On edge linking

Abstract

Edge detection is a fundamental rst step in many computer vision and image processing applications. Since traditional edge detection algorithms produce binary edge maps as output (which usually consist of multi-pixel wide, disconnected -especially in noisy images- edge fragments), an additional edge linking step is usually employed to clean up the resulting edge map and combine disjoint edge fragments. An edge linker takes a binary edge map as input and is expected to generate high-quality (one-pixel wide and contiguous) edge segments (chain of pixels), which are then used in such applications as line, arc and shape detection, image segmentation, tracking and registration, among many others. In this thesis, two edge linking algorithms are proposed: The rst algorithm makes use of the Smart Routing (SR) step of the recently proposed edge segment detection algorithm Edge Drawing (ED), to convert Canny's binary edge maps to edge segments; thus the name CannySR. The second algorithm takes in a binary edge map generated by any arbitrary traditional edge detection algorithm and converts it to a set of edge segments; lling in one pixel gaps in the edge map, cleaning up noisy edge pixel groups and thinning multi-pixel wide edge pixel formations in the process. The algorithm walks over the edge map based on the predictions generated from its past movements; thus the name Predictive Edge Linking (PEL). We evaluate the performance of CannySR and PEL both qualitatively using visual experiments and quantitatively within the precision-recall framework of the Berkeley Segmentation Benchmark (BSDS 300), and compare its performance with ED, which is a natural edge segment detection algorithm. Both visual experiments and quantitative evaluation results show that both CannySR and PEL greatly improves the modal quality of binary edge maps produced by traditional edge detectors, and take a very small amount of time to execute making them suitable for real-time image processing and computer vision applications.