传统图割算法解决双目立体匹配问题,在高精度的同时需要消耗大量时间.提出一种新的算法,将最小割求取问题转化为贪心问题,从而降低算法复杂度.由于转化后的图割在处理图像区域连续化问题时效率低下,给出了图割与区域匹配相结合的GR(Grap...传统图割算法解决双目立体匹配问题,在高精度的同时需要消耗大量时间.提出一种新的算法,将最小割求取问题转化为贪心问题,从而降低算法复杂度.由于转化后的图割在处理图像区域连续化问题时效率低下,给出了图割与区域匹配相结合的GR(Graphic Cut in Region)算法,算法不仅将图割理论运用到立体匹配问题中,且在求取初始视差时提出了用窗口单位化匹配代价算法来提高初始视差的精度.实验证明,该算法在图像区域连续化时有较好的效果,明显提高了匹配的精度,且复杂度也大大降低.展开更多
Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （K...Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.展开更多
This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence re...This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence regions for parameters and parameter subsets in terms of statistical curvatures are given based on the likelihood ratio statistics and score statistics. Several previous results, such as [1] and [2] are extended to AR(q) nonlinear regression models.展开更多
基金supported by the National Natural Science Foundation of China(11471161)the Nanjing Forestry University Grant(163101004)the Nanjing Science and Technology Innovation Item(013101001)
文摘传统图割算法解决双目立体匹配问题,在高精度的同时需要消耗大量时间.提出一种新的算法,将最小割求取问题转化为贪心问题,从而降低算法复杂度.由于转化后的图割在处理图像区域连续化问题时效率低下,给出了图割与区域匹配相结合的GR(Graphic Cut in Region)算法,算法不仅将图割理论运用到立体匹配问题中,且在求取初始视差时提出了用窗口单位化匹配代价算法来提高初始视差的精度.实验证明,该算法在图像区域连续化时有较好的效果,明显提高了匹配的精度,且复杂度也大大降低.
文摘Chaos theory has taught us that a system which has both nonlinearity and random input will most likely produce irregular data. If random errors are irregular data, then random error process will raise nonlinearity （Kantz and Schreiber （1997））. Tsai （1986） introduced a composite test for autocorrelation and heteroscedasticity in linear models with AR（1） errors. Liu （2003） introduced a composite test for correlation and heteroscedasticity in nonlinear models with DBL（p, 0, 1） errors. Therefore, the important problems in regression model axe detections of bilinearity, correlation and heteroscedasticity. In this article, the authors discuss more general case of nonlinear models with DBL（p, q, 1） random errors by score test. Several statistics for the test of bilinearity, correlation, and heteroscedasticity are obtained, and expressed in simple matrix formulas. The results of regression models with linear errors are extended to those with bilinear errors. The simulation study is carried out to investigate the powers of the test statistics. All results of this article extend and develop results of Tsai （1986）, Wei, et al （1995）, and Liu, et al （2003）.
文摘This paper constructs a set of confidence regions of parameters in terms of statistical curvatures for AR(q) nonlinear regression models. The geometric frameworks are proposed for the model. Then several confidence regions for parameters and parameter subsets in terms of statistical curvatures are given based on the likelihood ratio statistics and score statistics. Several previous results, such as [1] and [2] are extended to AR(q) nonlinear regression models.
