting prediction directions. We classify 3D surface texture images based on minimum Euclidean distance between the test images and the training sets. The feasibility and effectiveness of our proposed approach can be validated by the experimental results.

Quality evaluation of Polyporus umbellatus based on HPLC specific chromatogram and fingerprint analysis

To evaluate the quality of Polyporus umbellatus,we established a simple,repeatable and reliable method based on high performance liquid chromatography(HPLC)for specific chromatogram and fingerprint analysis,which was applied to analyze samples of medicinal materials and decoction pieces collected from different regions.Finally,ten characteristic peaks were designated in the specific chromatograms and applied to the authenticate identification of P.umbellatus samples.Nine common peaks were designated in the fingerprints,and then the similarities between 32 batches of samples were calculated.Among them,eight compounds were identified by HPLC-APCI-IT-TOF-MS^(n),four of which were identified in specific chromatograms and four in fingerprints.In the present study,we,for the first time,combined HPLC specific chromatograms and fingerprints for the species identification and quality evaluation of P.umbellatus.Collectively,our findings provided a new method for establishing a comprehensive quality standard of P.umbellatus.

Comparative study on the performance of textural image features for active contour segmentation

We present a computerized method for the semi-automatic detection of contours in ultrasound images. The novelty of our study is the introduction of a fast and efficient image function relating to parametric active contour models. This new function is a combination of the gray-level information and first-order statistical features, called standard deviation parameters. In a comprehensive study, the developed algorithm and the efficiency of segmentation were first tested for synthetic images. Tests were also performed on breast and liver ultrasound images. The proposed method was compared with the watershed approach to show its efficiency. The performance of the segmentation was estimated using the area error rate. Using the standard devia- tion textural feature and a 5x5 kernel, our curve evolution was able to produce results close to the minimal area error rate （namely 8.88% for breast images and 10.82% for liver images）. The image resolution was evaluated using the con- trast-to-gradient method. The experiments showed promising segmentation results.