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Luciano Nocera

Researcher at University of Southern California

Publications -  40
Citations -  1104

Luciano Nocera is an academic researcher from University of Southern California. The author has contributed to research in topics: Geospatial analysis & Point cloud. The author has an hindex of 14, co-authored 40 publications receiving 1025 citations. Previous affiliations of Luciano Nocera include University of Maryland, Baltimore.

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Patent

Wavelet-based facial motion capture for avatar animation

TL;DR: In this article, an image processing technique based on model graphs and bunch graphs that efficiently represent image features as jets is presented to animate an avatar image in accordance with the person's facial movements.
Proceedings ArticleDOI

Serious video game effectiveness

TL;DR: A comparative study is described that thoroughly investigates the effects of interactivity and media richness on science learning among college students and discusses important results and implications yielded from comparisons among four conditions in the experiment.
Journal ArticleDOI

Multimodality and interactivity: connecting properties of serious games with educational outcomes.

TL;DR: This study represents one of the first attempts to empirically test the educational impact of two important properties of serious games, multimodality and interactivity, through a partial 2 x 3 (interactive, noninteractive by high, moderate, low in multimodalities) factorial between-participants follow-up experiment.
Patent

Three-dimensional point processing and model generation

TL;DR: In this paper, a scene point cloud is processed and a solution to an inverse-function is determined to determine its source objects, and a primitive extraction process and a part matching process are used to compute the inverse function solution.
Patent

Method and system generating an avatar animation transform using a neutral face image

TL;DR: In this paper, a method and system for generating an animation transform using a neutral face image is presented, where the neutral face features are automatically found on the front and side head images using elastic bunch graph matching.