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.
20301430
A Three-Step Approach to Detect and Remove Grid Lines in Well-Logging Digitization
18353926
Inverse validation for accurate range image registration with structured data
Automatic range image registration is a fundamental yet extremely difficult problem in machine vision. In this paper, we extend a promising registration algorithm, the geometric iterative closest point (GICP), for structured image data. The extended algorithm is based on a relaxation method combining the motion estimation results from the possible correspondences established by the GICP algorithm and those that are further evaluated by an inverse validation procedure. A comparative study based on real images has shown that the extended algorithm is more accurate and robust than the original algorithm.
15266073
Improving ICP with easy implementation for free-form surface matching
Automatic range image registration and matching is an attractive but unresolved problem in both the machine vision and pattern recognition literature. Since automatic range image registration and matching is inherently a very difficult problem, the algorithms developed are becoming more and more complicated. In this paper, we propose a novel practical algorithm for automatic free-form surface matching. This method directly manipulates the possible point matches established by the traditional ICP criterion based on both the collinearity and closeness constraints without any feature extraction, image pre-processing, or motion estimation from outliers corrupted data. A comparative study based on a large number of real range images has shown the accuracy and robustness of the novel algorithm.
18042921
Transforming Least Squares to Weighted Least Squares for Accurate Range Image Registration
The traditional ICP algorithm is a de facto standard approach for range image registration. While it assumes that one data set is a subset of another, this assumption is often violated in practice. As a result, a number of algorithms have been developed to first eliminate false matches due to occlusion, appearance and disappearance of points and then estimate the camera motion parameters in the least squares sense. Instead of eliminating outliers in the process of image registration, we in this paper use the generalised entropy to estimate the probability of correspondences established using the traditional closest point criterion, leading camera motion parameters to be estimated in the weighted least squares sense. For more accurate registration results, we also learn from the probability of correspondences estimated in the past. Since one point in one data set can only correspond to a single point in another, the two way constraint is also imposed. Finally the cam era motion parameters are optimized using the powerful mean field annealing scheme. A comparative study based on real images has shown that the proposed algorithm produces promising automatic range image registration results.
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.
6201806
Loop optimization for aggregate array computations
An aggregate array computation is a loop that computes accumulated quantities over array elements. Such computations are common in programs that use arrays, and the array elements involved in such computations often overlap, especially across iterations of loops, resulting in significant redundancy in the overall computation. The paper presents a method and algorithms that eliminate such overlapping aggregate array redundancies and shows both analytical and experimental performance improvements. The method is based on incrementalization, i.e., updating the values of aggregate array computations from iteration to iteration rather than computing them from scratch in each iteration. This involves maintaining additional information not maintained in the original program. The authors reduce various analysis problems to solving inequality constraints on loop variables and array subscripts, and they apply results from work on array data dependence analysis. Incrementalizing aggregate array computations produces drastic program speedup compared to previous optimizations. Previous methods for loop optimizations of arrays do not perform incrementalization, and previous techniques for loop incrementalization do not handle arrays.
25530597
Optimal space-time scheduling for block fading channels with partial power feedbacks
The multi-user multiple-input-multiple-output (MIMO) space-time scheduling problem is the prime focus of the paper. Using multiple transmit (nT) and receive antennas (nR), it is well known that the link-level throughput will be increased linearly with respect to min[nT, nR] without increasing the bandwidth and power budget. However, optimizing the link-level performance of the MIMO system does not always imply achieving scheduling-level optimization. Therefore, the design optimization across the link layer and the scheduling layer is very important to fully exploit the temporal and spatial dimensions of the communication channel. In this paper, we address the design of the optimal space-time scheduler for the multi-user MIMO system based on an information theoretical approach. Since full knowledge of channel matrix at the transmitter requires a feedback channel capacity not scalable with respect to nT (number of transmit antennas at mobiles) and nR (number of receive antennas at base station), we will assume two partial feedback models.
16164675
Energy efficient routing with adaptive data fusion in sensor networks
While in-network data fusion can reduce data redundancy and hence curtail network load, the fusion process itself may introduce significant energy consumption for emerging wireless sensor networks with vectorial data. Therefore, fusion-driven routing protocols for sensor networks cannot optimize over communication cost only -- fusion cost must also be accounted for. Towards this end, we design a novel routing algorithm, called Adaptive Fusion Steiner Tree (AFST), for energy efficient data gathering in sensor networks that jointly optimizes over the costs for data transmission and data fusion. Furthermore, AFST evaluates the benefit and cost of data fusion along information routes and adaptively adjusts whether fusion shall be performed. Analytically and experimentally, we show that AFST achieves better performance than existing algorithms including SLT, MFST, and SPT.
772940
Fitting subdivision surfaces to unorganized point data using SDM
We study the reconstruction of smooth surfaces from point clouds. We use a new squared distance error term in optimization to fit a subdivision surface to a set of unorganized points, which defines a closed target surface of arbitrary topology. The resulting method is based on the framework of squared distance minimization (SDM) proposed by Pottmann et al. Specifically, with an initial subdivision surface having a coarse control mesh as input, we adjust the control points by optimizing an objective function through iterative minimization of a quadratic approximant of the squared distance function of the target shape. Our experiments show that the new method (SDM) converges much faster than the commonly used optimization method using the point distance error function, which is known to have only linear convergence. This observation is further supported by our recent result that SDM can be derived from the Newton method with necessary modifications to make the Hessian positive definite and the fact that the Newton method has quadratic convergence.
14419032
Content-based 3D neuroradiologic image retrieval: preliminary results
A content-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: directly dealing with multimodal 3D images (MR/CT); image similarity based on anatomical structures of the human brain; and combining both visual and collateral information for indexing and retrieval. A testbed has been implemented for using detected salient visual features for indexing and retrieving 3D images.