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.
2544691
Special Issue on Registration and Fusion of Range Images
This special issue contains nine high quality papers representative of the state-of-the art technologies used to acquire and process range image data. Following the call for papers, 23 manuscripts were received which were reviewed by a pool of 37 expert referees. We are thankful to these anonymous referees for their invaluable work which, as ever, is essential to maintain the high standards of CVIU. All selected papers carry an element of novelty, and we hope that this issue will be useful to theoreticians and practitioners alike.
20301430
A Three-Step Approach to Detect and Remove Grid Lines in Well-Logging Digitization
17413518
Enhancing the Effectiveness of Clustering with Spectra Analysis
For many clustering algorithms, such as K-Means, EM, and CLOPE, there is usually a requirement to set some parameters. Often, these parameters directly or indirectly control the number of clusters, that is, k, to return. In the presence of different data characteristics and analysis contexts, it is often difficult for the user to estimate the number of clusters in the data set. This is especially true in text collections such as Web documents, images, or biological data. In an effort to improve the effectiveness of clustering, we seek the answer to a fundamental question: How can we effectively estimate the number of clusters in a given data set? We propose an efficient method based on spectra analysis of eigenvalues (not eigenvectors) of the data set as the solution to the above. We first present the relationship between a data set and its underlying spectra with theoretical and experimental results. We then show how our method is capable of suggesting a range of k that is well suited to different analysis contexts. Finally, we conclude with further empirical results to show how the answer to this fundamental question enhances the clustering process for large text collections.
2056912
Performance and Scalability Measurement of COTS EJB Technology
Enterprise Java Bean (EJB) is the core technology of J2EE platform to provide the foundation for scalable enterprise applications. About 30 product vendors provide implementations of the J2EE/EJB specification. As each of these products is designed and implemented differently, they show radically different performance profiles. To complicate this technology space, product vendors of J2EE products typically provide different infrastructure options and tuning techniques to address the requirements of performance and scalability. Therefore it is essential for developers to understand the performance and scalability issues of COTS EJB technologies so that the system can meet the performance requirements. In this paper, we investigate an approach to measure the performance and scalability of COTS EJB technology based on its architecture and technology analysis. A benchmark application is developed and tested to measure the system's performance as both the client load and component infrastructure are scaled up. The empirical results are presented and analyzed to evaluate the performance and scalability of the EJB technology under study.
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.
15516376
Supervised rank aggregation
This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by means of unsupervised learning. To further enhance ranking accuracies, we propose employing supervised learning to perform the task, using labeled data. We refer to the approach as Supervised Rank Aggregation. We set up a general framework for conducting Supervised Rank Aggregation, in which learning is formalized an optimization which minimizes disagreements between ranking results and the labeled data. As case study, we focus on Markov Chain based rank aggregation in this paper. The optimization for Markov Chain based methods is not a convex optimization problem, however, and thus is hard to solve. We prove that we can transform the optimization problem into that of Semidefinite Programming and solve it efficiently. Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods.
8159144
Processor frequency selection for SoC platforms for multimedia applications
Of late, there has been a considerable interest in generic and configurable system-on-chip platforms specifically targeted towards implementing multimedia applications. A number of such platforms offer the possibility of including processor soft cores which are highly customizable. For voltage/frequency scaled processors, such customization includes the selection of appropriate voltage/frequency operating points which are tuned to the application set to be mapped onto the platform. In this context, we present an analytical framework that can guide a system designer in identifying the frequency ranges that should be supported by the different processors of a platform architecture. This framework can also be used to identify how such frequency ranges depend on the different parameters of the architecture (such as on-chip buffer sizes), and the performance impacts associated with selecting a particular frequency range. In the case of multimedia streaming applications, identifying such performance impacts and tradeoffs involved in customizing a platform architecture is especially difficult due to the bursty nature of on-chip traffic arising out of multimedia processing and the high variability in their execution requirements. The framework presented here is designed to precisely capture such characteristics and can be used in the design-space exploration of energy-aware platform architectures for multimedia processing.