Estimation of the extent and spread of wildland fires is an important application of high spatial resolution multispectral images. This work addresses a fuzzy segmentation algorithm to map fire extent, active fire front, hot burn scar, and smoke regions based on a statistical model. The fuzzy results are useful data sources for integrated fire behavior and propagation models built using Dynamic...

We describe our current progress in developing Human-Machine Collaborative Systems (HMCSs) for microsurgical applications such as vitreo-retinal eye surgery. Three specific problems considered here are (1) developing of systems tools for describing and implementing an HMCS, (2) segmentation of complex tasks into logical components given sensor traces of a human performing the task, and (3) meas...

In this thesis, a new algorithm to improve automatic target recognition techniques on High Range Resolution (HRR) Profiles is presented and also a number of ways are investigated for target detection using Synthetic Aperture Radar (SAR) images. A new 1-D hybrid Automatic Target Recognition (ATR) algorithm is developed for sequential High Range Resolution (HRR) radar signatures. The proposed hyb...

This paper presents a novel texture segmentation method using Bayesian estimation and neural networks. Multi-scale wavelet coefficients and the context information extracted from neighboring wavelet coefficients were used as input for the neural networks. The output was modeled as a posterior probability. The context information was obtained by HMT (Hidden Markov Trees) model. The proposed segm...

In this paper we propose a two-stage, supervised statistical model for detecting the activities of daily living (ADL) from sensor data streams. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features of the atomic activities. A novel data segmentation ...

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 tagging, text segmentation and information extraction. In these cases, the observations are usually modeled as multinomial distributions over a discrete vocabulary, and the HMM parameters are set to maximize the likelih...

Human–machine collaborative systems (HMCSs) are systems that amplify or assist human capabilities during the performance of tasks that require both human judgment and robotic precision. We examine the design and performance of HMCSs in the context of microsurgical procedures such as vitreo-retinal eye surgery. Three specific problems considered are: (1) development of systems tools for describi...

Hidden Markov Models have been proved to be an efficient way for statistically modeling sequence signals. And the Support Vector Machines seem to be a promising candidate to perform the classification task. A new method combining support vector machine and hidden Markov models is proposed. The output of support vector machines are modified as posterior probability using sigmoid function, and ac...