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Sonar image recognition using synthetic discriminant functions implemented with the Karhunen Loeve transform

机译:使用Karhunen Loeve变换实现的合成判别功能的声纳图像识别

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This paper discusses modified synthetic discriminant functions (SDF) using a Karhunen Loeve transform (KLT) used for improved sonar image recognition. The SDF filter synthesis involves using the whole image which in turn creates redundancies in the distinguishing features. A number of different schemes have been used to try to reduce the data in SDF filters in order to make them more practical, efficient, and reliable. The KLT is one method to reduce the redundancies in a set of training images to create a new data matrix. This data matrix has a new coordinate system in which the axes of the system are in the direction of the eigenvectors of the covariance matrix of the training set. With the realigned data found in the data matrix, the principle component images can be extracted. Principle component images are comprised of the variations of the original training images. This minimizes the training data to the necessary information that is needed for image recognition. The principle component images then become the training set to be used in the SDF filter. Because the KLT allows for the reduction in redundancies by examining only the variations of this new training set, it will increase the correlation found in this implementation of the SDF filter.
机译:本文讨论了使用用于改进声纳图像识别的Karhunen Loeve变换(KLT)修改了合成判别功能(SDF)。 SDF滤波器合成涉及使用整个图像,这反过来又会在区分特征中创造冗余。已经使用了许多不同的方案来试图减少SDF过滤器中的数据,以使其更实用,高效,可靠。 KLT是减少一组训练图像中冗余以创建新数据矩阵的方法。该数据矩阵具有新的坐标系,其中系统的轴呈训练集的协方差矩阵的特征向量方向。利用数据矩阵中的重新调整数据,可以提取原理成分图像。原理成分图像包括原始训练图像的变化。这使得训练数据最小化到图像识别所需的必要信息。然后,原理成分图像成为在SDF滤波器中使用的训练集。因为KLT允许通过仅检查该新培训集的变体来减少冗余,所以它将增加SDF过滤器的此实现中找到的相关性。

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