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首页> 外文期刊>Serbian Journal of Electrical Engineering >Television images identification in the vision system basis on the mathematical apparatus of cubic normalized B-splines
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Television images identification in the vision system basis on the mathematical apparatus of cubic normalized B-splines

机译：基于三次归一化B样条数学装置的视觉系统电视图像识别

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摘要

The solution the task of television image identification is used in industry when creating autonomous robots and systems of technical vision. A similar problem also arises in the development of image analysis systems to function under the influence of various interfering factors in complex observation conditions complicated the registration process and existing when priori information is absent, in background noise type. One of the most important operators is the contour selection operator. Methods and algorithms of processing information from image sensors must take into account the different character of noise associated with images and signals registration. The solution of the task of isolating contours, and in fact of digital differentiation of two-dimensional signals registered against a different character of background noise, is far from trivial. This is due to the fact that such task is incorrect. In modern information systems, methods of numerical differentiation or masks are usually used to solve the task of isolating contours. The paper considers a new method of differentiating measurement results against a noise background using the modern mathematical apparatus of cubic smoothing B-splines. The new high-precision method of digital differentiation of signals using splines is proposed for the first time, without using standard numerical differentiation procedures, to calculate the values of the derivatives with high accuracy. In fact, a method has been developed for calculating the image gradient module using spline differentiation. The method, as proved by experimental studies, and computational experiments has higher noise immunity than algorithms based on standard differentiation procedures using masks.
机译：在创建自主机器人和技术视觉系统时，该行业中使用电视图像识别任务的解决方案。在图像分析系统的开发中，在背景噪声类型下，在复杂的观察条件下，复杂的配准过程和缺少先验信息时存在的各种干扰因素的影响下工作的图像分析系统，也出现了类似的问题。轮廓选择运算符是最重要的运算符之一。处理来自图像传感器的信息的方法和算法必须考虑与图像和信号配准相关的噪声的不同特征。隔离轮廓的任务，实际上是针对背景噪声不同的特征对二维信号进行数字微分的解决方案，绝非易事。这是由于该任务不正确。在现代信息系统中，通常使用数字微分或掩码的方法来解决隔离轮廓的任务。本文考虑了一种使用三次三次平滑B样条的现代数学仪器在噪声背景下区分测量结果的新方法。首次提出了一种新的高精度的样条信号数字微分方法，无需使用标准的数值微分程序，即可高精度计算导数的值。实际上，已经开发出一种用于利用样条微分计算图像梯度模块的方法。实验研究和计算实验证明，该方法比基于使用掩码的标准区分程序的算法具有更高的抗噪性。

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