Detection of curved lines with B-COSFIRE filters: A case study on crack delineation
This addresses the challenge of curvilinear structure detection for applications like medical imaging and remote sensing, though it is incremental as it builds on existing brain-inspired filter methods.
The paper tackled the problem of detecting thin or incomplete curvilinear structures in noisy images, such as cracks in pavements, and achieved state-of-the-art results with an F-measure of 0.865 using B-COSFIRE filters.
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. %The visual system of the brain has remarkable abilities to detect curvilinear structures in noisy images. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure=0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.