Film genres in digital video can be detected automatically. In a three-step approach we analyze first the syntactic properties of digital films: color statistics, cut detection, camera motion, object motion and audio. In a second step we use these statistics to derive at a more abstract level film

Abstract Gesture-based interfaces, in which the user specifies commands by simple freehand drawings,offer an alternative to traditional keyboard, menu, and direct manipulation interfaces. The ability to specify objects, an operation, and additional parameters with a single intuitive gesture makesgesture-based systems appealing to both novice and experienced users.

In this paper we propose an automatic recognition system for wild flowers. Two photos, one of the flower and one of the leaves taken from directly above or at a close angle of a single wild flower, were used as a single set. The objective (flower, leaf) is extracted from each image using a

The project CL.E.A.R. (Cloudiness Experiment for Automatic Recognition) is a joint project between the ESTER outdoor Station of the University of Rome Tor Vergata and the Centre of Meteorological Experimentation (Re.S.M.A.) of Vigna di Valle. Its aim is to investigate the possibility of automatic

-visual automatic speech recognition and present novel contributions in two main areas: First, the visual front end design, based on a cascade of linear image transforms of an appropriate video region-of-interest, and subsequently, audio-visual speech integration. On the later topic, we discuss new work on feature

Abusive messages (flames) can be both a source of frustration and a waste of time for Internet users. This paper describes some approaches to flame recognition, including a prototype system, Smokey. Smokey builds a 47-element feature vector based on the syntax and semantics of each sentence

Polysemy is one of the major causes of difficulties in se- mantic clustering of words in a corpus. In this pal)er, we first; give a definition of polysemy from the viewpoint of clustering and then, based on this detinition, we prol)ose a clustering method which reeogniscs verbal 1)olysenfics fi'om a textual corpus. The results of experi,nensdemonstrate t;hc cffeetivencss of the prol) osed method.

This article presents a theory in which automatization is construed as the acquisition of a domain-specific knowledge base, formed of separate representations, instances, of each exposure to the task. Processing is considered automatic if it relies on retrieval of stored instances, which will occur

study on the complexity of classifying simple musical instruments by their sound. Very little work has previously been done on this type of problem, except in the related fields of automatic speaker recognition [1], and pitch recognition [2]. We have arbitrarily chosen the following three acoustical

We present a system for identifying the semantic relationships, or semantic roles, filled by constituents of a sentence within a semantic frame. Various lexical and syntactic features are derived from parse trees and used to derive statistical classifiers from hand-annotated training data. 1