Shared gaze visualizations have been found to enhance collaboration and communication outcomes in diverse HCI scenarios including computer supported collaborative work and learning contexts. Given the importance of gaze in surgery operations, especially when a surgeon trainer and trainee need to coordinate their actions, research on the use of gaze to facilitate intra-operative coordination and instruction has been limited and shows mixed implications. We performed a field observation of 8 surgeries and an interview study with 14 surgeons to understand their visual needs during operations, informing ways to leverage and augment gaze to enhance intra-operative coordination and instruction. We found that trainees have varying needs in receiving visual guidance which are often unfulfilled by the trainers’ instructions. It is critical for surgeons to control the timing of the gaze-based visualizations and effectively interpret gaze data. We suggest overlay technologies, e.g., gaze-based summaries and depth sensing, to augment raw gaze in support of surgical coordination and instruction.

In this paper, we seek to fit a model, specified in terms of connected ellipses, to an image silhouette. Some algorithms that have attempted this problem are sensitive to initial guesses and also may converge to a wrong solution when they attempt to minimize the objective function for the entire ellipse structure in one step. We present an algorithm that overcomes these issues. Our first step is to temporarily ignore the connections, and refine the initial guess using unconstrained Expectation-Maximization (EM) for mixture Gaussian densities. Then the ellipses are reconnected linearly. Lastly, we apply the Levenberg-Marquardt algorithm to fine-tune the ellipse shapes to best align with the contour. The fitting is achieved in a hierarchical manner based upon the joints of the model. Experiments show that our algorithm can robustly fit a complex ellipse structure to a corresponding shape for several applications.

In this paper, we discuss an effective method for detecting human upper body parts from a 2D image silhouette using curvature analysis and ellipse fitting. First we smooth the silhouette so that we can determine just the global features: the head, hands and armpits. Next we reduce the smoothing to detect the local features of the neck and elbows. We model the human upper body by multiple connected ellipses. Thus we segment the body by the extracted features. Ellipses are fitted to each segment. Lastly, we apply a non-linear least square method to minimize the differences between the connected ellipse model and the edge of the silhouette.

In recent years, the field of terahertz (THz) science and technology has entered a completely new phase of unprecedented expansion that is generating ever growing levels of broad-based international attention. In particular,there have been important advances in state-of-the-art THz technology and very enthusiastic growth in research activities associated with related scientific and industrial applications. One can legitimately argue that the potential payoffs of THz sensing and imaging (THz S&I) to application areas such as defense, security, biology and medicine are the major drivers of this new phenomenon. However, there remain major science and technology "gaps" in the THz regime that must be reconciled before many of the perceived payoffs ever become realizable. Therefore, it is natural to ask the question "Is now the time for THz?" or rather, are these recent events just a repeat of previous cycles in THz overenthusiasm that have been witnessed during the last century? The main goal of this paper is to consider some of the most promising THz S&I applications within the specific context of their particular science and technology challenges in an attempt to credibly judge (or speculate on) their future potential.