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Cholerny Spammer
Joined: 03 Nov 2010
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 Posted: Wed 20:07, 12 Jan 2011 Post subject: UGG 激安 Car-shaped contour based featur |
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Car-shaped contour based feature extraction method
Class device. Therefore it is necessary to present to the m-dimensional feature vector dimension feature vector (m ) can be followed to determine; Second,[link widoczny dla zalogowanych], the point sequence can be seen as circular, and has a rotation, translation degeneration,[link widoczny dla zalogowanych], so the point sequence can be used to determine the AR parameters to classify the shape of the final features. After using the regression model feature vector from the Y (Y, Y,[link widoczny dla zalogowanych], ..., Y) becomes a (a, n.,[link widoczny dla zalogowanych], ...,[link widoczny dla zalogowanych], A) (m <), the spatial characteristics into R. Although the characteristics of the original features of n y no longer have meaning, but in fact it is a weighted combination of features y. The following question is how to get the best of the AR parameters. For the linear model, often using the least square method to find the unknown parameters. So that Y, = Ⅱ 04-2 (Y a) i-m4-1, m4-2, ... (9); L matrix form of the equation is as follows】 Y1Y + is a X where Y = [Ⅱ 0. . ...] From the least squares estimate = (x curse) A (y) solving equations (11) point sequence can be obtained under the AR parameters. yIy2 ● ● ● y A (1O) q 4 Zhang Hongwen: A car-shaped contour-based feature extraction method for the rank of 87 on the choice of regression model, in pattern recognition is relevant to the question under study. In speech recognition, usually take a 10 ~ 15l '. In recognition of this pattern desirable for a 2 to 6 [. 4, we choose to distance measurement pattern classification classifier, which uses the known model and a new model to be tested to measure the distance between the realization of the right classification. Let {(m)} for the category known model, the class feature vector for each sample is expressed as (n, \m +1 parameters were composed by the AR features (1 ≤ ≤ Ⅳ / 2). Let {(%)) to be tested models, the mean square error of a (n a) (P: 1, ..., N / 2) (12) t-n is the distance metric used. If there are cars with class, the classification decision as follows {()) ∈ n If the D-mn {Dl a 1,2, ..., n} if the expansion (12) is a Σ + Σ D A 2Σ (13) is independent of the minimum and Σ. If all of the prior known patterns have normalized after treatment by the value Σ Σ.: 1, the minimum has nothing to do with the surface Σ. Estuary Σn related to only to, and entry Σ. the greater the distance is smaller. Thus, the minimum distance classifier can be interpreted as measuring strike ‰ and the maximum amount of self-related. 5 Simulation results of different vehicles by the transport sector Tonnage fee requirements, China's existing vehicles can be divided into the following 5 categories: ① motorcycle, 2t samples of the following vehicles; ② 2t the following passenger (cargo) vehicles; ⑧ 2 ~ 8t passenger (freight) car, car trailer; ④ 8 ~ 15t-off (goods) vehicle; ⑤ 15t or more. different tonnage car, its shape is different. without loss of generality we can select the form shown in Figure 5 for the three types of vehicle simulation goal, the recognition rate of 94% or more. of course In practice, consideration should be given to the loading situation. need to front, hooks and other parts of the car carrying the information to determine whether, and to car-shaped contours in the form of removal of goods. Figure 5 car-shaped contour map
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