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.
1747696
HOT SAX: efficiently finding the most unusual time series subsequence
In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. Time series discords have many uses for data mining, including improving the quality of clustering, data cleaning, summarization, and anomaly detection. Discords are particularly attractive as anomaly detectors because they only require one intuitive parameter (the length of the subsequence) unlike most anomaly detection algorithms that typically require many parameters. We evaluate our work with a comprehensive set of experiments. In particular, we demonstrate the utility of discords with objective experiments on domains as diverse as Space Shuttle telemetry monitoring, medicine, surveillance, and industry, and we demonstrate the effectiveness of our discord discovery algorithm with more than one million experiments, on 82 different datasets from diverse domains.
6605003
Sticky notes for the semantic web
Computer-based annotation is increasing in popularity as a mechanism for revising documents and sharing comments over the Internet. One reason behind this surge is that viewpoints, summaries, and notes written by others are often helpful to readers. In particular, these types of annotations can help users locate or recall relevant documents. We believe that this model can be applied to the problem of retrieval on the Semantic Web. In this paper, we propose a generalized annotation environment that supports richer forms of description such as natural language. We discuss how RDF can be used to model annotations and the connections between annotations and the documents they describe. Furthermore, we explore the idea of a question answering interface that allows retrieval based both on the text of the annotations and the annotations associated metadata. Finally, we speculate on how these features could be pervasively integrated into an information management environment, making Semantic Web annotation a first class player in terms of document management and retrieval
6272838
A visual language for sketching large and complex interactive designs
Informal, sketch-based design tools closely match the work practices of user interface designers. Current tools, however, are limited in the size and complexity of interaction that can be specified. We have created an advanced sketch-based visual language that allows for easy prototyping of large, complex interactive designs. In its current embodiment in the denim web design tool, the visual language allows designers to sketch reusable components for recurring page elements, such as navigation bars, as well as conditionals to illustrate and test transitions that depend on a user's input. Designers can also specify sites that accept richer user input than simple clicking. Our informal evaluation shows that these features allow designers with little programming experience to quickly create prototypes of large, complex web sites while still working inside an informal, sketch-based environment
12592432
User simulations for evaluating answers to question series
Recently, question series have become one focus of research in question answering. These series are comprised of individual factoid, list, and ''other'' questions organized around a central topic, and represent abstractions of user-system dialogs. Existing evaluation methodologies have yet to catch up with this richer task model, as they fail to take into account contextual dependencies and different user behaviors. This paper presents a novel simulation-based methodology for evaluating answers to question series that addresses some of these shortcomings. Using this methodology, we examine two different behavior models: a ''QA-styled'' user and an ''IR-styled'' user. Results suggest that an off-the-shelf document retrieval system is competitive with state-of-the-art QA systems in this task. Advantages and limitations of evaluations based on user simulations are also discussed.
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.