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To meet the requirements of some injection simulation platforms for reality, a real adaptive topography optimization algorithm based on Real Time Optimally Adapting Mesh (ROAM) algorithm was proposed. By taking the terrain generated by ROAM as a base grid, a new integrated parameter Y was proposed, and a given threshold was compared with the Y parameter to determine whether the local splitting was implemented or not. The sharp features of the terrain were extracted from a vertex that satisfy the splitting conditions, and the local curve surface was splitted by Loop algorithm. Finally, a video stream was generated by rotation mapping algorithm in real-time roaming process and it was sent to the injection simulation system to be displayed by a Digital Visual Interface (DVI) display. Experimental results show that the average of roaming frame rate of proposed algorithm is 45.1303 frame/s and the peak frame rate is close to 50 frame/s, higher than that of traditional algorithm. Moreover, the obtained terrain could display on the DVI display on the injection simulation platform with an average of roaming frame rate of 45 frame/s, which satisfies the requirements of the injection simulation systems for the reality.

To solve the problem brought by high dimensionality of multi-spectral images during color reproduction,a nonlinear dimensionality reduction method for multi-spectral images was presented.Firstly,according to the characteristics of human visual system,the CIE standard observer color matching functions were weighted to the source spectral reflectance and a Principal Component Analysis(PCA) method was used to the weighted spectrum to effectively improve the colorimetric precision and color difference stability of dimensionality reduction.Then,for the spectral reflectance loss caused by weighting color matching functions,a PCA method was imposed on the lost spectrum to compensate the lost spectral accuracy caused by the improvement of colorimetric precision to effectively improve the spectral precision of dimensionality reduction.Finally the principal components obtained from the first two steps were combined to form the low-dimensional spectral data.Experiments show that the proposed method can offer the average spectral precision in 0.013 9,average colorimetric precision in 0.705 8,and the color difference stability in 1.950 6,which is increased by 14% and 15%,47% and 68%,as well 84% and 82% as comparied those of the PCA and LabPQR methods.The method outperforms the existing methods in the colorimetric accuracy,spectral accuracy and color difference stability under different illuminants.