Computer Vision Photogrammetry Research Articles

Orbital Prosthesis Rehabilitation in Biomedical Engineering by Means of Computer Vision-Photogrammetry and 3D Prototyping

Eye loss may be caused as a result of eye trauma, accidents, or malignant tumors, which leads the patient to undergo surgery to remove the damaged parts. This research examines the potential of computer vision represented by Structure from Motion (SfM) photogrammetry in fabricating the orbital prosthesis as a noninvasive and low-cost technique. A low-cost camera was used to collect the data towards extracting the dense 3D data of the patient facial features following Structure from Motion-Multi View Stereo (SfM-MVS) algorithms. To restore the defective orbital, a Reverse Engineering (RE) based approach has been applied using the similarity RE algorithms based on the opposite healthy eye to rehabilitate the defected orbital precisely. Following quality assurance and best-fitting statistical analysis, the digital model of the restored eye was converted into a physical model using 3D prototyping. This is later used to fabricate the mold for casting medical-grade silicone to obtain the final orbital prosthesis. The results show the power of SfM photogrammetry by offering a high-accuracy model of 0.048 mm and 0.186 mm relative errors acquired in the horizontal and vertical directions, respectively. These results boost the RE implementation in medicine to reconstruct the patient's damaged eye by mirroring the image of the healthy eye using RE algorithms. Therefore, the margin matching results claim perfect data capture settings and successful data processing workflow as designed in the first place. Consequently, one can claim this approach effectively rehabilitates maxillofacial deformities as an alternative to invasive restoration approaches. The presented approach provided a low-cost and safe workflow that avoids the patient the risks of exposure to harmful rays or magnetic fields available in other sensors.

The Fahrdorf wreck: another large 12th-century cargo ship in the Schlei, northern Germany

In 2014 an accidentally raised construction timber turned out to be a part of a medieval shipwreck resting in the murky waters of the Schlei inlet of the Baltic Sea in northern Germany. The Study Group for Maritime and Limnic Archaeology (AMLA) of the Christian-Albrechts University (CAU) at Kiel has examined and documented the remains using computer vision photogrammetry over recent years. Based on these campaigns it has been possible to date the wreck and determine its function as a large cargo sailing vessel, which testifies to the importance of the Schlei as a trading route for the cities of Hedeby and Schleswig.

From features to fingerprints: A general diagnostic framework for anthropogenic geomorphology

Human societies have been reshaping the geomorphology of landscapes for thousands of years, producing anthropogenic geomorphic features ranging from earthworks and reservoirs to settlements, roads, canals, ditches and plough furrows that have distinct characteristics compared with landforms produced by natural processes. Physical geographers have long recognized the widespread importance of these features in altering landforms and geomorphic processes, including hydrologic flows and stores, to processes of soil erosion and deposition. In many of the same landscapes, archaeologists have also utilized anthropogenic geomorphic features to detect and analyse human societal activities, including symbolic formations, agricultural systems, settlement patterns and trade networks. This paper provides a general framework aimed at integrating geophysical and archaeological approaches to observing, identifying and interpreting the full range of anthropogenic geomorphic features based on their structure and functioning, both individually and as components of landscape-scale management strategies by different societies, or “sociocultural fingerprints”. We then couple this framework with new algorithms developed to detect anthropogenic geomorphic features using precisely detailed three-dimensional reconstructions of landscape surface structure derived from LiDAR and computer vision photogrammetry. Human societies are now transforming the geomorphology of landscapes at increasing rates and scales across the globe. To understand the causes and consequences of these transformations and contribute to building sustainable futures, the science of physical geography must advance towards empirical and theoretical frameworks that integrate the natural and sociocultural forces that are now the main shapers of Earth’s surface processes.

Photogrammetric texture mapping: A method for increasing the Fidelity of 3D models of cultural heritage materials

Accurately recording information is the single most important stage of an archaeological project. The biggest technological improvement to documentation techniques in the last 15 years has been the spread of various 3D digitization technologies, such as computer vision photogrammetry and 3D laser scanning. These technologies have allowed archaeologists to quickly and accurately capture and reconstruct the geometry and colors of the subjects being studied. Each of the various methods for 3D digitization of cultural heritage materials has advantages and disadvantages, such as processing speed, cost of equipment, and the accuracy of the captured data. Laser scanned data is among the most accurate geometrical data available in modern scanning techniques, but it lacks in its ability to accurately capture textures and diagnostic coloration information. Photogrammetric data produces highly detailed photographic textures on models, but the geometric data tends to be of a lower definition than the laser scanned data. In this paper, the authors discuss a new methodology that combines the advantages of computer vision photogrammetry with those of laser scanning by applying the photographic textures produced with photogrammetry to the geometric data obtained from laser scanning. This method allows archaeologists to achieve the best possible fidelity 3D models for interpretation and study.

COMPUTER VISION PHOTOGRAMMETRY FOR UNDERWATER ARCHAEOLOGICAL SITE RECORDING IN A LOW-VISIBILITY ENVIRONMENT

Abstract. Computer Vision Photogrammetry allows archaeologists to accurately record underwater sites in three dimensions using simple twodimensional picture or video sequences, automatically processed in dedicated software. In this article, I share my experience in working with one such software package, namely PhotoScan, to record a Dutch shipwreck site. In order to demonstrate the method’s reliability and flexibility, the site in question is reconstructed from simple GoPro footage, captured in low-visibility conditions. Based on the results of this case study, Computer Vision Photogrammetry compares very favourably to manual recording methods both in recording efficiency, and in the quality of the final results. In a final section, the significance of Computer Vision Photogrammetry is then assessed from a historical perspective, by placing the current research in the wider context of about half a century of successful use of Analytical and later Digital photogrammetry in the field of underwater archaeology. I conclude that while photogrammetry has been used in our discipline for several decades now, for various reasons the method was only ever used by a relatively small percentage of projects. This is likely to change in the near future since, compared to the ‘traditional’ photogrammetry approaches employed in the past, today Computer Vision Photogrammetry is easier to use, more reliable and more affordable than ever before, while at the same time producing more accurate and more detailed three-dimensional results.