Integrating photogrammetric techniques with scene analysis and machine vision. Proceedings of a meeting held in Orlando, FL, USA, April 14-15, 1993 (original) (raw)
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2014
Recent years have seen significant improvements in the performance of automatic cartographic feature extraction (CFE) systems. Early systems, painstakingly tweaked to work in a very limited fashion on small images, have given way to sys-tems which produce credible results in situations representative of production environments. While no existing automatic system is yet ready for incorporation into a production environment, significant strides have been made toward this goal. Semi-automated systems, with significant man-in-the-loop capabilities, continue to be developed by photogrammetric workstation vendors. However, a fundamental requirement for system development, and an absolute prerequisite for production applications, is the rigorous evaluation of automated system performance. Indeed, without meaningful evaluation, we can hardly be said to be doing science. Rigorous evaluation requires the definition of a set of metrics, relevant to user requirements and meaningful in terms of ...
A Comparison between" old and new" feature extraction and matching techniques in photogrammetry
ABSTRACT The development of new photogrammetric systems has changed the user demand. At present, the images acquired by Unmanned Aerial Vehicles (UAV) and Mobile Mapping Technologies (MMT) are far from the normal condition and they also still need to reach reliable results for bad-textured images. The interest point operators and image matching techniques that have traditionally used in Photogrammetry are unable to give good results for these applications. The algorithms that are used in Computer Vision (CV) community could instead assure good results, in terms of number of matched points. For this reason, a comparison analysis between the SIFT operator [1] and traditional photogrammetric feature extraction and matching techniques has been carried out. Many experimental tests on UAV aerial images and MMS terrestrial acquisitions with high geometric distortions (rotation, 3D viewpoint, scale) have been performed, in order to evaluate the reliability of SIFT for automatic homologous point extraction.
An Approach for Semi-Automatic Extraction of Features from Aerial Photographs
Aerial photographs have been evaluated manually by the operators for the extraction of the vector data to produce photogrammetric maps. In the recent years the developments, in the photogrammetry, provides to perform these extraction processes automatically. In this study, a new semi-automatic feature extraction approach, based on the segmentation of the images using color-differences of the pixels and the propogation of fronts by the Level Set algorithms, is developed. An object-oriented application software is also developed to test the capabilities of the developed method. Some semi-automatic feature extraction applications are made by the help of the developed software using 1:4000 and 1:35000 scale black/white aerial photographs for determining the capabilities of this method. The results of the tests show that this method can be used for the extraction of some features from aerial photographs for GIS and the production of the photogrammetric maps.
PHOTOGRAMMETRIC AUTOMATION: IS IT WORTH
2016
This paper focuses on the evaluation of automated image based techniques which are used lately in order to produce three dimensional digital models of cultural heritage objects. This implies a doubt as to how accurate and reliable are the products of these automated algorithms and how efficient they are for providing the necessary 3D material for the virtual environments. The implementation of this innovative approach has gradually become very popular in the field of Cultural Heritage during the last five years. Non-specialists found a way to easily produce 3D reconstructions just by taking a few images and using Structure-from-Motion and Multiview Stereo algorithms implemented by commercial or open source software. However, this fact led to debatable results, as a lot of ambiguities are lurking hidden in the "happiness" of automation. In order to assess the metric performance of these algorithms an innovative metric evaluation strategy has been designed. A very accurately measured test field, set up mainly for calibrating digital cameras, was used as an object of well-known ground truth. It was established that the accuracy of the resulting 3D reconstruction depends on the spatial analysis and the general quality of the original digital images, on the careful selection of the parameters provided by the software, the properties of the object itself and the computing power available.