Convolutional Networks Based Edge Detector Learned via Contrast Sensitivity Function

摘要

Edge detection extracts rich geometric structures of the image and largely reduces the amount of data to be processed, providing essential input to many visual tasks. Traditional algorithms consist of three steps: smoothing, filtering and locating, in which the filters are usually designed manually and thresholds are selected without strictly theoretical support. In this paper, convolutional networks (ConvNets) are trained to detect edges by learning a group of filters and classifiers simultaneously. In addition, the contrast sensitivity function (CSF) in visual psychology is adopted to determine whether an edge is visible to human visual system (HVS). Edge samples of various appearance are synthesised, and then labelled via CSP for model training. Multichannel ConvNets are trained to perceive edges of different frequencies and composed at last. Compared with classical algorithms, ConvNets-CSF model is more robust to contrast variation and more biologically plausible. Evaluated on USF edge detection dataset, it achieves comparable performance as Canny edge detector and outperforms other classical algorithms.