Understanding human behaviors is a challenging problem in computer vision that has
recently seen important advances. Human behavior understanding combines image and …

In image processing, cartoon character classification, retrieval, and synthesis are critical, so
that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon …

Many approaches to solve the problem of scene character recognition utilize local features
such as histograms of oriented gradients (HoG), SIFT, Shape Contexts (SC), Geometric Blur …

Recent efforts in computer vision tackle the problem of human activity understanding in
video sequences. Traditionally, these algorithms require annotated video data to learn …

In this paper, we investigate the concept of projective depth, demonstrate its application and
significance in view-invariant action recognition. We show that projective depths are …

In this paper, we present a new geometric-temporal representation for visual action
recognition based on local spatio-temporal features. First, we propose a modified …

The structure of data is important to the recognition of data. It is a fundamental question how
measures and complements the structure of multi-features, because the fusion structure of …

Action recognition in still images poses a great challenge due to (i) fewer available training
data,(ii) absence of temporal information. To address the first challenge, we introduce a …

In order to deal with the time-varying, nonlinear, and large-lagging problems in the auto-
chlorination control process of tap water, a self-tuning PID chlorination controller with …

In this paper, we propose an unsupervised framework for action spotting in videos that does
not depend on any specific feature (eg HOG/HOF, STIP, silhouette, bag-of-words, etc.) …