Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
Segmentation of 3D human body is a very challenging problem in applications exploiting human scan data. To tackle this problem, this paper proposes a topological approach based on...
We investigate genre effects on the task of automatic sentence segmentation, focusing on two important domains – broadcast news (BN) and broadcast conversation (BC). We employ a...
We propose an advanced visual hull technique to compensate for outliers using reliabilities of the silhouettes. The proposed method consists of a foreground extraction technique b...
Hansung Kim, Ryuuki Sakamoto, Itaru Kitahara, Neal...
We present the results of using Hidden Markov Models (HMMs) for automatic segmentation and recognition of user motions. Previous work on recognition of user intent with man/machin...
C. Sean Hundtofte, Gregory D. Hager, Allison M. Ok...