Abstract
Image matching techniques offer valuable opportunities for the construction industry. Image matching, a fundamental process in computer vision, is required for different purposes such as object and scene recognition, video data mining, reconstruction of three-dimensional (3D) objects, etc. During the image matching process, two images that are randomly (i.e., from different position and orientation) captured from a scene are compared using image matching algorithms in order to identify their similarity. However, this process is very complex and error prone, because pictures that are randomly captured from a scene vary in viewpoints. Therefore, some main features in images such as position, orientation, and scale of objects are transformed. Sometimes, these image matching algorithms cannot correctly identify the similarity between these images. Logically, if these features remain unchanged during the picture capturing process, then image transformations are reduced, similarity increases, and consequently, the chances of algorithms successfully conducting the image matching process increase. One way to improve these chances is to hold the camera at a fixed viewpoint. However, in messy, dusty, and temporary locations such as construction sites, holding the camera at a fixed viewpoint is not always feasible. Is there any way to repeat and retrieve the camera’s viewpoints during different captures at locations such as construction sites? This study developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results showed that images captured while using this approach had less image transformation in contrast to images not captured using this approach.
Highlights
The era of computer vision started in the early 1970s [1]
The first task was non-sensor-based for all the orientation value were similar for both tasks
To find change in experiment orientation to ofdiscuss the camera around of the the average values for Thethe results of the the accuracy theX-axis, two approaches forrotation producing pictures each group of pictures were compared with the reference rotation, and with each resembling the reference picture regarding the X and Y directions are presented in Table 1 and are other
Summary
The era of computer vision started in the early 1970s [1]. Computer vision is defined as a trick “to extract descriptions of the world from pictures or sequences of pictures” [2]. The process of image matching is required for tracking targets [8], image alignment and stitching [9,10], reconstruction of three-dimensional (3D) models from images [11], object recognition [12], face detection [13,14], data mining [15], robot navigation [8], motion tracking [16,17], and more These applications are promising in real world problems, and it is possible to leverage them at construction sites to monitor various activities. The position and orientation of a camera depends spatial degrees of freedom, including three degrees of freedom for position (i.e., X, Y, X, and on six spatial degrees of freedom, including three degrees of freedom for position (i.e., Y, Z), andand
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