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Delineation of line patterns in images using B-COSFIRE filters

机译:使用B-Casfire过滤器描绘图像中的线条图案

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Delineation of line patterns in images is a basic step required in various applications such as blood vessel detection in medical images, segmentation of rivers or roads in aerial images, detection of cracks in walls or pavements, etc. In this paper we present trainable B-COSFIRE filters, which are a model of some neurons in area V1 of the primary visual cortex, and apply it to the delineation of line patterns in different kinds of images. B-COSFIRE filters are trainable as their selectivity is determined in an automatic configuration process given a prototype pattern of interest. They are configurable to detect any preferred line structure (e.g. segments, corners, cross-overs, etc.), so usable for automatic data representation learning. We carried out experiments on two data sets, namely a line-network data set from INRIA and a data set of retinal fundus images named IOSTAR. The results that we achieved confirm the robustness of the proposed approach and its effectiveness in the delineation of line structures in different kinds of images.
机译:图像中的线条图案描绘是在医学图像中的血管检测等各种应用中所需的基本步骤,航空图像中的河流或道路的分割,墙壁或路面的裂缝等。在本文中,我们呈现培训B- Cosfire过滤器,是主要Visual Cortex的区域V1中一些神经元的模型,并将其应用于不同种类图像中的线条图案描绘。 B-COSFIRE过滤器是可培训的,因为它们在自动配置过程中确定了它们的选择性,给出了兴趣的原型模式。它们可配置以检测任何优选的线结构(例如,段,角落,交叉等),因此可用于自动数据表示学习。我们对两个数据集进行了实验,即从inria和名为iostar的视网膜眼底图像的数据集和数据集。我们实现了拟议方法的稳健性及其在不同种类图像中划分线结构的稳健性的稳健性。

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