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Video inpainting is the process of repairing missing regions (holes) in videos. Most automatic techniques are computationally intensive and unable to repair large holes. To tackle these challenges, a computationally-efficient algorithm that separately inpaints foreground objects and background is proposed. Using Dynamic Programming, foreground objects are holistically inpainted with object templates that minimizes a sliding-window dissimilarity cost function. Static background are inpainted by adaptive background replacement and image inpainting.

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The objective of image inpainting is to perform a seamless completion of missing areas in images. Evaluating the perceptual quality of an inpainting algorithm must rely on features of the Human Visual System. Using eye-tracking experiments, we show that there is a strong correlation between inpainting quality and visual attention. By comparing gaze densities within and outside the hole regions of inpainted images, we show that discernible artifacts due to inpainting attract an unusual amount of visual attention. The gaze density within the hole, normalized with the gaze density of the same region from the unmodified image, provides a useful measure in comparing different inpainting processes and corroborates well with subjective rankings.

Abstract-Group video-conferencing systems are routinely used in major corporations, hospitals and universities for meetings, tele-medicine and distance learning among participants from very distant locations. As the use of video-conferencing becomes widely prevalent, the privacy concern's raised by this technology becomes an important issue to be addressed. In this paper we propose a real-time privacy preserving video conferencing system which protects the visual and audio privacy of selected individuals. In our proposed system we differentiate between the general participants and private participants (PP) whose privacy needs to be protected. We further divide the private participants into two different categories and provide a varying level of privacy protection based on the requirements. Specifically, among private participants, we have Active Private Participants (APP) who interactively participate in the meeting and Passive Private Participants (PPP) who play a passive observatory role. The video and audio privacy of the APP are protected by obfuscating their visual information by simple black boxing and real-time pitch modification process respectively. For the PPP, we completely protect their privacy by continuously detecting their presence and erasing them with a real-time adaptive background replacement process.

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