Visualizing Life in a Graph Stream

  • Abello J
  • DeSimone D
  • Hadlak S
  • et al.
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Abstract

Our eyes collect a considerable amount of information when we use them to look at objects. In particular, eye movement allows us to gaze at an object and shows our level of interest in the object. In this research, we propose a method that involves real-time measurement of eye movement for human memory enhancement; the method employs gaze-indexed images captured using a video camera that is attached to the user's glasses. We present a prototype system with an infrared-based corneal limbus tracking method. Although the existing eye tracker systems track eye movement with high accuracy, they are not suitable for daily use because the mobility of these systems is incompatible with a high sampling rate. Our prototype has small phototransistors, infrared LEDs, and a video camera, which make it possible to attach the entire system to the glasses. Additionally, the accuracy of this method is compensated by combining image processing methods and contextual information, such as eye direction, for information extraction. We develop an information extraction system with real-time object recognition in the user's visual attention area by using the prototype of an eye tracker and a head-mounted camera. We apply this system to (1) fast object recognition by using a SURF descriptor that is limited to the gaze area and (2) descriptor matching of a past-images database. Face recognition by using haar-like object features and text logging by using OCR technology is also implemented. The combination of a low-resolution camera and a high-resolution, wide-angle camera is studied for high daily usability. The possibility of gaze-guided computer vision is discussed in this paper, as is the topic of communication by the photo transistor in the eye tracker and the development of a sensor system that has a high transparency.

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Abello, J., DeSimone, D., Hadlak, S., Schulz, H.-J., & Sumida, M. (2016). Visualizing Life in a Graph Stream. Big Data of Complex Networks, 293–312. https://doi.org/10.1145/1785455.1785480

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