Research On Tiny Crease Detection Of Feather Quill

Feather as the main raw material of badminton production, its quality directly affects the quality of the finished product badminton.Creases of feather quill occur when the cuticle is damaged and the fibres of the cortex unravel.However, the quality testing of badminton industry relies a lot on manual work in our country, which results in that the detecting speed and quality are influenced by personal. Actually high strength testing is harmful to eyesight of the workers. Consequently, it’s of great engineering significances and potential economic effect to study crease defect automatic detection by feature extraction using machine vision for testing image acquisition.Due to slender structure of feather quill with variable width, camber and curvature, in addition, boundary between crease of feather quill and background is fuzzy, all of these factors cause disturbance to the crease feature recognition.Based on detailed research development from home and abroad, this thesis explores experimental device,image segmentation,denoising,graylevel correction and crease features classification, etc.,deeply.There are some helpful researches about badminton industry from labour-intensive industry to technology-intensive industry.The related work and harvest in this paper as follows:(1) Image edge processing. Due to slender structure of feather quill with variable width, camber and curvature, this makes marginal noise caused disturbance to the crease feature recognition to increase the potential for misunderstanding and miscalculation. So need to eliminate the residual edge villi of feather quill, edge to reduce noise.The method partitions a feather image into clusters with normalized cut method to obtain an initial contour of the feather quill. The proposed narrow unidirectional expansion method with a region-scalable fitting term is used to adjust the initial boundary for the final result. This paper presents a method of image segmentation by combining certain hard constraints for segment by indicating certain seeds with unidirectional expansion. The method utilizes left and right vertex of feather quill as seeds which are passed by active contour to better over segmentation; changes bidirection dilation to inside direction dilation to improve the overlap of adjacent contour neighborhoods and reduce the computation scale. Experiment results show that the proposed method without human intervention can effectively realize the image segmentation effectively.(2) Statistical analysis of feather quill crease shows that most of feather quill is no crease or some feather quill only have one or two crease. Exhaustive searches of two-dimensional image can increase the potential for misunderstanding and miscalculation. As most of feather quill without creases in actual production,detection method requires not only effective feature extraction, but also a high degree of non-crease detection accuracy. Aiming at this characteristic, a detection method of feather quill crease is proposed. The feather quill image is transformed into one dimensional signal, using the relationship between wavelet transform modulus maxima and singular point,the crease coordinate can be prejudged. Subimage can be extracted through the coordinate to reduce misjudgement caused by image traversal.(3)Light correction.Side lighting exacerbated the uneven illumination phenomenon, combined with different camber and camber feather quill, so it is difficult to use correction calibration template to correction light. Due to the unique structure of feather quill, the attenuation of light can be seen as a slow process. The concept of "baseline drift" is introduced,and multi-scale decompositions are carried out for mean gray level curve. Using wavelet approximation coefficients to close characteristic of baseline drift completes light correction.(4) Image denoising.Tiny creases of feather quill is easily disturbed by noise that causing adverse effects on recognition performance. For the shortcomings of the traditional noise reduction algorithm in the process of image, i.e., it is sensitive to the noise resulting in the problem of weakened creases characteristics, this paper discusses probability measure of noisy image on manifolds based on heat equation theory, analyzes regularity and uniqueness of heat kernels on manifolds,and gets the corresponding relationship between density function of noisy data and probability density of clean data. A denoising algorithm of feather quill based on the heat equation of graph is proposed. In the method, de-noised image can be obtained through iterative solution of equation based on feather quill image representing as an undirected weighted graph. The Experimental results show that this suggested method can get better effect comparing with other transform domain algorithms. (5) Appropriate shape description method. According to the surface defect characteristics such as shape, size and gray distribution,a novel method based on improved Radon transform is proposed. In order to solve the scaling and translation sensitivity of Radon transformation, an improved Radon transformation is used to extract moment invariants of target region and introduces local projection technology to eliminate interference physiological texture of feather quill. Obtaining invariants matrix by changing the scale factor, singular value decomposition is provided here to obtain feature invariant for classification and recognition. Finally,the final recognition result of the system is achieved by the fusion of identification results of the gray domain and gradient domain at the decision level to overcome the limitations of single-modal and reduce the misjudgment of non-crease effectively.(6) Feather quill crease detection method based on Riemannian manifold.Research is mainly focused on the covariance matrix lie group structure with Log-Euclidean metric with double invariance properties and manifold kernel function expression with covariance matrices as the crease descriptors of feather quill;then this article designs algorithm with metrics of inner product space and manifold kernel function expression which are deduced. An affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to adjust class variance and within class variance. Finally, in order to implement the discrimination in nonlinear space, the best projection space of samples is gotten using the Riemannian index mapping. A feather quill crease recognition method based on locality preserving projection and manifold kernel function is proposed for feature extraction. Firstly, covariance matrices are computed as the crease descriptors of feather quill, and an affine invariance metric which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples. Secondly, the neighbors of a selected point can be determined by the proposed manifold kernel function to make choice of the nearest neighboring points in line with the hypothesis of data distribution with non-linear manifold. The kernel matrix is defined based on the manifold distance and category labels. Finally, the locality preserving projections algorithm is used to reduce the dimensionality of the feather quill images.(7) A feather quill crease recognition method based on sparse representation on Riemannian manifold is proposed for feature recognition. Firstly, Bregman divergence which is adopted to make the space meet the requirement of Riemannian manifold is used to measure the distance between the two samples, and the kernel function on Riemannian manifold is constructed. Secondly, the sample datasets are mapped into the reproducing kernel Hilbert space by kernel method, and kernel sparse representation coefficient is obtained. Finally, a mathematical model of dictionary learning is constructed and an efficient algorithm is proposed for dictionary learning according to the convex theory. The simulated experimental results verify the effectiveness of the proposed method which achieves better performance compared with many popular recognition algorithms.At the end of this dissertation, the main research is summarized. It makes out the main innovations and research achievements, and also points out the problems and issues which need to further research.