The Active Appearance Model (AAM) algorithm has proved to be a successful method for matching statistical models of appearance to new images. Since the original algorithm was described there have been a variety of suggested modifications to the basic algorithm, each typically claiming to be in some way superior. We review these algorithms and report the results of experiments comparing their performance. We also investigate the effects of different methods of estimating the update matrix used in the algorithm. We find that careful choice of the latter has at least as much effect as the choice of updating technique. 1

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Patient monitoring system has been providing a means for caregivers to regularly observe patients' condition in a multiple intensive care units from a single remote location in hospitals. In addition, the system may provide an addition layer of care, which includes software tools that support analysis of patients' vital signs, trends etc. To allow visual surveillance, cameras are installed in the patient wards. From the clinical observation, the key area in the hospital are around and on the bed as most of the activities take place and where patients spend most of their time. Therefore detecting the bed is the first basic task in studying and monitoring the patients' behavior. We have developed a novel technique in detecting the patient bed. This technique is based on Canny Edge detector and Hough Transformation. The algorithm is tested and the experimental results show that our proposed method can effectively locate the position of the patient bed.

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