Fast edge-preserving gravity-like image interpolation

In this paper we propose a novel image interpolation algorithm which preserves edges and keeps a natural texture of interpolated images. The algorithm is based on an idea that only pixels that belong to the same side of an edge should be used in interpolation of pixels that belong to an edge. Beside similarity-based separation of known interpolation pixels a gravity-like interpolation coefficient set is also introduced in order to support different number of interpolation pixels and their location in two dimensional plane. The algorithm also applies arbitrary scaling factors, thus offering a broader scope of applications. Use of a local set of interpolating points makes the proposed algorithm suitable for applications on resource-limited platforms. The edge performance is demonstrated for structured geometric forms, while a general interpolated image quality is evaluated using objective measures and subjective comparisons. A comparison with some relevant interpolation algorithms shows the desirable tradeoff between image quality (sharpness and texture) and requested computing power (run-time).

Speech Feature Parameter Extraction and Recognition Based on Interpolation

The research of the existing speech recognition is based on speech feature parameter, acco-rding to the shortage of poor anti noise and larger storage capacity, etc. So, curve interpolation has been introduced into speech feature parameter extraction to enhance that. Refer to the speech spectrum dynamic changes and the short-time energy smooth stationary characteristics of speech signal, this paper puts forward and designs an arithmetic of speech feature parameter extraction based on interpolation, constructs the feature parameter extraction and personal identification scheme based on speech, and also designs critical modules algorithm. The detail process of feature parameter extraction: firstly, it creates two-dimensional coordinate for each frame data. Then, according to two-dimensional coordinate, it performs Lagrange cubic interpolation for segmentation the data in a signal frame. Get the interpolation coefficient, average the interpolation coefficient for a signal frame, here the average value is seen as the feature parameter for each frame. Lastly, the each frame’s feature parameter is connected in series to form feature parameter of the speech segment. The arithmetic has been simulated an experiment, in order to confirm the applicability and feasibility. The results illustrates the method has preferable anti noise performance, especially expression and storage for overall speech segment feature parameter show more obvious advantages.

Analysis and Research of Extrapolation Accuracy with Interpolation Model Based on Network RTK

In network RTK the models extrapolation accuracy of spatial error is directly related to RTK position validity and reliability. So the extrapolation accuracy of coordinate linear interpolation model and distance linear interpolation model are studied in this paper. Firstly, according to the root mean square of interpolation coefficient of model and the law of errors propagation, two models stability and the factors which affected the extrapolation accuracy are analyzed from the relationship of the models linear function. Secondly, in different areas out of net the change regularity of extrapolation error is further discussed with types of data in different length baselines. The result shows that extrapolation accuracy of the above model are related to the net shape of network RTK, the estimation precision of the comprehensive error that exists between reference stations, and the distance that rover is away from the net. The extrapolation accuracy of the coordinate linear interpolation model is better than the distance linear interpolation model. In the areas that not more than 40km to the net external it can be reached within 5cm estimation accuracy of the extrapolation comprehensive error. It is helpful to rapid high-precision position out of the network.