A novel capacity expansion and recognition acceleration method for dot-dispersing coded targets in photogrammetry

Abstract

In close-range photogrammetry, some improved designs have been developed to increase the coding capacity of Schneider-like concentric circular coded targets (SCTs). But another typical type, Hattori-like dot-dispersing coded targets (HCTs), is rarely expanded. In this paper, we give our detailed solution to recognize a kind of non-public HCTs used in V-STARS. Then an advanced HCTs (AHCTs) with multiple templates and simplified -Invariant is proposed, which achieved capacity expansion and recognition acceleration. First, the cross-ratio invariant is used in template recognition, then the affine transformation is used in decoding. Among the template recognition procedure, multiple templates are designed to expand the coding capacity and a simplified -Invariant is adopted to accelerate the recognition. Experiments with different shooting angles ranging from 0° to 80°, with smaller coded targets, with mixed coded targets of different sizes and with large outdoor scenes are carried out, and the results show that AHCTs can not only preserve the stability of the HCTs, but also achieves the capacity growth of coded targets at a faster recognition rate by 14.6%. The AHCTs with large capacity can be applied in scenario with large size or complex structure, and the acceleration advantage can be considered in real-time dynamic domains.

A Robust and Effective Identification Method for Point-Distributed Coded Targets in Digital Close-Range Photogrammetry

“…Artificial targets are easier to identify than natural features and are suitable for scenes lacking texture features, such as geometric feature detection in industry [5,6]. In addition, in image processing, artificial targets and background information can be easily distinguished, and then the process can be accelerated by filtering out useless background information, which leads to the improvement of accuracy and reliability of measurement [7].…”

“…In close-range photogrammetry and computer vision, many kinds of coded targets have been used, which can be found in [7,15,20]. These types of coded targets include cyber code, circular point-distributed code, circular ring-shaped code, QR code, Chinese character code, etc.…”

“…These types of coded targets include cyber code, circular point-distributed code, circular ring-shaped code, QR code, Chinese character code, etc. The current studies about coded targets aim at how to identify them, how to expand the capacity, how to strengthen the stability, and how to improve the identification accuracy or the positioning precision [7,[18][19][20][21]. Different coded targets differ in processing algorithms.…”

“…by filtering out useless background information, which leads to the improvement of accuracy and reliability of measurement [7].…”

“…The coding capacity of HCTs and GCTs are determined by all possible combinations of three decoding points' distributions. The coding capacity of SCTs, HCTs, and GCTs is 256, 420, and 505, respectively [7]. This means 505 GCTs can be placed in one measurement scenario at once, so the GCTs can be applied in a more complex or larger working environment compared with SCTs and HCTs.…”