Abstract: Many defects now appear in ancient murals due to both natural and man-made factors. To better repair the damaged ancient murals, this paper improves the existing intelligent restoration method. It identifies and marks the crack-producing area, categorizes the mural image into two types – texture area and flat area according to the local gradient features, decides the initial sample block and calculates the weight, analyzes the extracted pixel data and applies discrete differential algorithm to supplement image defects. Through experiments, the method is validated in meeting the needs of image continuity law and human vision. It can shorten the repair…time and restore mural cracks in an intelligent way.
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Keywords: Discrete differential algorithm, ancient mural, crack, intelligent restoration
Abstract: The local data of ancient murals is seriously damaged, and image noise exists in the process of restoration, which affects the quality of restoration of ancient murals. Therefore, this paper studies the restoration method of ancient mural image defect information based on neighborhood filtering. On the premise of obtaining the causes of ancient mural defects, this method enhances image data based on spatial domain enhancement method, extracts pixel similar information based on neighborhood filtering, searches in the whole image, and removes image noise used to repair local areas; By extracting the line drawing features of mural, the defect part of…ancient mural image can be repaired. The experimental results show that the peak signal-to-noise ratio of the repaired image is the highest and the quality of the image is better under the application of the repair method.
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Keywords: Neighborhood filtering, ancient fresco, image defect, information restoration
Abstract: In order to solve the problems of the traditional methods in detecting color image edge chromatic aberration, such as the poor accuracy of detection and the poor detection effect, a color image edge chromatic aberration detection method based on artificial intelligence technology is proposed. The approximate principal component analysis method is used to segment the color image and smooth the image denoising; The linear gray-scale transformation is applied to the color image to enlarge the smaller gray-scale space to the larger gray-scale space according to the linear relationship and obtain the edge information of the color image; The artificial intelligence…technology is used to locate the edge sub-pixel of the image to complete the edge color difference detection of the color image. The experimental results show that the detection accuracy of the proposed method is about 98%, and the detection effect is good, which is feasible.
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Keywords: Artificial intelligence technology, color image, edge color difference, approximate principal component, approximate principal component, location
Abstract: Due to the influence of recognition parameters, image recognition has low recognition accuracy, long recognition time and large storage cost. Therefore, an automatic image recognition method based on Boltzmann machine is proposed. Based on threshold method and fuzzy set method, image malformation correction is performed. The mean filter and median filter are combined to eliminate the influence of image filtering, and the pre-processing of image is completed by using the fuzzy enhancement of image. Based on the restricted Boltzmann method, the network model is dynamically evolved, and the identification parameters of each shape and contour are obtained. Different shapes and…contours are classified and recognized. Simulation results show that image recognition method based on human-computer interaction has high recognition ability, shortens the time cost and greatly reduces the space needed for node storage.
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Keywords: Human computer interaction, cross culture, language image, automatic identification
Abstract: Traditional mural repair methods only observe the texture of murals when segmenting the repair area, but ignore the extraction of a mural damage data, resulting in incomplete damage crack information. For this reason, the method of repairing the damaged murals based on machine vision is studied. Using machine vision, it can get two-dimensional image of a mural, preprocess the image, extract the damaged data of a mural, and then divide the repair area and repair degree index. According to different types of damage, it can choose the corresponding repair methods to achieve the repair of damaged mural. The results show:…Compared with the OPTICS-based unsupervised method and the machine vision for orchard navigation method, the number of repair points and repair cracks extracted by the proposed method is more than that of the two traditional methods, which can more accurately and comprehensively extract the repair information of murals.
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Abstract: During the traditional cultural heritage virtual interaction algorithm in the interaction action recognition, the database is too single, resulting in low recognition accuracy, recognition time-consumer and other issues. Therefore, this paper introduces the multi feature fusion method to optimize the cultural heritage virtual interaction algorithm. Kinect bone tracking technology is applied to identify the movement of the tracking object, 20 joints of the human body are tracked, and interactive action recognition is realized according to the fingertip candidate points. In order to carry out the judgment virtual interactive operation of subsequent recognition actions, a multi feature fusion database is established.…The mean shift is used to derive the moving mean of the target’s action position and to track the interactive object. The Euclidean distance formula is used to train samples of multi feature fusion database data to realize the judgment of recognition action and virtual interaction. In order to verify the feasibility of the research algorithm, the virtual interactive script of ink painting in a cultural heritage museum is used to simulate the research algorithm, and a comparative experiment is designed. The experimental results show that the proposed algorithm is superior to the traditional virtual interactive algorithm in recognition accuracy and efficiency, which proves the feasibility of this method.
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Keywords: Virtual interaction; action recognition; candidate points; multi feature fusion; kinect bone tracking; mean shift
Abstract: As the fat-tail property is a well-known observational result in financial return distributions, the Student's t-distribution became popular in financial return distribution modeling. We introduce an extension of the t-distribution model in this paper, called polynomial-t-distribution model, in which we use the product of the t-distribution density and a polynomial to fit the density function of financial returns. This extended model allows arbitrary moment parameters of the return distribution, in addition of the fat-tail adjustment. The formulae of the European option prices, VaR and CVaR are given. Numerical illustrations are made based on S&P 500 index returns.
Keywords: t-distribution, orthogonal polynomial, fat-tail distribution, S&P 500, Romanovski polynomial, options pricing, VaR, CVaR, maximum likelihood
Abstract: The traditional distributed WSNs fire remote monitoring system has single monitoring variables and incomplete fire detection data, which leads to large monitoring error and long delay. A distributed WSNs fire remote monitoring system based on fuzzy algorithm is designed. The hardware part of the system consists of distributed WSNs fire remote monitor, air temperature and humidity parameters acquisition, LCD unit and system power supply unit. The remote fire monitor is designed by using microprocessor C8051F060, and the centralized monitoring of information is realized by using distributed WSNs. Based on this, the fuzzy algorithm is introduced to standardize the fire detection…data, and the fuzzy similar matrix is established. According to the improved similarity coefficient, the matrix is solved, the fuzzy equivalent matrix is calculated, and the optimal threshold value of fuzzy monitoring is determined. The fuzzy language monitoring rules are set by using three fuzzy variables of current, temperature and smoke to complete the design of distributed WSNs fire remote monitoring system. The simulation results show that: compared with the traditional fire monitoring system, the system designed in this paper has higher throughput limit, shorter delay, and the accuracy rate of monitoring and alarm is higher than 95%. The experimental results show that the system has good generalization ability and is suitable for large-scale high-rise buildings and large-scale networks.
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Abstract: Aiming at the problems of low fire detection accuracy and high false alarm rate of the current intelligent camera fire accident alarm system, a fire accident alarm system based on fuzzy recognition algorithm is designed. By analyzing the structural principle of the fire detection and alarm system, selecting the CO gas, temperature and smoke sensor selection, designing the corresponding fire signal detection circuit, and designing the single-chip system circuit, including the single-chip clock circuit, reset circuit, power supply circuit and A/D conversion circuit design, on the basis of in-depth study of the Bluetooth communication protocol structure, the hardware design of…the serial interface circuit of the single-chip microcomputer, PC and Bluetooth module has been completed. The fuzzy recognition algorithm is used to set the input and output, establish the control rule table and reasoning relationship, generate the input and output rule table, preprocess the sensor signal, and finally output the fire alarm model through the fuzzy inference system, so as to realize the fire accident alarm of the intelligent camera. The experimental results show that the fire detection accuracy of the proposed method is high, and can effectively reduce the false alarm rate and false alarm rate of the system.
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Keywords: Fuzzy recognition algorithm, fuzzy control, neural network, fire alarm system