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Physical imagery: Kinematic versus dynamic models
- Cognitive Psychology
, 1999
"... Physical imagery occurs when people imagine one object causing a change to a second object. To make inferences through physical imagery, people must represent information that coordinates the interactions among the imagined objects. The current research contrasts two proposals for how this coordinat ..."
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Cited by 10 (1 self)
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Physical imagery occurs when people imagine one object causing a change to a second object. To make inferences through physical imagery, people must represent information that coordinates the interactions among the imagined objects. The current research contrasts two proposals for how this coordinating information is realized in physical imagery. In the traditional kinematic formulation, imagery transformations are coordinated by geometric information in analog spatial representations. In the dynamic formulation, transformations may also be regulated by analog representations of force and resistance. Four experiments support the dynamic formulation. They show, for example, that without making changes to the spatial properties of a problem, dynamic perceptual information (e.g., torque) and beliefs about physical properties (e.g., viscosity) affect the inferences that people draw through imagery. The studies suggest that physical imagery is not so much an analog of visual perception as it is an analog of physical action. A simple model that represents force as a rate helps explain why inferences can emerge through imagined actions even though people may not know the answer explicitly. It also explains how and
Using Blur to Affect Perceived Distance and Size
"... We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image’s ..."
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Cited by 1 (0 self)
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We present a probabilistic model of how viewers may use defocus blur in conjunction with other pictorial cues to estimate the absolute distances to objects in a scene. Our model explains how the pattern of blur in an image together with relative depth cues indicates the apparent scale of the image’s contents. From the model, we develop a semi-automated algorithm that applies blur to a sharply rendered image and thereby changes the apparent distance and scale of the scene’s contents. To examine the correspondence between the model/algorithm and actual viewer experience, we conducted an experiment with human viewers and compared their estimates of absolute distance to the model’s predictions. We did this for images with geometrically correct blur due to defocus and for images with commonly used approximations to the correct blur. The agreement between the experimental data and model predictions was excellent. The model predicts that some approximations should work well and that others should not. Human viewers responded to the various types of blur in much the way the model predicts. The model and algorithm allow one to manipulate blur precisely and to achieve the desired perceived scale efficiently.
Exposing Digital Forgeries in Ballistic Motion
"... Abstract—We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a sta ..."
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Abstract—We describe a geometric technique to detect physically implausible trajectories of objects in video sequences. This technique explicitly models the three-dimensional ballistic motion of objects in free-flight and the two-dimensional projection of the trajectory into the image plane of a static or moving camera. Deviations from this model provide evidence of manipulation. The technique assumes that the object’s trajectory is substantially influenced only by gravity, that the image of the object’s center of mass can be determined from the images, and requires that any camera motion can be estimated from background elements. The computational requirements of the algorithm are modest, and any detected inconsistencies can be illustrated in an intuitive, geometric fashion. We demonstrate the efficacy of this analysis on videos of our own creation and on videos obtained from videosharing web-sites. Index Terms—Digital Forensics, Video Forensics I.
The Inversion Effect in Biological Motion Perception: Evidence for a ‘‘Life Detector’’?
"... If biological-motion point-light displays are presented upside down, adequate perception is strongly impaired [1, 2]. Reminiscent of the inversion effect in face recognition, it has been suggested that the inversion effect in biological motion is due to impaired configural processing in a highly tra ..."
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If biological-motion point-light displays are presented upside down, adequate perception is strongly impaired [1, 2]. Reminiscent of the inversion effect in face recognition, it has been suggested that the inversion effect in biological motion is due to impaired configural processing in a highly trained expert system [3–5]. Here, we present data that are incompatible with this view. We show that observers can readily retrieve information about direction from scrambled point-light displays of humans and animals. Even though all configural information is entirely disrupted, perception of these displays is still subject to a significant inversion effect. Inverting only parts of the display reveals that the information about direction, as well as the associated inversion effect, is entirely carried

