We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression. The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneously learning a regression model that maps features that characterize paths through the model to con...

Whenever people move through their environments they do not move randomly. Instead, they usually follow specific trajectories or motion patterns corresponding to their intentions. Knowledge about such patterns may enable a mobile robot to robustly keep track of persons in its environment. This paper proposes a technique to derive a Hidden Markov Model (HMM) from learned motion patterns of peopl...

A 70-year record of daily monsoon-season rainfall at a network of 13 stations in central-western India is analyzed using a 4state homogeneous hidden Markov model (HMM). The diagnosed states are seen to play distinct roles in the seasonal march of the monsoon, can be associated with “active” and “break” monsoon phases and capture the northward propagation of convective disturbances associated wi...

Rushes footage are considered as cheap gold mine with the potential for reuse in broadcasting and filmmaking industries. However, it is difficult to mine the “gold” from the rushes since usually only minimum metadata is available. This paper focuses on the structuring and indexing of the rushes to facilitate mining and retrieval of “gold”. We present a new approach for rushes structuring and in...

Interaction with human musicians is a challenging task for robots as it involves online perception and precise synchronization. In this paper, we present a consistent and theoretically sound framework for combining perception and control for accurate musical timing. For the perception, we develop a hierarchical hidden Markov model that combines event detection and tempo tracking. The robot perf...

Acoustic-to-articulatory inverse mapping is a difficult problem because of its non-linear and oneto-many characteristics. We have previously developed a speech inversion method using a hidden Markov model (HMM)-based speech production model which takes into account the phonemespecific dynamic constraints of articulatory parameters. We found that the constraint significantly decreases the estima...

Structural health monitoring based on guided wave plays an important role in the damage evaluation of practical application. However, the damage evaluation under time-varying environments which introduces undesired uncertainties to guided wave features is difficult to achieve reliably. In this paper, an approach of guided wave based Hidden Markov Model (HMM) method is proposed to improve the re...

We present a system for generating 2D illustrations from hand drawn outlines consisting of only curve strokes. A user can draw a coarse sketch and the system would automatically augment the shape, thickness, color and surrounding texture of the curves making up the sketch. The styles for these refinements are learned from examples whose semantics have been pre-classified. There can be several s...

In this article we propose a modification to the HMRF-EM framework applied to image segmentation. To do so, we introduce a new model for the neighborhood energy function of the Hidden Markov Random Fields model based on the Hidden Markov Model formalism. With this new energy model, we aim at (1) avoiding the use of a key parameter chosen empirically on which the results of the current models ar...

New potential risk factors for cardioembolic strokes are being considered in the medical community. The presence of these factors can be determined by reading an electrocradiogram (ECG). Manual ECG analysis can take hours. We propose combining accurate Hidden Markov Model (HMM) techniques with Apache Spark to improve the speed of ECG analysis. The potential exists for developing a fast classife...