There was an error published in J. Exp. Biol. 216,[3035][3036][3037][3038][3039][3040][3041][3042][3043][3044][3045][3046] The authors inadvertently omitted to declare a competing interest for one of the authors. The correct Competing Interests statement is given below.J.S.B. owns the company (Positive Science) that manufactured the eye-tracking headpiece and designed the eye-tracking software, which were used in the experiments described within this manuscript.
Eyetracking systems that use video-based cameras to monitor the eye and scene can be made significantly smaller thanks to tiny micro-lens video cameras. Pupil detection algorithms are generally implemented in hardware, allowing for real-time eyetracking. However, it is likely that real-time eyetracking will soon be fully accomplished in software alone. This paper encourages an "open-source" approach to eyetracking by providing practical tips on building a lightweight eyetracker from commercially available micro-lens cameras and other parts. While the headgear described here can be used with any dark-pupil eyetracking controller, it also opens the door to open-source software solutions that could be developed by the eyetracking and image-processing communities. Such systems could be optimized without concern for real-time performance because the systems could be run offline.
There was an error published in J. Exp. Biol. 216,[3035][3036][3037][3038][3039][3040][3041][3042][3043][3044][3045][3046] The authors inadvertently omitted to declare a competing interest for one of the authors. The correct Competing Interests statement is given below.J.S.B. owns the company (Positive Science) that manufactured the eye-tracking headpiece and designed the eye-tracking software, which were used in the experiments described within this manuscript.
We explore the way in which people look at images of different semantic categories (e.g., handshake, landscape), and directly relate those results to computational approaches for automatic image classification. Our hypothesis is that the eye movements of human observers differ for images of different semantic categories, and that this information can be effectively used in automatic content-based classifiers. First, we present eye tracking experiments that show the variations in eye movements (i.e., fixations and saccades) across different individuals for images of 5 different categories: handshakes (two people shaking hands), crowd (cluttered scenes with many people), landscapes (nature scenes without people), main object in uncluttered background (e.g., an airplane flying), and miscellaneous (people and still lives). The eye tracking results suggest that similar viewing patterns occur when different subjects view different images in the same semantic category. Using these results, we examine how empirical data obtained from eye tracking experiments across different semantic categories can be integrated with existing computational frameworks, or used to construct new ones. In particular, we examine the Visual Apprentice, a system in which image classifiers are learned (using machine learning) from user input as the user defines a multiple level object definition hierarchy based on an object and its parts (scene, object, object-part, perceptual area, region), and labels examples for specific classes (e.g., handshake). The resulting classifiers are applied to automatically classify new images (e.g., as handshake/non-handshake). Although many eye tracking experiments have been performed, to our knowledge, this is the first study that specifically compares eye movements across categories, and that links categoryspecific eye tracking results to automatic image classification techniques.
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