Effective Volumetric Feature Modeling and Coarse Correspondence via Improved 3DSIFT and Spectral Matching

This paper presents a nonrigid coarse correspondence computation algorithm for volumetric images. Our matching algorithm first extracts then correlates image features based on a revised and improved 3DSIFT (I3DSIFT) algorithm. With a scale-related keypoint reorientation and descriptor construction, this feature correlation is less sensitive to image rotation and scaling. Then, we present an improved spectral matching (ISM) algorithm on correlated features to obtain a one-to-one mapping between corresponded features. One can effectively extend this feature correspondence to dense correspondence between volume images. Our algorithm can benefit nonrigid volumetric image registration in many tasks such as motion modeling in medical image analysis and processing

Efficient Dense 3D Reconstruction Using Image Pairs

The 3D reconstruction of a scene from 2D images is an important topic in the _x000C_eld of Computer Vision due to the high demand in various applications such as gaming, animations, face recognition, parts inspections, etc. The accuracy of a 3D reconstruction is highly dependent on the accuracy of the correspondence matching between the images. For the purpose of high accuracy of 3D reconstruction system using just two images of the scene, it is important to _x000C_nd accurate correspondence between the image pairs. In this thesis, we implement an accurate 3D reconstruction system from two images of the scene at di_x000B_erent orientation using a normal digital camera. We use epipolar geometry to improvise the performance of the initial coarse correspondence matches between the images. Finally we calculate the reprojection error of the 3D reconstruction system before and after re_x000C_ning the correspondence matches using the epipolar geometry and compare the performance between them. Even though many feature-based correspondence matching techniques provide robust matching required for 3D reconstruction, it gives only coarse correspondence matching between the images. This is not su_x000E_cient to reconstruct the detailed 3D structure of the objects. Therefore we use our improvised image matching to calculate the camera parameters and implement dense image matching using thin-plate spline interpolation, which interpolates the surface based on the initial control points obtained from coarse correspondence matches. Since the thin-plate spline interpolates highly dense points from a very few control points, the correspondence mapping between the images are not accurate. We propose a new method to improve the performance of the dense image matching using epipolar geometry and intensity based thin-plate spline interpolation. We apply the proposed method for 3D reconstruction using two images. Finally, we develop systematic evaluation for our dense 3D reconstruction system and discuss the results

Image Processing Applications in Real Life: 2D Fragmented Image and Document Reassembly and Frequency Division Multiplexed Imaging

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied. In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the Fourier domain. Finally, a Texas Instruments digital micromirror device (DMD) based implementation of FDMI is presented and results are shown. Chapter 2 discusses the problem of image reassembly which is to restore an image back to its original form from its pieces after it has been fragmented due to different destructive reasons. We propose an efficient algorithm for 2D image fragment reassembly problem based on solving a variation of Longest Common Subsequence (LCS) problem. Our processing pipeline has three steps. First, the boundary of each fragment is extracted automatically; second, a novel boundary matching is performed by solving LCS to identify the best possible adjacency relationship among image fragment pairs; finally, a multi-piece global alignment is used to filter out incorrect pairwise matches and compose the final image. We perform experiments on complicated image fragment datasets and compare our results with existing methods to show the improved efficiency and robustness of our method. The problem of reassembling a hand-torn or machine-shredded document back to its original form is another useful version of the image reassembly problem. Reassembling a shredded document is different from reassembling an ordinary image because the geometric shape of fragments do not carry a lot of valuable information if the document has been machine-shredded rather than hand-torn. On the other hand, matching words and context can be used as an additional tool to help improve the task of reassembly. In the final chapter, document reassembly problem has been addressed through solving a graph optimization problem

Heterogeneous volumetric data mapping and its medical applications

With the advance of data acquisition techniques, massive solid geometries are being collected routinely in scientific tasks, these complex and unstructured data need to be effectively correlated for various processing and analysis. Volumetric mapping solves bijective low-distortion correspondence between/among 3D geometric data, and can serve as an important preprocessing step in many tasks in compute-aided design and analysis, industrial manufacturing, medical image analysis, to name a few. This dissertation studied two important volumetric mapping problems: the mapping of heterogeneous volumes (with nonuniform inner structures/layers) and the mapping of sequential dynamic volumes. To effectively handle heterogeneous volumes, first, we studied the feature-aligned harmonic volumetric mapping. Compared to previous harmonic mapping, it supports the point, curve, and iso-surface alignment, which are important low-dimensional structures in heterogeneous volumetric data. Second, we proposed a biharmonic model for volumetric mapping. Unlike the conventional harmonic volumetric mapping that only supports positional continuity on the boundary, this new model allows us to have higher order continuity $C^1$ along the boundary surface. This suggests a potential model to solve the volumetric mapping of complex and big geometries through divide-and-conquer. We also studied the medical applications of our volumetric mapping in lung tumor respiratory motion modeling. We were building an effective digital platform for lung tumor radiotherapy based on effective volumetric CT/MRI image matching and analysis. We developed and integrated in this platform a set of geometric/image processing techniques including advanced image segmentation, finite element meshing, volumetric registration and interpolation. The lung organ/tumor and surrounding tissues are treated as a heterogeneous region and a dynamic 4D registration framework is developed for lung tumor motion modeling and tracking. Compared to the previous 3D pairwise registration, our new 4D parameterization model leads to a significantly improved registration accuracy. The constructed deforming model can hence approximate the deformation of the tissues and tumor

A symmetric 4D registration algorithm for respiratory motion modeling.

We propose an effective 4D image registration algorithm for dynamic volumetric lung images. The registration will construct a deforming 3D model with continuous trajectory and smooth spatial deformation, and the model interpolates the interested region in the 4D (3D+T) CT images. The resultant non-rigid transformation is represented using two 4D B-spline functions, indicating a forward and an inverse 4D parameterization respectively. The registration process solves these two functions by minimizing an objective function that penalizes intensity matching error, feature alignment error, spatial and temporal non-smoothness, and inverse inconsistency. We test our algorithm for respiratory motion estimation on public benchmarks and on clinic lung CT data. The experimental results demonstrate the efficacy of our algorithm