Virtual viewpoint three-dimensional panorama

Conventional panoramic images are known to provide for an enhanced field of view in which the scene always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual viewpoint creation to generate different panoramic images of the same scene with three-dimensional component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of panoramic images

Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

FPGA-based multi-view stereo system with flexible measurement setup

In recent years, stereoscopic image processing algorithms have gained importance for a variety of applications. To capture larger measurement volumes, multiple stereo systems are combined into a multi-view stereo (MVS) system. To reduce the amount of data and the data rate, calculation steps close to the sensors are outsourced to Field Programmable Gate Arrays (FPGAs) as upstream computing units. The calculation steps include lens distortion correction, rectification and stereo matching. In this paper a FPGA-based MVS system with flexible camera arrangement and partly overlapping field of view is presented. The system consists of four FPGA-based passive stereoscopic systems (Xilinx Zynq-7000 7020 SoC, EV76C570 CMOS sensor) and a downstream processing unit (Zynq Ultrascale ZU9EG SoC). This synchronizes the sensor near processing modules and receives the disparity maps with corresponding left camera image via HDMI. The subsequent computing unit calculates a coherent 3D point cloud. Our developed FPGA-based 3D measurement system captures a large measurement volume at 24 fps by combining a multiple view with eight cameras (using Semi-Global Matching for an image size of 640 px × 460 px, up to 256 px disparity range and with aggregated costs over 4 directions). The capabilities and limitation of the system are shown by an application example with optical non-cooperative surface

AN ALGORITHM FOR RECONSTRUCTING THREE-DIMENSIONAL IMAGES FROM OVERLAPPING TWO-DIMENSIONAL INTENSITY MEASUREMENTS WITH RELAXED CAMERA POSITIONING REQUIREMENTS, WITH APPLICATION TO ADDITIVE MANUFACTURING

Cameras are everywhere for security purposes and there are often many cameras installed close to each other to cover areas of interest, such as airport passenger terminals. These systems are often designed to have overlapping fields of view to provide different aspects of the scene to review when, for example, law enforcement issues arise. However, these cameras are rarely, if ever positioned in a way that would be conducive to conventional stereo image processing. To address this, issue an algorithm was developed to rectify images measured under such conditions, and then perform stereo image reconstruction. The initial experiments described here were set up using two scientific cameras to capture overlapping images in various cameras positons. The results showed that the algorithm was accurately reconstructing the three-dimensional (3-D) surface locations of the input objects. During the research an opportunity arose to further develop and test the algorithms for the problem of monitoring the fabrication process inside a 3-D printer. The geometry of 3-D printers prevents the location of cameras in the conventional stereo imaging geometry, making the algorithms described above seem like an attractive solution to this problem. The emphasis in 3-D printing on using extremely low cost components and open source software, and the need to develop the means of comparing observed progress in the fabrication process to a model of the device being fabricated posed additional development challenges. Inside the 3-D printer the algorithm was applied using two scientific cameras to detect the errors during the printing of the low-cost open-source RepRap style 3-D printer developed by the Michigan Tech’s Open Sustainability Technology Lab. An algorithm to detect errors in the shape of a device being fabricated using only one camera was also developed. The results show that a 3-D reconstruction algorithm can be used to accurately detect the 3-D printing errors. The initial development of the algorithm was in MATLAB. The cost of the MATLAB software might prevent it from being used by open-source communities. Thus, the algorithm was ported to Python and made open-source for everyone to use and customize. To reduce the cost, the commonly used and widely available inexpensive webcams were also used instead of the expensive scientific cameras. In order to detect errors around the printed part, six webcams were used, so there were 3 pairs of webcams and each pair were 120 degrees apart. The results indicated that the algorithms are precisely detect the 3-D printing errors around the printed part in shape and size aspects. With this low-cost and open-source approach, the algorithms are ready for wide range of use and applications

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation

Three dimensional information estimation and tracking for moving objects detection using two cameras framework

Calibration, matching and tracking are major concerns to obtain 3D information consisting of depth, direction and velocity. In finding depth, camera parameters and matched points are two necessary inputs. Depth, direction and matched points can be achieved accurately if cameras are well calibrated using manual traditional calibration. However, most of the manual traditional calibration methods are inconvenient to use because markers or real size of an object in the real world must be provided or known. Self-calibration can solve the traditional calibration limitation, but not on depth and matched points. Other approaches attempted to match corresponding object using 2D visual information without calibration, but they suffer low matching accuracy under huge perspective distortion. This research focuses on achieving 3D information using self-calibrated tracking system. In this system, matching and tracking are done under self-calibrated condition. There are three contributions introduced in this research to achieve the objectives. Firstly, orientation correction is introduced to obtain better relationship matrices for matching purpose during tracking. Secondly, after having relationship matrices another post-processing method, which is status based matching, is introduced for improving object matching result. This proposed matching algorithm is able to achieve almost 90% of matching rate. Depth is estimated after the status based matching. Thirdly, tracking is done based on x-y coordinates and the estimated depth under self-calibrated condition. Results show that the proposed self-calibrated tracking system successfully differentiates the location of objects even under occlusion in the field of view, and is able to determine the direction and the velocity of multiple moving objects