In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild.” Such unconstrained videos are abundant in personal collections as...
In this paper, we propose a framework that fuses multiple features for improved action recognition in videos. The fusion of multiple features is important for recognizing actions ...
Quadratic optimization lies at the very heart of many structural pattern recognition and computer vision problems, such as graph matching, object recognition, image segmentation, ...
The Computer Vision and Image Processing Laboratory (CVIP Lab) was established in 1994 at the University of Louisville and is committed to excellence in research, teaching and trai...
The accurate localization of facial features plays a fundamental
role in any face recognition pipeline. Constrained
local models (CLM) provide an effective approach to localizati...