This research is to evaluate the feasibility of applying three-dimensional modelling of the close-range photogrammetry in documenting archaeological monuments by using digital photogrammetry image processing software and digital consumer camera. The digital camera used was Nikon D3100, the processing software was (AgiSoft PhotoScan) and (ArcGIS, ArcScene extension). The study area was selected in the centre of Baghdad province by choosing one of the archeological monuments in it, namely the Abbasid alace. A set of camera locations represent the locations of the images, and as a result of the processing, 81 digital images were arranged in a sequence in which the results of this step were verified. The points cloud after processing were 1,082,617 points. Six control points were selected, used as distances constrained. The validity of the fixed location of the points can be ascertained by checking the data. The program provide the error and accuracy for each image, where a total error in the scale bar was 0.005253 meters, a total error of marks points was 0.010957 meters and the accuracy for all six points was 0.005 meters.

This paper studies data manipulation using analytical relative orientation process to determine the exterior orientation parameters. This process can be accomplished by two scenarios; the first one is implemented using collinearity condition while the second one is implemented using coplanarity condition. The final results of both scenarios will be a three dimensional (3D) model and in order to specify the more precise scenario in reconstruction of (3D) models Root Mean Square Error (RMSE) for each scenario was computed. Absolute orientation process was used to transform coordinates of the model system into coordinates of the ground system then (RMSE) for each was computed. The difficulty of obtaining Ground Control Points (GCPs) that covers the photogrammetric project had been overcome by establishing a Portable Control System (PCS) .This (PCS) is a block made of an Aluminum alloy that had been shaped by a (TNC) milling machine to produce plane surfaces on it. The points of intersection of the produced surfaces on the block was labeled or coded and the distances between these points were measured manually by the digital vernier caliper 150mm and micrometer. These points represented control points in the captured images also a specific point within the block was chosen to be the center of this control system and all the remaining points was calculated with reference to it. The calculated (RMSE) for collinearity condition was 1.0212 mm. while for coplanarity condition was 1.0230 mm. So the precision of both models is nearly identical moreover coplanarity condition was more feasible for the requirements of close range photogrammetry.

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Photogrammetry is an approach that can determine the size and the shape of objects through analyzing images recorded by a photographic or video camera. This paper studies measuring and evaluating the structure by using analytical close range photogrammetry method since it is characterized by; high precision, low effort and cost, as well as the possibility of measuring and / or assessing inaccessible places. The objective of the present study is to assess the distortion that occur during projects execution in compare with charts and designs and detect the problem by using (DCRP) with high precision to make a decision to keep or stop working. Several methods can be applied in data processing to determine 3D coordinates of object points for two or more images. To evaluate data processing using four commercial software (LPS, PhotoModeler, Photogrammetric MATLAB and AutoCAD Civil 3D). Also to overcome the difficulty of obtaining Ground Control Points (GCPs) that covers the photogrammetric object, a Portable Control System (PCS) had been established. Using a single camera Canon EOS 500D with image size is (4752 x 3168) pixels to capturing images. Two approaches used for 3D assessment of structure (single model and all models). This study depended structure of directorate of engineering projects at the university of technology this structures represented the study area. The results of analysis processes (3D model) will help the researcher to detect the distortion and suggest the proper solution for dissolve and develop it. The precision obtained from this results show high precision. The results are very promising ranges between (0.18 -1.77) mm.

Image matching and finding correspondence between a stereo image pair is an essential task in digital photogrammetry and computer vision. Stereo images represent the same scene from two different perspectives, and therefore they typically contain a high degree of redundancy. This paper includes an evaluation of implementing manual as well as auto-match between a pair of images that acquired with an overlapped area. Particular target points are selected to be matched manually (22 target points). Auto-matching, based on feature-based matching (FBM) method, has been applied to these target points by using BRISK, FAST, Harris, and MinEigen algorithms. Auto matching is conducted with two main phases: extraction (detection and description) and matching features. The matching techniques used by the prevalent algorithms depend on local point (corner) features. Also, the performance of the algorithms is assessed according to the results obtained from various criteria, such as the number of auto-matched points and the target points that auto-matched. This study aims to determine and evaluate the total root mean square error (RMSE) by comparing coordinates of manual matched target points with those obtained from auto-matching by each of the algorithms. According to the experimental results, the BRISK algorithm gives the higher number of auto-matched points, which equals 2942, while the Harris algorithm gives 378 points representing the lowest number of auto-matched points. All target points are auto-matched with BRISK and FAST algorithms, while 3 and 9 target points only auto-matched with Harris and MinEigen algorithms, respectively. Total RMSE in its minimum value is given by FAST and manual match in the first image, it is 0.002651206 mm, and Harris and manual match provide the minimum value of total RMSE in the second image is 0.002399477 mm.