5 topics：
Lecture 1: An introduction to vision science
Most of us take completely for granted our ability to understand the world through seeing. However, we do not fully understand how humans are able quickly and effortlessly to perceive meaningful, coherent, three-dimensional scenes. I will demonstrate that perception is not a clear window onto reality, but rather an actively constructed, meaningful model of the world. Examples include phenomena related to adaptation, illusions, ambiguous figures, visual completion, and visual categorization. I will then describe four stages of visual perception?the image-based, surface-based, object-based, and category-based stages of perception?and two metaphorical information processing “directions”?bottom-up and top-down.
Lecture 2: Connecting the statistics in images with vision theories
I will first present computational evidence on the basis of the work by Olshausen and Field (1998, 2004) to demonstrate the connection between the statistics in natural images and neural encoding of image-based information. Second, I will present the texton theory developed by Julesz (1981) and related experimental evidence.
Lecture 3: From natural statistics to generic priors
This lecture will illustrate how prior knowledge affects human perception. This prior knowledge, which in its most general form is termed generic priors, is consistent with the statistics of natural scenes. Three studies will be presented, illustrating generic priors related to light-from-above (Kersten, Adelson, etc.), non-accidental viewpoints (Freeman), and slow-and-smooth motion (Weiss).
Lecture 4: Bayesian inference in cognitive science
This lecture will illustrate the use of a Bayesian framework in the context of cognitive research investigating how humans draw strong inferences from noisy and sparse data.? I will present computational models that have been developed for two different domains in which inference is critical?motion perception and causal reasoning. The picture emerging from this work is that a key basis for human inference is the use of generic priors?tacit general assumptions people make about the way the world works, which then guide their learning and inference from observed data. I will sketch broader implications, including future directions for applying Bayesian modeling.
Lecture 5: Review of object recognition
I will review the literature concerning object recognition from three perspectives?psychophysics, physiology, and computation. I will further discuss the strengths and weaknesses of two main theories, template matching and structural description.
Curriculum Vitae
Hongjing Lu
6451 Franz Hall, UCLA
310-267-4683 ext. #2
hongjing@ucla.edu
http://hongjing.bol.ucla.edu/ |
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Education
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Research Interests
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Cognitive psychology and cognitive neuroscience studies of human vision
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Computational vision
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Object recognition biological motion
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Perceptual learning
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Statistical modeling of cognition
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Causal learning
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