Jiann-Shu Lee National Huwei Institute of Technology (Taiwan) Jzau-Sheng Lin National Chin-Yi Institute of Technology (Taiwan) Ching-Tsorng Tsai Tunghai Univ. (Taiwan) Ding-Horng Chen Southern Taiwan Univ. of Technology (Taiwan)
Junctions convey rich information and are often used to identify objects in the scene or for stereoscopic matching or displacement vector measuring. In a previous work, an accurate wavelet-based corner detection method has been proposed. This work is an extension of the work on junction characterization and detection in real images. We first perform an analytical study for a general junction model that allows us to better understand the behavior of junctions in the wavelet domain. Two new properties, including the scale proportion property of the modulus and the scale invariant property of orientation in the wavelet domain, are derived. These properties are applied to accurately detect junctions. Several promising experimental results have been carried out using noisy and noisy-free real images. These results show that our approach is a general methodology for accurate detection of discrete surface intersection.