3703843
3D model-based hand tracking using stochastic direct search method
Tracking the articulated hand motion in a video sequence is a challenging problem in which the main difficulty arises from the complexity of searching for an optimal motion estimate in a high dimensional configuration space induced by the articulated motion. Considering that the complexities of this problem mmay be reduced by learning th lower dimensional manifold of the articulation motion in the configuration space, we propose a new representation for the nonlinear manifold of the articulated motion, with a stochastic simplex algorithm that facilitates very efficient search. Contrary to traditional methods of representing the manifolds through clustering and transition matrix construction, we maintain the set of all training samples. To perform the search of best matching configuration with respect to the input image, we combine sequential Monte Carlo technique with the Nelder-Mead simplex search which is efficient and effective when the gradient is not readily accessible. This new approach has been successfully applied to hand tracking and our experiments show the efficiency and robustness of our algorithm.
1721157
Modeling the constraints of human hand motion
Hand motion capture is one of the most important parts of gesture interfaces. Many current approaches to this task generally involve a formidable nonlinear optimization problem in a large search space. Motion capture can be achieved more cost-efficiently when considering the motion constraints of a hand. Although some constraints can be represented as equalities or inequalities, there exist many constraints which cannot be explicitly represented. In this paper, we propose a learning approach to model the hand configuration space directly. The redundancy of the configuration space can be eliminated by finding a lower-dimensional subspace of the original space. Finger motion is modeled in this subspace based on the linear behavior observed in the real motion data collected by a CyberGlove. Employing the constrained motion model, we are able to efficiently capture finger motion from video inputs. Several experiments show that our proposed model is helpful for capturing articulated motion.
2209704
Capturing natural hand articulation
Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.
3682795
Multibody grouping via orthogonal subspace decomposition
Multibody structure from motion could be solved by the factorization approach. However, the noise measurements would make the segmentation difficult when analyzing the shape interaction matrix. This paper presents an orthogonal subspace decomposition and grouping technique to approach such a problem. We decompose the object shape spaces into signal subspaces and noise subspaces. We show that the signal subspaces of the object shape spaces are orthogonal to each other. Instead of using the shape interaction matrix contaminated by noise, we introduce the shape signal subspace distance matrix for shape space grouping. Outliers could be easily identified by this approach. The robustness of the proposed approach lies in the fact that the shape space decomposition alleviates the influence of noise, and has been verified with extensive experiments.
17428031
A Function-Based Classifier Learning Scheme Using Genetic Programming
Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multicategory classifiers based on genetic programming. For a k-class classification problem, a training strategy called adaptive incremental learning strategy and a new fitness function are used to generate k discriminant functions. We urge the discriminant functions to map the domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a Z-value measure is developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy of classification.
11169177
Mining maximal frequent intervals
Many real world data are associated with intervals of time or distance. Mining frequent intervals from such data allows the users to group transactions with similar behavior together. Previous work only focuses on the problem of mining frequent intervals in a discrete domain. This paper first proposes the notion of maximal frequent intervals, which are superior to frequent intervals in many perspectives, and then provides a method for mining maximal frequent intervals in either a discrete domain or a continuous domain. Experimental results indicate that our method outperforms previous method for mining frequent intervals, and improves the runtime by two orders of magnitude for databases with a large number of distinct endpoints.
13185193
Question answering from the web using knowledge annotation and knowledge mining techniques
We present a strategy for answering fact-based natural language questions that is guided by a characterization of real-world user queries. Our approach, implemented in a system called Aranea, extracts answers from the Web using two different techniques: knowledge annotation and knowledge mining. Knowledge annotation is an approach to answering large classes of frequently occurring questions by utilizing semi\-structured and structured Web sources. Knowledge mining is a statistical approach that leverages massive amounts of Web data to overcome many natural language processing challenges. We have integrated these two different paradigms into a question answering system capable of providing users with concise answers that directly address their information needs.
15165046
A Classification Tree Based on Discriminant Functions
The classification problem is an important topic in knowledge discovery and machine learning. Traditional classification tree methods and their improvements have been discussed widely. This work proposes a new approach to construct decision trees based on discriminani functions which are learned using genetic programming. A discriminant function is a mathematical function for classifying data into a specific class. To learn discriminant functions effectively and efficiently, a distance-based fitness function for genetic programming is designed. After the set of discriminant functions for all classes is generated, a classifier is created as a binary decision tree with the Z-value measure to resolve the problem of ambiguity among discriminant functions. Several popular datasets from the UCI Repository were selected to illustrate the effectiveness of the proposed classifiers by comparing with previous methods. The results show that the proposed classification tree demonstrates high accuracy on the selected datasets.
16556041
Segmented fuzzy checkpointing for main memory databases
'Toscane' can be used as a decorative pot plant that flowers early and abundantly. The plant can be sold in different sizes from approximately 40 cm to 80 cm in height. The plant produces abundant flowers that enlarge in size as the plant becomes bigger. The decorative leaves are glossy, dark green, coriaceous and extremely durable. 'Toscane' produces bright, long-lived and large flowers that are held above the foliage. The heart-shaped flowers maintain their original red color as they mature.