Ji-Hyun Choi

The term videosomnography captures a range of video-based methods used to record and subsequently score sleep behaviors (most commonly sleep vs. wake states). Until recently, the time consuming nature of behavioral videosomnography coding has limited its clinical and research applications. However, with recent technological advancements, the use of auto-videosomnography techniques may be a practical and valuable extension of behavioral videosomnography coding. To test an auto-videosomnography sy

Mean-shift algorithm shows robust performances in various object-tracking technologies including face tracking. Due to its robustness and accuracy, mean-shift algorithm is regarded as one of the best ways to apply in object-tracking technology in computer vision fields. However, it has a drawback of getting into a bottleneck state when faced with a speedy object moving beyond its window size within one image frame interval time. The time required to calculate mean-shift vector could be much less

Recent advancements in video processing make video-based heart rate (HR) estimation possible. Building on this burgeoning field, we adapted an existing video-based HR estimation method to produce more robust and accurate results. Specifically, we removed periodic signals from the recording environment by identifying (and removing) frequency clusters that are present the face region and background. This adaptive passband filter generates more accurate HR estimates and allows other applied filters

Videosomnography (VSG) is a group of video-based methods used to record and label sleep versus awake states in humans. Traditional behavioral-VSG (B-VSG) labeling requires visual inspection of the video by a trained technician to determine whether a subject is sleep or awake. B-VSG is not used to label sleep stages (e.g., slow wave or REM sleep), rather it solely labels whether a subject is asleep or awake at a particular time. In this paper we describe an automated VSG sleep detection system wh

Videosomnography (VSG) is a range of video-based methods used to record and assess sleep vs. wake states in adults and children. Traditional behavioral-VSG (B-VSG) coding requires almost real-time visual inspection by a trained technicians/coders to determine sleep vs wake states. In this paper we describe an automated VSG sleep detection system (auto-VSG) which employs motion analysis to determine sleep vs. wake states in young children. We used child head size to normalize the motion index and

This paper describes a method for estimating the location of an IP-connected camera (a web cam) by analyzing a sequence of images obtained from the camera. First, we classify each image as Day/Night using the mean luminance of the sky region. From the Day/Night images, we estimate the sunrise/set, the length of the day, and local noon. Finally, the geographical location (latitude and longitude) of the camera is estimated. The experiment results show that our approach achieves reasonable performa

Over the past 5 years several video-based heart rate (HR) estimation methods have been developed. These non-contact methods of HR estimation use video processing techniques to estimate the HR of humans in the scene. This is known as videoplethys-mography (VHR) and has applications to the medical and surveillance fields. In this paper, we review two previous VHR techniques and describe techniques to improve VHR accuracy. These include: (1) targeted skin detection within the facial region, (2) rec

Thousands of sensors are connected to the Internet and many of these sensors are cameras. The “Internet of Things” will contain many “things” that are image sensors. This vast network of distributed cameras (i.e. web cams) will continue to exponentially grow. In this paper we examine simple methods to classify an image from a web cam as “indoor/outdoor” and having “people/no people” based on simple features. We use four types of image features to classify an image as indoor/outdoor: color, edge,

Kernel-based tracker shows robust performances in various object tracking technologies. Due to its robustness and accuracy, kernel-based tracker using mean-shift algorithm is regarded as one of the best ways to apply in object tracking technology in computer vision fields. However, it fails tracking when faced with a speedy object moving beyond its window size within one image frame interval time. These tracking failures are reduced with the use of target-adjusted kernel models proposed in this

We addressed two interesting video-based health measurements. First is video-based Heart Rate (HR) estimation, known as video-based Photoplethysmography (PPG) or videoplethysmography (VHR). We adapted an existing video-based HR estimation method to produce more robust and accurate results. Specifically, we removed periodic signals from the recording environment by identifying (and removing) frequency clusters that are present the face region and background. This adaptive passband filter generate