Fast template matching based on grey prediction for real-time object tracking

The grey prediction is a common method in the prediction. Studies show that general grey models have high modeling precision when the time sequence varies slowly, but some grey models show low modeling precision for the high-growth sequence. The paper researches the grey modeling for the high-growth sequence using the extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α). To improve the nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α)'s prediction precision and make data have better adaptability to the model, the paper makes improvements in the following three aspects: (1) the paper improves the accumulated generating sequence of original time sequence, i.e. making a new transformation of traditional accumulated generating sequence; (2) the paper improves the model's structure, extends the grey action and builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃p,α); (3) the paper improves the model's background value and uses the value of cubic spline function to approximate the background value. Because the parameters of the new accumulated generating sequence transformed, the nonlinear grey Bernoulli model's time response equation and the background value are optimized simultaneously, the prediction precision increases greatly. The paper builds an extended nonlinear grey Bernoulli model NGBM(1,1,t⌃2,α) using the method proposed and seven comparison models for China's express delivery volume per capita. Comparison results show that the extended nonlinear grey Bernoulli model built with the method proposed has high simulation and prediction precision and shows the precision superior to that of seven comparison models.

This paper presents a new semi-automatic approach to extracting main road centrelines from high-resolution satellite images. The approach is based on active window line segment matching and an improved sequential similarity detection algorithm (SSDA). First, a user-specified point on the road centreline is selected as a reference central point to define a template window, and a thresholding operation is performed within the window. Then a best matched target window along the road is searched to determine the next central point in the road. To avoid the influence of noise, only the line segment of ‘interest’ in the window is matched. An improved SSDA searching strategy is proposed to increase the matching speed. When the best matched target window is found, its centre is used as the next central point in the road, and a new template window is generated. By repeating the matching process, a series of central points can be obtained automatically and the corresponding final road centreline from them is extracted. Our approach also allows some kinds of user interventions in case automatic tracking fails. Experimental results confirm that the proposed approach is capable of rapidly and accurately extracting main road centrelines and has good robustness against noise.

Based on analyzing the traditional image matching algorithms, a fast image matching method, which integrates improved sequential similarity detection algorithm (SSDA) and wavelet transformation, is presented in this paper. Firstly, some basic principles of SSDA image matching and wavelet transformation are introduced. Secondly, according to the shortcoming of traditional SSDA algorithm, a new SSDA algorithm is studied for improving the determining velocity of the threshold in SSDA searching match. By integrating wavelet transformation, an integrated matching algorithm is given out. Finally, the new method is compared with the traditional matching algorithms in experiments. The experimental results show that the new method has higher efficiency and accuracy in image matching and is suitable for the realization of real-time underwater navigation.

Grey extended prediction model based on IRLS and its application on smog pollution

In this paper, three novel grey breakpoint prediction models are proposed based on calculating the development coefficient and grey action of grey prediction models after fuzzy breakpoints, unifying the calculation methods for parameter estimation and the relevant time-response equations, and using the particle swarm optimisation algorithm to optimise the two-stage background values. Finally, the novel grey breakpoint prediction models are used to simulate and forecast the CO2 emissions in BRICS countries. We can see that by setting time breakpoints and fuzzy breakpoint intervals, the novel methods successfully detect abrupt changes in the system and achieve accurate predictions, thus improving the accuracy and applicability of the grey model. The new grey breakpoint prediction models demonstrate better estimation in all cases in CO2 emissions forecasting. The projections show that between 2022 and 2025, CO2 emissions in Brazil and South Africa will decrease each year, while CO2 emissions in China, Russia and India will increase each year, but the upwards trend in India shows signs of slowing.

The traditional global template matching is time consuming, has low accuracy, and cannot be adapted to rotation and scale change. The template matching technique proposed in this study improves the time, accuracy and robustness for printed circuit boards (PCB). In order to shorten the image positioning time, the image preprocessing is implemented on PCB image and the image blocks are labeled to obtain the tagged image, and the feature vector is extracted and the marking point region image is selected. The feature vector with rotation change and scale change robustness is extracted from the tagged image after labeling in the PCB image by using artificial neural network, combined with image moments for training. The marking point region image in the PCB image is selected. The scale value of the marking point region image is estimated by parametric template vector matching. The deflection angle of marking point region image is calculated by Hough transform. The obtained scale value and deflection angle value are used for fast template matching to determine the marking point positioning. The three-dimensional (3D) parabolic curve fitting is implemented in marking point positioning and adjacent pixel position to reach the sub-pixel level accuracy. The experiment showed that the proposed template matching technique for the PCB image with or without noise or angle rotation, the average position accuracy error of each translated image is lower than 7 \(\upmu \)m, and the error standard deviation is lower than 5 \(\upmu \)m. The rotation angle error average and standard deviation of angular error of Hough transform are lower than 0.2\(^{\circ }\), more accurate than orientation code (OC) method. The scale value estimation, relative error average and error standard deviation are lower than 0.004 and 0.006 for the image with or without noise. The average complete positioning time of PCB image at resolution of \(2500\times 2500\) is only 0.55 s, which is better than the 3.97 s of traditional global template matching. The results prove that the template matching technique of this study not only has sub-pixel level high accuracy and short computing time, but also has the robustness of rotation change and scale change interference. It can implement rapid, efficient and accurate positioning.

PurposeIn order to overcome the uncertainty and improve the accuracy of spectral estimation, this paper aims to establish a grey fuzzy prediction model of soil organic matter content by using grey theory and fuzzy theory.Design/methodology/approachBased on the data of 121 soil samples from Zhangqiu district and Jiyang district of Jinan City, Shandong Province, firstly, the soil spectral data are transformed by spectral transformation methods, and the spectral estimation factors are selected according to the principle of maximum correlation. Then, the generalized greyness of interval grey number is used to modify the estimation factors of modeling samples and test samples to improve the correlation. Finally, the hyper-spectral prediction model of soil organic matter is established by using the fuzzy recognition theory, and the model is optimized by adjusting the fuzzy classification number, and the estimation accuracy of the model is evaluated using the mean relative error and the determination coefficient.FindingsThe results show that the generalized greyness of interval grey number can effectively improve the correlation between soil organic matter content and estimation factors, and the accuracy of the proposed model and test samples are significantly improved, where the determination coefficient R2 = 0.9213 and the mean relative error (MRE) = 6.3630% of 20 test samples. The research shows that the grey fuzzy prediction model proposed in this paper is feasible and effective, and provides a new way for hyper-spectral estimation of soil organic matter content.Practical implicationsThe research shows that the grey fuzzy prediction model proposed in this paper can not only effectively deal with the three types of uncertainties in spectral estimation, but also realize the correction of estimation factors, which is helpful to improve the accuracy of modeling estimation. The research result enriches the theory and method of soil spectral estimation, and it also provides a new idea to deal with the three kinds of uncertainty in the prediction problem by using the three kinds of uncertainty theory.Originality/valueThe paper succeeds in realizing both the grey fuzzy prediction model for hyper-spectral estimating soil organic matter content and effectively dealing with the randomness, fuzziness and grey uncertainty in spectral estimation.

In this paper, an improved object tracking algorithm based on Camshift and grey prediction model GM (1.1) was proposed to solve problem of the poor tracking ability in occlusions. The whole tracking system could be divided into three parts: the occlusions identifying system based on Bhattacharyya coefficient analysis, the prediction system based on grey model GM (1.1) and Camshift algorithm. In the tracking process, those three parts run by turns according to different cases. Experiment results of variant video sequences demonstrate that the proposed method can track the objects stably and accurately during occlusions. Keywords-Occlusions, Grey prediction, Camshift, Object tracking

Accuracy and speed are the essential metrics for the template matching algorithms in solving object tracking problems. Since the method based on Best Buddies Similarity (BBS) has achieved the state-of-the-art performance in terms of accuracy, matching speed becomes the shortest piece of wood of the bucket. In this paper, we propose a fast template matching method based on our deformable BBS measure. The deformable BBS measure enables matching to be performed between the patches in varying sizes, and hence leads to even higher accuracy than the original BBS-based methods. More important, we develop a fast potential-area discovery algorithm based on proposal generation and selection. It significantly reduces the numbers of useless attempts on calculating and comparing similarities of impossible image patches. The experimental results show that, with the deformable BBS measure and the fast potential-area discovery, our template matching method outperforms the state-of-the-art methods in terms of accuracy, speed and robustness.

As the number of college students and graduate students increasing, taking part in the entrance exams for postgraduate schools has become more and more popular for college students' choice. The increasing number of taking part in the entrance exams for postgraduate schools has the vital significance to our economic construction and education development. By application of grey system theory and its methods, based on the change in the number of taking part in the entrance exams for postgraduate schools in recent years in China, this article constructed grey model GM(1,1) and grey metabolic prediction model, compared two models, and took some relative optimum processing. At last, using grey metabolic prediction model to forecast the number of students who take part in the entrance exams for postgraduate schools in next five years, provided a reference for the relevant departments.

Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent

Application of a novel nonlinear multivariate grey Bernoulli model to predict the tourist income of China

How to build a football match prediction model and use scientific methods to solve the prediction problem has become a key point in the application of artificial intelligence in the sports industry. In this paper, we choose a BP neural network model that is powerful in processing nonlinear data to perform research. According to the demand, this paper constructs a gray fuzzy prediction model based on neural network, a gray extreme learning machine prediction model, and a gray fuzzy extreme learning machine prediction combination model based on neural network. Moreover, this paper tests the neural network model by comparing actual results with predicted results. In addition, by predicting and analyzing the football league data, this article tests the three models in terms of match result prediction accuracy, data processing speed, data transmission accuracy, match analysis scores, etc., and uses statistical analysis methods to process data, and uses intuitive statistical graphs to obtain the processing results. The research results show that the gray fuzzy extreme learning machine prediction combination model based on neural network constructed in this paper can retain the advantages of a single model and effectively improve the prediction accuracy of the model and the performance of the system.

Based on agricultural production data of Liaoning Province from 1980a to 2009a, using stepwise regression method, the gray prediction method, BP neural network, the grain yield prediction model was respectively established in Liaoning Province, China. The grain yield was predicted with these models, and models were compared. The results show that the yield forecasts relative error of the stepwise regression model, gray prediction model, BP neural network model are respectively: 3.41%, 6.59%, and 1.16%. Among the three models, the order of best fit is the BP neural network model, the less is the stepwise regression model, the least is the gray model. It was proved that the BP neural network model is optimum one with high correspondence degreed and high accuracy for food production forecast in Liaoning Province.

An approach of fast image mosaic is presented, which involves image matching and image intensity smoothing. Image matching includes two procedures, i.e. rough matching and fine matching. In rough matching, the overlapped regions of two adjacent images to be mosaicked are segmented to binary image at first. Then the binary images are filtered by open operation of mathematic morphologic method. In the binary image region of the reference image, feature template is searched and extracted on a given rule. Via XOR operation of the feature template and search region, some possible matching positions in the overlapped region of the other image are got. In the fine matching, the sequential similarity detection algorithm (SSDA) is adopted to perform matching computation in the small regions near the positions got in the rough matching, and then the relative position offsets in X-orientation and Y-orientation between the two adjacent images are got. Based on the result of the image matching, the two images are stitched. An approach of seam-line smoothing is adopted to adjust the intensity of the overlapped area. Simulation experimental results show that the approach greatly improves the operation speed, while the precision remains fine, so it can be applied in real-time mosaicking.