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Nano Fiber Images Thresholding based on Imperial Competitive Algorithm
Abstract and Figures
Nanofibers are widely used in various industries, therefor knowing the morphology is important. Thresholding is a simple but effective technique for image segmentation. The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. In this paper a novel method is proposed for performing image segmentation. The purpose of this paper proposed an imperial competitive algorithm with the objective function from Kmeans clustering algorithm for Nano fibers image thresholding. Then algorithm used, with the algorithms such as: global threshold, local threshold, Kmeans clustering algorithm and FCM methods were compared. Finally, a powerful algorithm for image thresholding is found. The comparisons and experimental results show that purposed algorithm is better than other methods particularly global and local thresholding, Kmeans and even FCM.
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In the magnetic-suspension bearing-rotor system, the shape features of the rotor axis trajectory can directly reflect the system operation status and fault information. Accurately classifying the bearing faults can promptly find the cause of the fault and respond quickly and protect it. In this paper, in order to achieve fault identification by identifying and classifying the rotor axis trajectory, we use Hu moment invariants to extract feature vectors from the rotor axis trajectory to identify faults. We use imperial competitive algorithm to reduce redundant data and establish a directed acyclic graph support vector machine to classify and identify feature data. Compared with traditional classification models such as C4.5 and BP neural networks, the experimental results show that the support vector machine model based on Hu moment invariants and simulated annealing has a higher accuracy and robustness in small sample classification. It is suitable for intelligent identification and classification for bearing faults.
Orientation of cell and its actin fibres dictate various
properties of the cell and its associated tissue including
morphology, cell migration and response of cell to
external stimuli. While the vital role of cell and actin
fibre orientation has been demonstrated in various
studies and across many cell-types, extracting this
information from microscopy images is a challenging
task. Although several methods have been developed
to quantify alignment of a cell and its actin fibres
from experimental images, all of them differ at one
step or the other. We highlight the importance of orientation
of cell and associated fibres and present a generic
scheme of various approaches to assimilate the
widely used methods. We have presented a systematic
approach to determine cell and fibre orientation. The
study will improve our understanding of the core
processes and will also help in development of high
throughput imaging methods to extract information
from experimental images.
The image segmentation is an effort to classify similar colors of image in the same group. It clusters colors into several groups based on the closeness of color intensities inside an image. In preprocessing to use Enhancement there are two segmentation techniques to apply for Image clustering & Artificial Neural Network .Algorithms based on cluster methods are normally used to obtain data, which are based on the features space, where these groups are represented by clusters. In existing local threshold and fuzzy set measure is used in that we can only classify the images. Now we apply Neural Network segmentation relies on processing small areas of an image using an artificial neural network or a set of neural networks. After such processing the decision-making mechanism marks the areas of an image accordingly to the category recognized by the neural network.
Fuzzy c-means clustering (FCM) with spatial constraints is an effective algorithm suitable for image segmentation. Considering the spatial information of pixels and based on spatially weighted FCM, we propose a new method which improve the accuracy color image segmentation results. Color image features (RGB) are converted into (Hue, Inyensity, saturation) space, and use improved FCM clustering algorithm to get membership function of I (intensity) component of pixels. Thus a new vector if descriptors is fcreated. component of pixels. A clustering algorithm is used for final decision. The results of experiments show a good performances.
Fiber diameter is an important structural characteristic for electrospinning, due to its direct influence on the properties of the produced webs. In this paper, an image analysis-based method, called direct tracking, for measuring the electrospun fiber diameter, has been developed. The results obtained by direct tracking significantly excelled distance transformation, indicating that the method could be used for measuring electrospun fiber diameter. INTRODUCTION In recent years, nanotechnology has become a topic of great interest to scientists and engineers, and is now established as a prioritized research area in many countries. Size reduction to the nanometer range creates new possibilities in terms of material properties, in particular with respect to the achievable surface, to volume ratios. Electrospinning of nanofibers is a novel process for producing superfine fibers by forcing a solution through a spinnerette with an electric field. An emerging technology for manufacturing thin natural fibers is based on the electrospinning principle. In conventional fiber spinning, the mechanical force is applied to the end of a jet, whereas, in the electrospinning process, the electric body force acts on the element of the charged fluid. Electrospinning has emerged as a specialized processing technique for the formation of submicron fibers (typically between 100 nm and 1 µm in diameter), with high specific surface areas. Due to their high specific surface area, high porosity and small pore size, the unique fibers have been suggested for wide range of applications. Electrospinning of nanofibers offers unique capabilities for producing novel natural nanofibers and fabrics with controllable pore structure. About 4-9% of the cotton fiber is lost at textile mill, during the so-called opening and cleaning, which involves mechanically separa-ted compressed clumps of fibers for the removal of trapped debris. Another 1% is lost during drawing and roving-pulling lengths of the fiber into longer and longer segments, which are then twisted together for strength. An average of 20% is lost during combing and yarn production. Typically, waste cotton is used in relatively low-value products, such as cotton balls, yarn and cotton batting. A new process for electrospinning waste cotton using a less harmful solvent has been developed. Electrospinning is an economical and simple method used in the preparation of polymer fibers. Fibers prepared via this method typically have much smaller diameters than what is possible to attain by standard mechanical fiber-spinning technologies. 1 Electrospinning has gained much attention in the last few years as a cheap and straightforward method to produce nanofibers. Electrospinning differs from the traditional wet/dry fiber spinning in several ways, of which the most striking differences refer to the origin of the pulling force and to the final fiber diameters. The mechanical pulling forces in the traditional industrial fiber spinning processes lead to fibers in the micrometer range, being contrasted in electrospinning by the electrical pulling forces that permit the production of nanofibers. Depending on the solution properties, the throughput of single-jet electrospinning systems is around 10 mL/min.
Criterion based thresholding algorithms are simple and effective for two-level thresholding. However, if a multilevel thresholding is needed, the computational complexity will exponentially increase and the performance may become unreliable. In this approach, a novel and more effective method is used for multilevel thresholding by taking hierarchical cluster organization into account. Developing a dendogram of gray levels in the histogram of an image, based on the similarity measure which involves the inter-class variance of the clusters to be merged and the intra-class variance of the new merged cluster . The bottom-up generation of clusters employing a dendogram by the proposed method yields good separation of the clusters and obtains a robust estimate of the threshold. Such cluster organization will yield a clear separation between object and background even for the case of nearly unimodal or multimodal histogram. Since the hierarchical clustering method performs an iterative merging operation, it is extended to multilevel thresholding problem by eliminating grouping of clusters when the pixel values are obtained from the expected numbers of clusters. This paper gives a comparison on Otsu’s & Kwon’s criterion with hierarchical based multi-level thresholding.
Objective methods for assessing perceptual image quality have traditionally attempted to quantify the visibility of errors between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MatLab implementation of the proposed algorithm is available online at http://www.cns.nyu.edu/~lcv/ssim/.
This study aims to segment objects using the K-means algorithm for texture features. Firstly, the algorithm transforms color images into gray images. This paper describes a novel technique for the extraction of texture features in an image. Then, in a group of similar features, objects and backgrounds are differentiated by using the K-means algorithm. Finally, this paper proposes a new object segmentation algorithm using the morphological technique. The experiments described include the segmentation of single and multiple objects featured in this paper. The region of an object can be accurately segmented out. The results can help to perform image retrieval and analyze features of an object, as are shown in this paper.
Statistical measures have been developed for quality control as well as characterization of mass distribution in fibrous substrates. The mass distribution in 2D space has been measured by imaging the substrate. The intensity distribution of reflected/transmitted light has been used as a representative of the mass distribution. Spatial mass uniformity has been measured in terms of a standardized index of dispersion and mass anisotropy ratio that are reasonably independent of fiber mass density. The dispersion index is standardized between the limits of −1 and +1, with −1 representing uniform distribution and +1 representing aggregated distribution. Furthermore, the standardized values have been combined with the qualitative grades to categorize and differentiate uniform patterns. Standardized index of dispersion and mass anisotropy ratio have been evaluated for a wide range of non-woven webs and adhesive lay down patterns.