In this work, two enhancement methods are proposed to speed up junction detection performed by the JUDOCA detector. The first enhancement method minimizes the number of junction candidates on which the circular kernel is applied. This is achieved by introducing a suppression technique that takes both the thin and thick edge images into consideration. The second method works on relaxing the step of checking the edge continuity. Instead of traversing the edge pixel by pixel, using Bresenham's algorithm, sample pixels along the edge are considered. Test results show that an enhancement factor of 62% to 75% in runtime can be reached as a result of applying both enhancements.

In this paper, we propose an edge-based junction detector. In addition to detecting the locations of junctions, this operator specifies their orientations as well. In this respect, a junction is defined as a meeting point of two or more ridges in the gradient domain into which an image can be transformed through Gaussian derivative filters. To accelerate the detection process, two binary edge maps are produced; a thick-edge map is obtained by imposing a threshold on the gradient magnitude image, and another thin-edge map is obtained by calculating the local maxima. Circular masks are centered at putative junctions in the thick-edge map, and the so-called circumferential anchors or CA points are detected in the thin map. Radial lines are scanned to determine the presence of junctions. Comparisons are made with other well-known detectors. This paper proposes a new formula for measuring the detection accuracy. In addition, the so-called junction coordinate systems are introduced. Our operator has been successfully used to solve many problems such as wide-baseline matching, 3-D reconstruction, camera parameter enhancing, and indoor and obstacle localization.

In this paper, we propose an algorithm for fast indoor localization. The algorithm does not require any sensors to be installed; instead, localization is determined using image matching. Our system studies (or learns) the indoor environment through detecting image junctions using the so-called JUDOCA detector. Any 2-edge junction forms a triangle that can be used to store information and recognize the environment afterwards. Correlation is applied to points denoted with respect to one side of the triangle formed by the junction. Experiments show that this approach reaches similar accuracy of the affine-based correlation approach in less processing time.

This paper presents a new junction detection operator that defines junctions as points where linear ridges in the gradient domain intersect. The radial lines that compose the junction are therefore identified by searching, in a circular neighborhood, for directional maxima of the intensity gradient. The proposed algorithm operates on two binary edge maps, the computational complexity of the detection process is then considerably reduced.