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—When looking at a scene, observers feel that they see its entire structure in great detail and can immediately notice any changes in it. However, when brief blank fields are placed between alternating displays of an original and a modified scene, a striking failure of perception is induced: identification of changes becomes extremely difficult, even when(More)
Microsaccades, or tiny eye movements that take place during periods of fixation, have long been thought to be random artifacts of the oculomotor system. Here we demonstrate a possible link between microsaccades and covert attention shifts. We designed two psychophysical tasks involving spatial cues that had identical sensory stimuli but differing patterns(More)
This paper presents an FPGA-based system for detecting people from video. The system is designed to use JPEG-compressed frames from a network camera. Unlike previous approaches that use techniques such as background subtraction and motion detection, we use a machine-learning-based approach to train an accurate detector. We address the hardware design(More)
Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the classifier offline with manually labeled training data. We present a framework that learns the classifier online with automatically labeled data for the specific case of detecting(More)
Eye movements can be used to infer the allocation of covert attention. In this article, we propose to model the allocation of attention in a task-dependent manner based on different eye movement conditions, specifically fixation and pursuit. We show that the image complexity at eye fixation points during fixation, and the pursuit direction during pursuit(More)