10845590
Developing structural constraints for accurate registration of overlapping range images
Abstract Automatic image registration is an attractive and unresolved problem in the machine vision literature. This paper presents two novel structural constraints, namely proximity and closeness constraints, that improve both the accuracy and robustness of an existing motion consistency based algorithm (GICP) for automatic image registration. It also defines the conditions when such constraints are fired at specific points of the registration of two overlapping range images. The proximity constraint says that neighbouring points should be neighbouring before and after a rigid motion. The closeness constraint implements a local closest point search from the second image to the first image. While the GICP algorithm uses an exhaustive search for the closest points, in this paper, we employ the optimised k–d tree data structure to accelerate the closest point search. A large number of experiments based on real range images demonstrate that the combination of rigid motion constraints with these novel proximity and closeness constraints leads to a more accurate and robust evaluation of possible correspondences leading, in turn, to more accurate, robust and efficient automatic image registration results.
36759557
Geometrical analysis of two sets of 3D correspondence data patterns
Given two sets of image correspondence data, the analysis of the corresponding rigid body transformation that accurately describes the object's motion in 3D space is a fundamental problem in image understanding. While many methods have been put forward to analyse 3D transformations, such methods do not make use of the vector distance between feature points and angular information as constraints to analyse transformation parameters. Current methods and algorithms have also suffered from the problems of lack of efficiency, sensitivity to noise, and multiplicity of solutions. In this paper we present a novel geometrical analysis of rigid body transformations and a novel algorithm to calibrate transformation parameters based on image correspondence. The algorithm is validated through experimental calibration and a comparison is made with calibration implemented by the least squares method. We demonstrate that the proposed algorithm works well in the presence of noise and that its performance is in general superior to algorithms based on the least squares method.
18654744
Evaluating collinearity constraint for automatic range image registration
While most of the existing range image registration algorithms either have to extract and match structural (geometric or optical) features or have to estimate the motion parameters of interest from outliers corrupted point correspondence data for the elimination of false matches in the process of image registration, the registration error and the collinearity error derived directly from the traditional closest point criterion are also capable of doing the same job. However, the latter has an advantage of easy implementation. The purpose of this paper is to investigate which definition of collinearity is more accurate and stable in eliminating false matches inevitably introduced by the closest point criterion. The experiments based on real images show the advantages and disadvantages of different definitions of collinearity.
26921590
Evaluating structural constraints for accurate range image registration
New technological developments in optics and electronics are rendering laser scanning systems cheaper and more accurate. Such systems can directly capture depth information from objects simplifying range image analysis and enlarging their applications scope. Accurate image registration algorithms represent a pivotal aspect of range image analysis which still require substantial improvements. While a large number of algorithms have been proposed, it is very important to compare the performance of different registration algorithms so that good algorithms can be selected for real applications. Unfortunately, most of these algorithms are iterative with a number of parameters initialised by users. The main disadvantages of these methods are that these parameters are often context dependent. Thus, while aiming to develop automatic registration algorithms instead of comparing the registration algorithms themselves, we compare the structural constraints based on a single registration algorithm(GICP). A large number of experiments using real range images demonstrate their effectiveness on improving the GICP algorithm.
9293300
Automatic registration of overlapping 3D point clouds using closest points
Abstract While the SoftAssign algorithm imposes a two-way constraint embedded into the deterministic annealing scheme and the EMICP algorithm imposes a one-way constraint, they represent the state of the art technique for the automatic registration of overlapping free form shapes. They both have a time complexity of O ( n 2 ). While the former has a space complexity also of O ( n 2 ), the latter has a space complexity of O ( n ). The heavy demand for computation and storage memory renders either the SoftAssign or EMICP algorithm to hardly operate on whole shapes with thousands of points. In this case, they often have to reduce the number of points to an order of 100s on the free form shapes to be registered. This paper proposes using closest points in conjunction with either the one-way or two-way constraint for the automatic registration of overlapping 3D point clouds and thus, combining the accuracy of both the SoftAssign and EMICP algorithms with the efficiency of the traditional ICP algorithm. A comparative study based on both synthetic data and real images has shown that the proposed algorithm does not significantly sacrifice accuracy and stability of either the SoftAssign or EMICP algorithm, but gains remarkable efficiency of the traditional ICP algorithm for the automatic registration of overlapping 3D point clouds. Since, the proposed algorithm is of general use and has an advantage of easy implementation, it is likely to become in the future a benchmark for the automatic registration of overlapping 3D point clouds.
14532857
Projecting registration error for accurate registration of overlapping range images
Abstract In this paper, we propose a novel algorithm for the automatic registration of two overlapping range images. Since it is relatively difficult to compare the registration errors of different point matches, we project them onto a virtual image plane for more accurate comparison using the classical pin-hole perspective projection camera model. While the traditional ICP algorithm is more interested in the points in the second image close to the sphere centred at the transformed point, the novel algorithm is more interested in the points in the second image as collinear as possible to the transformed point. The novel algorithm then extracts useful information from both the registration error and projected error histograms for the elimination of false matches without any feature extraction, image segmentation or the requirement of motion estimation from outliers corrupted data and, thus, has an advantage of easy implementation. A comparative study based on real images captured under typical imaging conditions has shown that the novel algorithm produces good registration results.
11221345
Multiresolution free form object modeling with point sampled geometry
In this paper an efficient framework for the creation of 3D digital content with point sampled geometry is proposed. A new hierarchy of shape representations with three levels is adopted in this framework. Based on this new hierarchical shape representation, the proposed framework offers concise integration of various volumetric- and surface-based modeling techniques, such as Boolean operation, offset, blending, free-form deformation, parameterization and texture mapping, and thus simplifies the complete modeling process. Previously to achieve the same goal, several separated algorithms had to be used independently with inconsistent volumetric and surface representations of the free-form object. Both graphics and industrial applications are presented to demonstrate the effectiveness and efficiency of the proposed framework.
9356286
A design of overlay anonymous multicast protocol
Multicast services are demanded by a variety of applications. Many applications require anonymity during their communication. However, there has been very little work on anonymous multicasting and such services are not available yet. Since there are fundamental differences between multicast and unicast, the solutions proposed for anonymity in unicast communications cannot be directly applied to multicast applications. In this paper, we define the anonymous multicast system, and propose a mutual anonymous multicast (MAM) protocol including the design of a unicast mutual anonymity protocol and construction and optimization of an anonymous multicast tree. MAM is self organizing and completely distributed. We define the attack model in an anonymous multicast system and analyze the anonymity degree. We also evaluate the performance of MAM by simulations
1966938
Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images
This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.
1265218
Solving Regular Tree Grammar Based Constraints
This paper describes the precise specification, design, analysis, implementation, and measurements of an efficient algorithm for solving regular tree grammar based constraints. The particular constraints are for dead-code elimination on recursive data, but the method used for the algorithm design and complexity analysis is general and applies to other program analysis problems as well. The method is centered around Paige's finite differencing, i.e., computing expensive set expressions incrementally, and allows the algorithm to be derived and analyzed formally and implemented easily. We propose higher-level transformations that make the derived algorithm concise and allow its complexity to be analyzed accurately. Although a rough analysis shows that the worst-case time complexity is cubic in program size, an accurate analysis shows that it is linear in the number of live program points and in other parameters, including mainly the arity of data constructors and the number of selector applications into whose arguments the value constructed at a program point might flow. These parameters explain the performance of the analysis in practice. Our implementation also runs two to ten times as fast as a previous implementation of an informally designed algorithm.
31965236
Iterate, Incrementalize, and Implement: A systematic approach to efficiency improvement and guarantees
People may more easily specify what they would like to compute if not concerned with how to compute efficiently. Given descriptions that may lead to inefficient computations, can we systematically obtain efficient algorithms and implementations? Can we further have efficiency guarantees for them? We give an overview of a general and systematic method for achieving efficiency improvement and providing efficiency guarantees. The method is centered around incrementalization: making computation proceed repeatedly on small incremented inputs and effectively maintaining and using previously computed results. The method has three steps: iterate, incrementalize, and implement. Step 1 determines a minimum step to be taken repeatedly. Step 2 makes each step compute incrementally, particularly by using and maintaining and appropriate additional values. Step 3 designs appropriate data structures for storing and accessing the values maintained. The central step of incrementalization [4] is: given a program f and an increment operation +, derive an incremental program that computes f(x+y) efficiently by using the result of f(x) [13], the intermediate results of f(x) [11], and auxiliary information of f(x) that can be inexpensively maintained [12]. This unifies existing approaches to incremental computation and is generally applicable; it exploits many existing program analysis and transformation techniques and can be systematically applied. The method has been used successfully in optimizing expensive recursive Electronic Notes in Theoretical Computer Science 90 (2003) 45–47