C. Adaptive Template Matcher (ATM)As mentioned above, one of our const的简体中文翻译

C. Adaptive Template Matcher (ATM)A

C. Adaptive Template Matcher (ATM)As mentioned above, one of our constraints was to have a fully generic TLR. Therefore, and in order to be able to adapt our algorithm to different types of traffic lights, we designed Adaptive Templates. Those templates are evaluated with our Adaptive Template Matcher (ATM).Template Matching was used previously in different recognition process [11]-[2]. This technique is usually slow when applied on the whole image. However, we use the previously detected spots as hypothesis for template matching. Hence, we create candidates only where spots were detected. Those candidates will then be evaluated and either accepted or rejected according to their matching confidence value. The fact that we use the template matching only where spots were previously found, make it a lot faster (as detailed in the Results section).An Adaptive Template can be defined as a combination of the 2D visual shape representations of the 3D elements which form the real object. In addition, templates also define algorithmic operators linked to one or more elements and which will be evaluated at run time. Both elements and operators can have different weights and matching confidence thresholds, or even be set to non discriminatory. Therefore, if any non discriminatory element or operator failed, it prevents the candidate from being rejected.The matching process is the recursive evaluation of a template and its hierarchy, until all its elements and linked operators have been evaluated. The confidence value of an element is computed according to weight and confidence value of each child element or linked operator.Geometry Definition ATM uses the common top-down approach to define template geometry. This simple approach, shown in Fig. 5, involves decomposing a real 3D object (traffic light in our case) into 2D visual shapes.
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C.自适应模板匹配(ATM)<br><br>正如上面提到的,我们的制约因素之一是有一个完全通用的TLR。因此,为了能够为我们的算法适应不同类型的交通灯,我们设计了自适应模板。这些模板与我们的自适应模板匹配(ATM)进行评估。<br><br>模板匹配是在不同的识别处理先前使用的[11] - [2]。当在整个图像上应用该技术通常是缓慢的。但是,我们使用先前检测点作为假设的模板匹配。因此,我们创建只有在检测点的候选人。这些候选人将然后根据它们的匹配的置信度值进行评价,并接受或拒绝。我们使用的模板匹配只有在点以前被发现,这个事实让它快了很多(如在结果部分详细介绍)。<br><br>一种自适应模板可以被定义为在形成所述真实对象的三维元件的2D视觉形状表示的组合。此外,模板还定义了连接到一个或更多的元件,并且将在运行时被评估算法运算符。两个元件和运营商可以具有不同的重量和匹配置信度的阈值,或甚至被设定为不歧视。因此,如果任何非歧视性元件或操作失败了,它防止从被拒绝的候选者。<br><br>匹配的过程是一个模板,它的层次结构的递归计算,直到它的所有元素,并联系运营商进行了评估。元素的置信度值是根据重量和每个子元素的置信度值或链接运营商计算的。<br><br>几何定义ATM采用了常见的自上而下的方法来定义模板的几何形状。这种简单的方法,在图5中所示,包括分解的真实3D对象(在我们的情况下,交通灯)转换成2D视觉形状。
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结果 (简体中文) 2:[复制]
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C. Adaptive Template Matcher (ATM)<br><br>As mentioned above, one of our constraints was to have a fully generic TLR. Therefore, and in order to be able to adapt our algorithm to different types of traffic lights, we designed Adaptive Templates. Those templates are evaluated with our Adaptive Template Matcher (ATM).<br><br>Template Matching was used previously in different recognition process [11]-[2]. This technique is usually slow when applied on the whole image. However, we use the previously detected spots as hypothesis for template matching. Hence, we create candidates only where spots were detected. Those candidates will then be evaluated and either accepted or rejected according to their matching confidence value. The fact that we use the template matching only where spots were previously found, make it a lot faster (as detailed in the Results section).<br><br>An Adaptive Template can be defined as a combination of the 2D visual shape representations of the 3D elements which form the real object. In addition, templates also define algorithmic operators linked to one or more elements and which will be evaluated at run time. Both elements and operators can have different weights and matching confidence thresholds, or even be set to non discriminatory. Therefore, if any non discriminatory element or operator failed, it prevents the candidate from being rejected.<br><br>The matching process is the recursive evaluation of a template and its hierarchy, until all its elements and linked operators have been evaluated. The confidence value of an element is computed according to weight and confidence value of each child element or linked operator.<br><br>Geometry Definition ATM uses the common top-down approach to define template geometry. This simple approach, shown in Fig. 5, involves decomposing a real 3D object (traffic light in our case) into 2D visual shapes.
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结果 (简体中文) 3:[复制]
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C、 自适应模板匹配器(ATM)<br>如上所述,我们的一个约束是要有一个完全通用的TLR。因此,为了使我们的算法能够适应不同类型的红绿灯,我们设计了自适应模板。这些模板使用我们的自适应模板匹配器(ATM)进行评估。<br>模板匹配以前用于不同的识别过程[11]-[2]。这种技术在应用于整个图像时通常速度很慢。然而,我们使用先前检测到的点作为模板匹配的假设。因此,我们只在检测到斑点的地方创建候选。然后,将根据这些候选人的匹配置信值对其进行评估和接受或拒绝。事实上,我们只在先前发现斑点的地方使用模板匹配,这会使它更快(如结果部分所述)。<br>自适应模板可以定义为构成真实对象的三维元素的二维视觉形状表示的组合。此外,模板还定义了链接到一个或多个元素并将在运行时计算的算法运算符。元素和运算符可以具有不同的权重和匹配的置信阈值,甚至可以设置为非歧视性。因此,如果任何非歧视性元素或运算符失败,它将阻止候选人被拒绝。<br>匹配过程是对模板及其层次结构的递归求值,直到对其所有元素和链接运算符求值为止。元素的置信值根据每个子元素或链接运算符的权重和置信值计算。<br>几何定义ATM使用通用的自顶向下方法来定义模板几何。这个简单的方法,如图5所示,包括将真实的3D物体(在我们的例子中是红绿灯)分解成2D视觉形状。<br>
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