It solves the problem that the unitary contour presentation can not correctly extract face contour in a face image which suffers from scale, rotation etc. The definition of the internal and external energy function is provided. At the same time, the global matching algorithm and local matching algorithm is given. The experiment shows that this presentation and the accompanying matching algorithm can be used to extract the face contour very well. So the image segmentation can be implemented by using it.②By analyzing the recognition principle of PCA method, we can conclude that the face images coming from different surrounding consist of different face image space. This is the essential reason that makes the generality of PCA method worse. Also, we give a measurement means to measure the distance from different face image space, so we can analyze face image space more conveniently.③We also construct various scale models and rotation pose models to detect the scale and rotating angle of face image to be recognized. The experiment results show that the detecting precision is very high. So it is good for face image feature extraction and face image representation.④Similarly, we construct local feature models of face image and utilize them to detect the local feature of face image. At the same time, we put forward a novel face image local feature detection algorithm, locating step by step. The experiment results show that this method can accurately detect the location of local face feature in a image.⑤A novel face image presentation model, dual attribute graph , is put forward. Firstly, it utilizes attribute graph to present the face image, then exact the local principal component coefficient and Gabor transform coefficient of thc pixels which corresponds to the nodes of the graph as the attribute of the nodes. This representation fully makes use of the statistical characteristic of the local face feature and utilizes Gabor transform to present the topographical structure of face image. So DAG has more general property.⑥Based on the DAG presentation, we give a DAG matching function and matching algorithm. During the design of the function and algorithm, the noise factor, e. g., lighting, scale and rotation pose are considered and tried to be eliminated. So the algorithm can give more general property.⑦A general face image recognition system is implemented. The experiment show the system can get better recognition performance under the noise surrounding of lighting, scale and rotation pose.

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你想不想使用京都藝術(shù)中心演出空間？

Abstract］This paper studies the animation industry by exploring European animation festivals panoramically from the perspective of aesthetic economy and space economy. It is found that semiotic products with great semiotic meaning are not a kind of transcendental presentation but daily reality in the aesthetic economy and space economy era. The animation industry, which is focused on cultural and creative content producing, is a rising industry in which the animation festival is not only the exhibition of traditional commodities but also a creative arena of meaning produce. The restructuring of place for European animation festivals is followed the basic requirement of aesthetic economy and space economy, since the festivals add new semiotic meaning and cultural elements to the original places.

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graphics presentation space：圖形表達(dá)空間

圖形端口 graphics port | 圖形表達(dá)空間 graphics presentation space | 圖形表達(dá)空間窗口 graphics presentation space window

graphics presentation space window：圖形表達(dá)空間窗口

圖形表達(dá)空間 graphics presentation space | 圖形表達(dá)空間窗口 graphics presentation space window | 圖形基元 graphics primitive

graphics presentation space window：圖形表達(dá)空間視窗

"圖形表達(dá)空間","graphics presentation space" | "圖形表達(dá)空間視窗","graphics presentation space window" | "圖形基元","graphics primitive"