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Analysing the patterns of spatial contrast discontinuities in natural images for robust edge detection

机译：鲁棒边缘检测自然图像中空间对比不连续性的模式分析

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摘要

The pattern of spatial contrast discontinuities in natural images has been analysed in the present work, and based on it, a new adaptive model of the bio-inspired Difference of Gaussian (DOG)-based edge detector has been designed. The distinguishing feature of the proposed filter is that the magnitude of surround suppression in receptive field of the DOG is adaptively adjusted depending on the nature of discontinuity of the edge profile. The model is based on the biological evidences indicating the possibility that human brain may be endowed with the ability to perform Fourier decomposition of visual images into its various components of spatial frequencies. It may be shown that information obtained from such a Fourier decomposition may help to measure the strength of contrast (sharpness of discontinuity) in the intensity profile across any possible edge in the natural image. In the present model, it is assumed that the magnitude of surround suppression in an excitatory-inhibitory receptive field is dependent on the sharpness of discontinuity. The suppression is strong when the edge contrast is poor, while it becomes weaker as the edge contrast is high. At a biphasic edge, the surround suppression is vanishingly small. Natural images collected from benchmark databases are used to evaluate the efficiency and robustness of the proposed model for the detection of edges. The result shows that the edge maps generated through the proposed model are at par, if not more effective as compared to the classical edge detectors like Canny. The performance of the proposed model is also compared with a number of recently proposed alternative adaptive models for edge detection.
机译：在本工作中分析了自然图像中的空间对比不连续性的模式，并基于它，设计了一种新的高斯（狗）的边缘检测器的生物启发差异的新自适应模型。所提出的滤波器的区别特征是根据边缘轮廓的不连续性的性质，自适应地调节狗的接受场中的环绕抑制的大小。该模型基于生物证据，表明人体大脑可以具有能够在其各种空间频率的各个组件中执行傅立叶分解的能力。可以示出从这种傅立叶分解获得的信息可以有助于在自然图像中的任何可能的边缘中测量强度分布中的对比度（不连续性的清晰度）。在本模型中，假设兴奋性抑制接收领域的环绕抑制幅度取决于不连续性的锐度。当边缘对比度较差时，抑制很强，而边缘对比度很高，它变弱。在双相边缘处，环绕抑制越来越小。从基准数据库收集的自然图像用于评估所提出的模型检测边缘的效率和稳健性。结果表明，通过所提出的模型生成的边缘映射在于与古典边缘探测器相比，如图所示的典型边缘探测器。拟议模型的性能也与最近提出的边缘检测的最近提出的替代自适应模型进行了比较。

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