Deriving human body shape and motion from optical or magnetic motion-capture data is an inherently difficult task. The body is very complex and the data is rarely error-free and often incomplete. The task becomes even more difficult if one attempts to use video-data instead, because it is much noisier. In the last few years, we have developed techniques that use sophisticated human animation mo...

It is now generally recognized that user-provided image tags are incomplete and noisy. In this study, we focus on the problem of tag completion that aims to simultaneously enrich the missing tags and remove noisy tags. The novel component of the proposed framework is a noisy matrix recovery algorithm. It assumes that the observed tags are independently sampled from an unknown tag matrix and our...

The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...

This paper presents a cross-modal data refinement algorithm for social image parsing, or segmenting all the objects within a social image and then identifying their categories. Different from the traditional fully supervised image parsing that takes pixel-level labels as strong supervisory information, our social image parsing is initially provided with the noisy tags of images (i.e. image-leve...

Assuming string theorists will not soon provide a compelling case for the primary theory underlying particle physics, the field will proceed as it has historically: with data stimulating and testing ideas. Ideally the soft supersymmetry breaking Lagrangian will be measured and its patterns will point to the underlying theory. But there are two new problems. First a matter of principle: the theo...

Motivated by applications to crowdsourcing, we study voting rules that output a correct ranking of alternatives by quality from a large collection of noisy input rankings. We seek voting rules that are supremely robust to noise, in the sense of being correct in the face of any “reasonable” type of noise. We show that there is such a voting rule, which we call the modal ranking rule. Moreover, w...

We propose a non-standard modal logic for specifying agent domains where the agent’s actuators and sensors are noisy, causing uncertainty in action and perception. The logic is multi-modal, indexed with actions; the logic is also augmented with observation objects to facilitate knowledge engineers dealing with explicit observations in the environment, and it includes a notion of probability. A ...

The rough-set theory proposed by Pawlak, has been widely used in dealing with data classification problems. The original rough-set model is, however, quite sensitive to noisy data. Tzung thus proposed deals with the problem of producing a set of fuzzy certain and fuzzy possible rules from quantitative data with a predefined tolerance degree of uncertainty and misclassification. This model allow...