Clinical Research of Orthopedic Trauma Based on Computer 3D Image Analysis

In order to improve the clinical research effect of orthopedic trauma, this paper applies computer 3D image analysis technology to the clinical research of orthopedic trauma and proposes the BOS technology based on FFT phase extraction. The background image in this technique is a “cosine blob” background image. Moreover, this technology uses the FFT phase extraction method to process this background image to extract the image point displacement. The BOS technology based on FFT phase extraction does not need to select a diagnostic window. Finally, this paper combines computer 3D image analysis technology to build an intelligent system. According to the experimental research results, the clinical analysis system of orthopedic trauma based on computer 3D image analysis proposed in this paper can play an important role in the clinical diagnosis and treatment of orthopedic trauma and improve the diagnosis and treatment effect of orthopedic trauma.

User-guided Image Editing using Radial Basis Functions

<p>Good image editing tools that modify colors of specified image regions or deform the depicted objects have always been an important part of graphics editors. Manual approaches to this task are too time-consuming, while fully automatic methods are not robust enough. Thus, the ideal editing method should include a combination of manual and automated components. This thesis shows that radial basis functions provide a suitable “engine” for two common image editing problems, where interactivity requires both reasonable performance and fast training. There are many freeform image deformation methods to be used, each having advantages and disadvantages. This thesis explores the use of radial basis functions for freeform image deformation and compares it to a standard approach that uses B-spline warping. Edit propagation is a promising user-guided color editing technique, which, instead of requiring precise selection of the region being edited, accepts color edits as a few brush strokes over an image region and then propagates these edits to the regions with similar appearance. This thesis focuses on an approach to edit propagation, which considers user input as an incomplete set of values of an intended edit function. The approach interpolates between the user input values using radial basis functions to find the edit function for the whole image. While the existing approach applies the user-specified edits to all the regions with similar colors, this thesis presents an extension that propagates the edits more selectively. In addition to color information of each image point, it also takes the surrounding texture into account and better distinguishes different objects, giving the algorithm more information about the user-specified region and making the edit propagation more precise.</p>

User-guided Image Editing using Radial Basis Functions

<p>Good image editing tools that modify colors of specified image regions or deform the depicted objects have always been an important part of graphics editors. Manual approaches to this task are too time-consuming, while fully automatic methods are not robust enough. Thus, the ideal editing method should include a combination of manual and automated components. This thesis shows that radial basis functions provide a suitable “engine” for two common image editing problems, where interactivity requires both reasonable performance and fast training. There are many freeform image deformation methods to be used, each having advantages and disadvantages. This thesis explores the use of radial basis functions for freeform image deformation and compares it to a standard approach that uses B-spline warping. Edit propagation is a promising user-guided color editing technique, which, instead of requiring precise selection of the region being edited, accepts color edits as a few brush strokes over an image region and then propagates these edits to the regions with similar appearance. This thesis focuses on an approach to edit propagation, which considers user input as an incomplete set of values of an intended edit function. The approach interpolates between the user input values using radial basis functions to find the edit function for the whole image. While the existing approach applies the user-specified edits to all the regions with similar colors, this thesis presents an extension that propagates the edits more selectively. In addition to color information of each image point, it also takes the surrounding texture into account and better distinguishes different objects, giving the algorithm more information about the user-specified region and making the edit propagation more precise.</p>

A Method of Image Quality Assessment for Text Recognition on Camera-Captured and Projectively Distorted Documents

In this paper, we consider the problem of identity document recognition in images captured with a mobile device camera. A high level of projective distortion leads to poor quality of the restored text images and, hence, to unreliable recognition results. We propose a novel, theoretically based method for estimating the projective distortion level at a restored image point. On this basis, we suggest a new method of binary quality estimation of projectively restored field images. The method analyzes the projective homography only and does not depend on the image size. The text font and height of an evaluated field are assumed to be predefined in the document template. This information is used to estimate the maximum level of distortion acceptable for recognition. The method was tested on a dataset of synthetically distorted field images. Synthetic images were created based on document template images from the publicly available dataset MIDV-2019. In the experiments, the method shows stable predictive values for different strings of one font and height. When used as a pre-recognition rejection method, it demonstrates a positive predictive value of 86.7% and a negative predictive value of 64.1% on the synthetic dataset. A comparison with other geometric quality assessment methods shows the superiority of our approach.

A Novel Method for Density Analysis of Repaired Point Cloud with Holes Based on Image Data

Repairing point cloud holes has become an important problem in the research of 3D laser point cloud data, which ensures the integrity and improves the precision of point cloud data. However, for the point cloud data with non-characteristic holes, the boundary data of point cloud holes cannot be used for repairing. Therefore, this paper introduces photogrammetry technology and analyzes the density of the image point cloud data with the highest precision. The 3D laser point cloud data are first formed into hole data with sharp features. The image data are calculated into six density image point cloud data. Next, the barycenterization Bursa model is used to fine-register the two types of data and to delete the overlapping regions. Then, the cross-section is used to evaluate the precision of the combined point cloud data to get the optimal density. A three-dimensional model is constructed for this data and the original point cloud data, respectively and the surface area method and the deviation method are used to compare them. The experimental results show that the ratio of the areas is less than 0.5%, and the maximum standard deviation is 0.0036 m and the minimum is 0.0015 m.

Holographic Near-Eye 3D Display Method Based on Large-Size Hologram

In this paper, we propose a holographic near-eye 3D display method based on large-size computer-generated hologram (CGH). The reconstructed image with a large viewing angle is obtained by using a time multiplexing and spatial tiling system. The large-size CGHs are generated and they record the information of the 3D object from different angles. The CGHs are reproduced at different moments. For a certain reconstructed moment, three spatial light modulators (SLMs) spatially spliced into a linear structure are used to load a single CGH. The diffraction boundary angle of the reconstructed light forming each image point is equal to the maximum diffraction angle of the SLM, so the viewing angle of the image generated by the CGH is enlarged. For different CGHs, the incident angle of reconstructed light is changed. Through time multiplexing, the reconstructed images of the CGHs are combined into a reconstructed image whose viewing angle is further enlarged. Due to the large viewing angle of the reconstructed image, the proposed method has unique advantages in near-eye display. The feasibility of the proposed method is proved by experimental results.

Case Study: Improving the quality of the seismic reflection image for a Fujian mineral exploration data set with offset-domain common-image gathers

Seismic reflection is a proven and effective method commonly used during the exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we employed the attribute migration method. This worked by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponded to each image point was obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function was proposed to map every image point to a certain range of offsets. This helped improve the signal-to-noise ratio, which was especially important in handing low quality seismic data obtained during mineral exploration. Residual velocity analysis was applied to these gathers to update the velocity model and improve image quality. The offset-domain common-image gathers were also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, the proposed procedure achieved an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.

3D Reconstruction of Exposed Underground Utilities Using Photogrammetric Methods

This thesis addresses the topic of three-dimensional (3D) reconstruction of exposed underground utilities using photogrammetric methods. Research on this topic is mainly motivated by the need for improved information on the location of underground utilities and, thus, to provide reliable information for the management of buried assets. In this thesis, a system of photogrammetric software programs is developed for 3D reconstruction of underground utilities. Camera calibration programs are used for computing interior elements and lens distortion coefficients of digital cameras and saving them in a lookup table (LUT). The accuracy of calibrated image coordinates satisfies the photogrammetric processing demand. An automatic image point detection method is proposed and achieved in these programs. External orientation programs are used for calculating exterior elements of the digital images. Based on geographic information system (GIS) and global positioning system (GPS) techniques, a new ground control points (GCPs) collection method is proposed and implemented in these programs. A 3D reconstruction program provides corresponding functions to obtain and edit 3D information of underground utilities. Epipolar lines are employed as an assisting tool that helps operators easily find homologous points from different digital images. The study results indicate that photogrammetric methods for reconstructing 3D information of underground utilities are effective and low cost.