Research On Image Analyzing And Automatic Determining Techniques For Cotton And Bast Fiber

The feature extraction and determination of cotton and bast fiber is researched in this thesis. A series of automated determining technology in textile fibers is studied, which include microscopical sample preparing technology, image automatic photographing technology, the object contour describing technology, the image segmented technology, feature extraction technology and fiber identification technology.The determination of fiber contents is very important in textile industry, national and international trade. The chemical dissolving analysis is no use for these textiles containing two or more fiber types of similar chemical structure. The only way is microscopical analysis, and the determination of fiber content in cotton/blast fiber blends is typical in this identification type.Microscopical analysis is based on manual determining, which results in much time-consuming, high testing-cost. The identification result can be easily influenced by subjective emotion and eye strain for long time work. The automatic determination of fiber contents can shorten inspection period, heighten testing efficiency, lighten technical labor intension, eliminate artificial disturb, ensure results consistency and objectivity.The slicing technology of fiber directly influences the difficulty and correctness of automatic identification. The procedure of preparing the longitudinal slices is simply introduced. A uniform and fairly fiber suspension is obtained through quick boiling, the noticing part of which is to avoid the fiber overlapping and crossing. The improved methacrylate resin embedding is used in fiber cross-section slices. This method reduces the polymerized shrinkage, uneven polymerization and embedding time. The embedding method is fit for research and fast testing. The hollowness and circularity measuring from the slices of hollow polyester fiber and kapok proved this method has best form-keeping. The slice from resin embedding has little deformation, low touching and thin thickness. These will facilitate the automated image testing system. The automatic photograph ensures the automated image-digitalizing. This technology is made up of auto-focus technology, multi-focus image fusion and image stitching, the core of which is auto focusing algorithm. In the part of auto-focusing, several focusing algorithms are discussed: the sum of the absolute of value of gray difference(SMD), the gray scale variance, the Tengrad operator, the Lapal operator and the gray scale entropy. The unbiased ness, uniqueness, single meanings, anti-noise performance and simplicity of these algorithms is compared, and the SMD is selected for coarse focusing algorithm based on the focusing curve. The adaptive local sub-area algorithm is applied in the fiber longitudinal section image, and the gray variation around the contour is applied in the cross sectional focusing. The practical focusing procedure is analyzed, and the relativity between locations of focal planes of different visual fields is used to decide the pre-location of focusing procedure. The pre-location method which shortens the focus distance and the improved focusing tactic increase focusing speed.At the use of the info of multi-focus image from auto-focusing procedure and coarse focusing image, the images from the image series are segmented into background(no object), sharp area(the object focusing) and blur area(the object defocusing). The background of the fusion image is obtained from the average of backgrounds of series images, and the object area of the fusion image is obtained through the sharp area substituting blur area and combining with border processing.In microscopically image analysis, several images is stitched together to form an entirely image for the limitation of visual field. The algorithm is based on template matching principle. The stitched template and area is defined from the relationship between neighboring images. The two areas are processed by median filter and gradient operator in order to escape from the influence of noise and image gray level difference, which ensures the correctness of location and join.The character of object contour is the focus of fiber image recognizing. The feature extraction of 2-D image is converted into the analysis of 1-D signal by the use of chain code describing method for the contour, which facilitates the following feature analysis of shape. The border smoothing is used to decrease the influence of image noise. The concept of direction chain code is introduced, which is the sum of differential chain code cumulativeness to increase the ability of direction expression, anti-noise and anti-rectilinear-sampling. Depended on the direction chain code curve from artificial contour, the method of detecting corner is studied. Aiming at longitudinal image, the start point of border-tracing, the ends and cross part of fibers is decided on the directional chain code. The middle axis can be quickly decided on the character of longitudinal shape. The touching cross-section can be divided into two basic touching type, serial touching and shunt touching. The algorithm to judge touching cross-section and the different separating algorithm for different touching types is brought forward. The lumen and cross-section border of the typical fiber/ramie fiber is described by the direction chain code, and the character of these curves is concluded.If the contour is described by the chain code, this means that the 2-D image analysis can be converted into 1-D signal proceeding, so the signal analyzing technique can be used to extract features of fiber contour. The contour signal can be taken for the buildup of envelop signal and detail signal. The wavelet transform have multi-scale analysis on signal through dilation and translation transform, which is suitable for detecting the transient abnormal phenomena and its component. The principle of wavelet transform, multi-resolution analysis and 1 -D Mallat fast method is simply introduced. The reason for the surge of directional chain code curve is analyzed, and the curve is denoised by the multi-level wavelet transform. The envelop signal is obtained from the contour curve by the multi-scale analysis, and then the detail signal is gotten. This method is validated on the crack model, concavity model and corner model. The correlativity between lumen and cross-sectional border is calculated, and the coefficient is used to discriminated cotton/bast fibers.In the part of distinguishment of longitudinal modality, the distinguishing algorithms which have been studied are simply introduced. A fast algorithm of measuring fiber widths is put forward based on the fiber longitudinal character. The gray level variation is used to decide natural convolution and lumen of cotton. The gray projecting method is used to extract the cross markings and cracks, which converts 2-D feature extraction into the 1-D gray curve analysis. The difficulty of extraction of the cross markings and cracks on curly fiber is solved by the image transform, and a fast improved deciding method based on fiber middle axis is put forward. The flow of deciding fiber type on the longitudinal feature is built up.The neutral network is used to determine the fiber types to answer for the unsteadiness of natural fibers feature. The distributing of cross-sectional character parameter is investigated, and the elementary statistics rule is obtained. After the principle and trait of neutral network is introduced, the three-level BP neutral network is formed for fiber determining. The effect of feature in fiber determining is researched on steady testing method and cross testing method, from which the determine set of cross-sectional feature is screened out. Based on these work, the hide-layer nodes number and activating function is choose.This thesis focuses on the determination of cotton/blast fiber. The determining technologies are systemically researched. First the embedding resin is improved, and the embedding time is shortened. The little deforming and high separating half-thin microscopy slide is provided, which ensures the cross-sectional image quality and satisfies the practical testing task. The auto-focusing algorithm is decided by the character of microscopical image of textile fiber, which is the foundation of auto-microscope. The Freeman chain code is improved and the concept of direction chain code is introduced. The new code describes the contour of fiber cross-sectional more precisely. The feature extraction of 2-D image is converted into the analysis of 1-D signal, which facilitate the following feature analysis of shape. The detail feature is extracted from the multi-resolution analysis of wavelet transform, which indicated the character more truly. The gray level info is fully made use of to increase the analysis source and the determining correctness. The cross-sectional image is differentiated by the artificial neutral network, of which the self-study and self-organize is fit for the unsteadiness of the natural fiber cross-sectional feature. All above technology is not just fit for fiber content analyzing, but also can be used in fiber quality inspect, fiber forming technology, the quality control of spinning procedure and the testing of textile material structure, which has extensively applied fields.