International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1168
Статья научная
Crankshaft is the most important and hard manufacture part in engine and diesel, its deformation contains multiple arcs, and its straightening technology is a complex NP-hard determination problem. The crankshaft straightening process was analyzed, and some effects from multiple arcs crankshaft straightening was investigated. The mathematics model of multiple arcs straightening technology determination system was built based on graph theory principle. And the Influence of Bauschinger effect was considered, the online straightening technology determination calculation algorithm based on weighted evaluation function was proposed. The experiments about straightening crankshaft were performed in YH40-160 straightening press to evaluate two different weighted set C1={2.5,1.5,1,1.2,3.5} and C2={1,1,1,1,1}, and the results expressed that the online straightening technology determination calculation algorithm was advantage to crankshaft straightening process, and C1 had better influence to qualities and efficiencies than C2.
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Crop Type Classification Based on Clonal Selection Algorithm for High Resolution Satellite Image
Статья научная
This paper presents a hierarchical clustering algorithm for crop type classification problem using multi-spectral satellite image. In unsupervised techniques, the automatic generation of clusters and its centers is not exploited to their full potential. Hence, a hierarchical clustering algorithm is proposed which uses splitting and merging techniques. Initially, the splitting method is used to search for the best possible number of clusters and its centers using non-parametric technique i.e., clonal selection method. Using these clusters, a merging method is used to group the data points based on a parametric method (K-means algorithm). The performance of the proposed hierarchical clustering algorithm is compared with two unsupervised algorithms (K-means and Self-Organizing Map) that are available in the literature. A performance comparison of the proposed algorithm with the conventional algorithms is presented. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is more accurate.
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Статья научная
In this paper we propose a method for automatic detection of crowd escape behaviour. Motion features are extracted by optical flow using Lucas-Kanade derivative of Gaussian method (LKDoG) followed by robust probabilistic weighted feature pooling operation. Probabilistic feature polling chooses the most descriptive features in the sub-block and summarizes the joint representation of the selected features by Probabilistic Weighted Optical Flow Magnitude Histogram (PWOFMH) and Probabilistic Weighted Optical Flow Direction Histogram (PWOFDH). One class Extreme Learning Machine (OC-ELM) is used to train and test our proposed algorithm. The accuracy of our proposed method is evaluated on UMN, PETS 2009 and AVANUE datasets and correlations with the best in class techniques approves the upsides of our proposed method.
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Curvelet transform for efficient static texture classification and image fusion
Статья научная
Wavelet Transform (WT) has widely been used in signal processing. WT breaks a signal into its wavelets that are scaled and shifted versions of given signal. Thus wavelets are able represent graphical objects. The irregular shape and compact support of wavelets made them ideal for analyzing non-stationary signals. They are useful in analysis in both temporal and frequency domains. In contract, the Fourier transform provides information in frequency domain lacking in information in time domain. Thus wavelets became popular for signal processing and image processing applications. Nevertheless, wavelets suffer from a drawback as they cannot effectively represent images at different angles and different scales. To overcome this problem, of late, Curvelet Transform (CT) came into existence. CT is nothing but the higher dimensional generalization of WT which can effectively represent images at different angles and different scales. In this paper we proposed a CT method that is used to represent textures and classify them. The methodology used in this paper has an underlying approach that exploits statistical features of curvelets that resulted in curvelet decomposition. We built a prototype application using MATLAB to demonstrate proof of the concept.
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