Proposed Reconstruction System Research Articles

Active Stereo Vision System with Rotated Structured Light Patterns and Two-Step Denoising Process for Improved Spatial Resolution

Three-dimensional (3D) scanning systems are widely used nowadays for multiple applications. If the object is too large, it is a challenge to provide the detailed characteristics of the scanned object. Improving the spatial resolution of the scanning process is essential in improving the quality of the reconstruction results. Accordingly, this study has proposed an active stereo vision system for improving the spatial resolution. It is well known that the reconstruction quality of stereo vision systems is severely impaired for objects with limited features on their surfaces, thereby, in the present study, an active stereo vision system is proposed in which stripe patterns are projected onto the object surface with various rotation angles in order to facilitate a more accurate and complete detection of the matching points. Notably, the point clouds obtained by the projected patterns at each rotation angle are formed within the same world coordinate system. As a result, the final reconstructed 3D object can be obtained by merging sets of point clouds directly without the need for a prior registration process. In practice, the quality of the reconstruction results obtained by stereo vision systems is commonly degraded by the effects of noise. Therefore, this study also proposes a two-step (multi-decoding pattern and Epipolar line) method for removing the incorrect matching points and improving the reconstruction results accordingly. The performance of the proposed reconstruction system is evaluated experimentally and compared with that of a general active stereo vision system. The experimental results show that the active stereo vision system presented in the present study provides a low-cost, convenient and effective approach for performing the reconstruction of 3D objects with high spatial resolution.

Tomographic Image Reconstruction based on Artificial Neural Network (ANN) Techniques

A new approach for tomographic image reconstruction from projections using Artificial Neural Network (ANN) techniques is presented in this work. The design of the proposed reconstruction system is based on a simple but efficient network architecture, which best utilizes all available input information. Due to the computational complexity, which grows quadratically with the image size, the training phase of the system is characterized by relatively large CPU times. The trained network, on the contrary, is able to provide all necessary information in a quick and efficient way giving results comparable to other time consuming iterative reconstruction algorithms. The performance of the network studied with a large number of software phantoms is directly compared to the well known Algebraic Reconstruction Technique (ART). For a given image and projections size, the role of the hidden layers in the network architecture is examined and the quality dependence of the reconstructed image on the size of the geometrical patterns used in the training phase is also investigated.

3D Reconstruction system for collaborative scanning based on multiple RGB-D cameras

Current 3D reconstruction systems can easily fall into the problems of insufficient memory and low efficiency when reconstructing complex large scenes, and the reconstructed model drifts a lot at the same time. To solve the above problems, a 3D reconstruction system for collaborative scanning based on multiple RGB-D Cameras (Xtion sensors) is presented. In the case of no pose estimation, this paper can obtain the camera pose initial values of the sequence images through the image acquisition platform that has been calibrated in advance, which can well cope with the problems existing in the current 3D reconstruction system. The main innovations of this paper are as follows: (1) In view of the large amount of human-computer interaction required for the current 3D reconstruction system and the high requirements for data acquisition, a collaborative scanning 3D reconstruction system based on multiple Xiton sensors (RGB-D camera) is developed. The proposed system does not require human-computer interaction and can capture high quality image sequences fully automatically; (2) In view of the problems of memory shortage and low efficiency in current 3D reconstruction systems applied to complex large-scale scenes, this paper proposes a system which can obtain camera poses in advance calibration without the need for camera pose estimation; (3) In terms of camera pose optimization, a segmented bundle adjustment method has been presented to obtain the high-precision camera poses. A large number of experiments demonstrate that the proposed system can effectively solve the existing problems. Meanwhile, the proposed reconstruction system can obtain high-precision 3D models suitable for various complex large scenes, which can be widely used in human-computer interaction, virtual reality and other fields.

Solving Double‐Sided Puzzles: Automated Assembly of Torn‐up Banknotes Evidence

Reconstruction of 2D object is a problem concerning many different fields such as forensics science, archiving, and banking. In the literature, it is considered as one-sided puzzle problem. But this study handles torn banknotes as a double-sided puzzle problem for the first time. In addition to that, a new dataset (ToB) is created for solving this problem. A selection approach based on the Borda count method is adopted in order to make the right decision as to which keypoint-based method is to be used in the proposed reconstruction system. The selection approach was determined the Accelerated-KAZE (AKAZE) as the most successful keypoint-based method. This study also proposes new measures determining the success ratio of the reconstructed banknotes and calculating their loss ratio. When the torn banknotes were reconstructed with the AKAZE-based reconstruction system, the average success rate was calculated as 95.55% by the proposed metric.

Simultaneous measurement of 2-dimensional H2O concentration and temperature distribution in premixed methane/air flame using TDLAS-based tomography technology

An innovative tomographic method using tunable diode laser absorption spectroscopy (TDLAS) and algebraic reconstruction technique (ART) is presented in this paper for detecting two-dimensional distribution of H2O concentration and temperature in a premixed flame. The collimated laser beam emitted from a low cost diode laser module was delicately split into 24 sub-beams passing through the flame from different angles and the acquired laser absorption signals were used to retrieve flame temperature and H2O concentration simultaneously. The efficiency of the proposed reconstruction system and the effect of measurement noise were numerically evaluated. The temperature and H2O concentration in flat methane/air premixed flames under three different equivalence ratios were experimentally measured and reconstruction results were compared with model calculations. Numerical assessments indicate that the TDLAS tomographic system is capable for temperature and H2O concentration profiles detecting even the noise strength reaches 3% of absorption signal. Experimental results under different combustion conditions are well demonstrated along the vertical direction and the distribution profiles are in good agreement with model calculation. The proposed method exhibits great potential for 2-D or 3-D combustion diagnostics including non-uniform flames.