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.
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.
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.
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
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.
10720584
Graph-Based Semi-Supervised Learning as a Generative Model
This paper proposes and develops a new graph-based semi-supervised learning method. Different from previous graph-based methods that are based on discriminative models, our method is essentially a generative model in that the class conditional probabilities are estimated by graph propagation and the class priors are estimated by linear regression. Experimental results on various datasets show that the proposed method is superior to existing graph-based semi-supervised learning methods, especially when the labeled subset alone proves insufficient to estimate meaningful class priors.
5822903
Automatic Accurate Live Memory Analysis for Garbage-Collected Languages
This paper describes a general approach for automatic and accurate live heap space and live heap space-bound analyses for high-level languages. The approach is based on program analysis and transformations and is fully automatic. The space-bound analysis produces accurate (tight) upper bounds in the presence of partially known input structures. The analyses have been implemented and experimental results confirm their accuracy.
9822374
Initial Study on Automatic Identification of Speaker Role in Broadcast News Speech
Identifying a speaker's role (anchor, reporter, or guest speaker) is important for finding the structural information in broadcast news speech. We present an HMM-based approach and a maximum entropy model for speaker role labeling using Mandarin broadcast news speech. The algorithms achieve classification accuracy of about 80% (compared to the baseline of around 50%) using the human transcriptions and manually labeled speaker turns. We found that the maximum entropy model performs slightly better than the HMM, and that the combination of them outperforms any model alone. The impact of the contextual role information is also examined in this study.
206600425
Genetic programming-based decision trees for software quality classification
The knowledge of the likely problematic areas of a software system is very useful for improving its overall quality. Based on such information, a more focused software testing and inspection plan can be devised. Decision trees are attractive for a software quality classification problem which predicts the quality of program modules in terms of risk-based classes. They provide a comprehensible classification model which can be directly interpreted by observing the tree-structure. A simultaneous optimization of the classification accuracy and the size of the decision tree is a difficult problem, and very few studies have addressed the issue. This paper presents an automated and simplified genetic programming (gp) based decision tree modeling technique for the software quality classification problem. Genetic programming is ideally suited for problems that require optimization of multiple criteria. The proposed technique is based on multi-objective optimization using strongly typed GP. In the context of an industrial high-assurance software system, two fitness functions are used for the optimization problem: one for minimizing the average weighted cost of misclassification, and one for controlling the size of the decision tree. The classification performances of the GP-based decision trees are compared with those based on standard GP, i.e., S-expression tree. It is shown that the GP-based decision tree technique yielded better classification models. As compared to other decision tree-based methods, such as C4.5, GP-based decision trees are more flexible and can allow optimization of performance objectives other than accuracy. Moreover, it provides a practical solution for building models in the presence of conflicting objectives, which is commonly observed in software development practice.
37778003
Automatic Accurate Cost-Bound Analysis for High-Level Languages
This paper describes a language-based approach for automatic and accurate cost-bound analysis. The approach consists of transformations for building cost-bound functions in the presence of partially known input structures, symbolic evaluation of the cost-bound function based on input size parameters, and optimizations to make the overall analysis efficient as well as accurate, all at the source-language level. The calculated cost bounds are expressed in terms of primitive cost parameters. These parameters can be obtained based on the language implementation or can be measured conservatively or approximately, yielding accurate, conservative, or approximate time or space bounds. We have implemented this approach and performed a number of experiments for analyzing Scheme programs. The results helped confirm the accuracy of the analysis.