Automatic Recognition of Coded Targets Using Feature Based Matching Algorithms in a Ubiquitous GIS

Accurate positioning is an important problem in many fields especially Geographic Information System (GIS). With the advent of ubiquitous computing, a vast and massive change took place in various technologies, and a new generation of GIS, called ubiquitous GIS (UBGIS), was created. One of the most important aspects transformed by ubiquitous computing is in positioning process. ubiquitous GIS and its components provide an environment where position information can be accessed both inside and outside. Computer Vision and Vision based approaches could be a good and appropriate solution to improve positioning accuracy in a Ubiquitous GIS. Using simple and well-known objects such as targets is an appropriate method. Target recognition is important in determining the center and code of the target. In recent years, especial kind of targets called Coded Targets are considered in different fields of vision based approaches and the demand for a Coded Target guaranteeing automatic, error-free correspondence and accurate image point measurement, has been dramatically increased. Therefore, automatically detecting, matching, and determining the center coordinates of Coded Targets are critical issues. Due to various factors in the environment, automatic execution of this process is very difficult and complex or accuracy and speed are not suitable. This paper aims to propose a new method using feature based matching algorithms for automatically recognizing Coded Targets and identifying their centers with sub-pixel accuracy, which can be used to enhance positioning accuracy in ubiquitous GIS. To achieve this aim, feature based matching algorithms and combining local feature detectors and descriptors like SIFT, SURF, and AKAZE are used to find corresponding image points and automatically recognition of Coded Targets. Therefore, suitable matching algorithm is chosen by comparing different matching algorithms. Results show that the best matching algorithm for this usage is SIFT-SURF that means using SIFT descriptors and SURF detectors will lead to best matching results. Then K-means clustering method is applied to distinguish Coded Targets and extract code of target with respect to template targets that are stored in the database. The cluster with the largest number of corresponding points belongs to the template Coded Target. In second stage a bounding box around the matched Coded Target is considered by defining minimum and maximum coordinates of corresponding points around target, so that there is only one target in this boundary. Then the image is cut in this boundary to firstly increase the speed of the calculation, because the search area for an image gets smaller and secondly reduce the possibility of mistake, because the other features and targets in the cut image are almost eliminated. Then center coordinates of Coded Targets are computed by finding contours in this bounding box and fitting a Hough ellipse to central ellipse of target. Finally, the center of this fitted ellipse is computed as the center of Coded Target. The results of implementing the methods are compared with well-known photogrammetry software called Agisoft (modelling and accurate measuring based on basic photogrammetry and computer vision). Results demonstrate sub pixel accuracy {0.574, 0.496 pixel} in center determination in X and Y direction respectively and success possibility of 63% in code recognition.