生成式算法的关键是对算法函数的准确定义,从而将特定假设与图像信息进行比对,如果算法函数失准,则无法实现最优模型参数的匹配,导致运动约束降低和的英语翻译

生成式算法的关键是对算法函数的准确定义,从而将特定假设与图像信息进行比

生成式算法的关键是对算法函数的准确定义,从而将特定假设与图像信息进行比对,如果算法函数失准,则无法实现最优模型参数的匹配,导致运动约束降低和出现异常值的概率增加。构建针对较高图像噪声和较低模型配置的高鲁棒性算法函数较为困难,一方面由于生成式算法需要对模型参数进行合理可靠的初始推测,另一方面被捕捉目标需要在开始阶段以特定的姿势进行初始标定
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结果 (英语) 1: [复制]
复制成功!
The key to the generative algorithm is the accurate definition of the algorithm function, so as to compare the specific hypothesis with the image information. If the algorithm function is misaligned, the matching of the optimal model parameters cannot be achieved, resulting in the reduction of motion constraints and the probability of outliers. Increase. It is difficult to construct a highly robust algorithm function for higher image noise and lower model configuration. On the one hand, generative algorithms need to make reasonable and reliable initial guesses for model parameters, and on the other hand, the captured target needs to be specified in the initial stage. pose for initial calibration
正在翻译中..
结果 (英语) 2:[复制]
复制成功!
The key of generative algorithm is to accurately define the algorithm function, so as to compare specific assumptions with image information. If the algorithm function is inaccurate, the matching of optimal model parameters cannot be realized, resulting in the reduction of motion constraints and the increase of the probability of outliers. It is difficult to construct a highly robust algorithm function for high image noise and low model configuration. On the one hand, the generative algorithm needs to make reasonable and reliable initial speculation on the model parameters, on the other hand, the captured target needs to be initially calibrated with a specific pose in the initial stage
正在翻译中..
结果 (英语) 3:[复制]
复制成功!
The key of generative algorithm is the accurate definition of algorithm function, so that specific assumptions can be compared with image information. If the algorithm function is inaccurate, the matching of optimal model parameters can't be achieved, resulting in the reduction of motion constraints and the increase of the probability of outliers. It is difficult to construct a robust algorithm function with high image noise and low model configuration. On the one hand, the generative algorithm needs reasonable and reliable initial estimation of model parameters; on the other hand, the captured target needs to be initially calibrated in a specific posture at the beginning stage.
正在翻译中..
 
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