doctoral dissertation, tech. report CMU-RI-TR-03-14, Robotics Institute, Carnegie Mellon University, May, 2003
|Perceptual organization refers to the process of organizing sensory input into coherent and interpretable perceptual structures. This process is challenging due to the chicken-and-egg nature between the various sub-processes such as image segmentation, figure-ground segregation and object recognition. Low-level processing requires the guidance of high-level knowledge to overcome noise; while high-level processing relies on low-level processes to reduce the computational complexity. Neither process can be sufficient on its own. Consequently, any system that carries out these processes in a sequence is bound to be brittle. An alternative system is one in which all processes interact with each other simultaneously.
In this thesis, we develop a set of simple yet realistic interactive processing models for perceptual organization. We model the processing in the framework of spectral graph theory, with a criterion encoding the overall goodness of perceptual organization. We derive fast solutions for near-global optima of the criterion, and demonstrate the efficacy of the models on segmenting a wide range of real images.
Through these models, we are able to capture a variety of perceptual phenomena: a unified treatment of various grouping, figure-ground and depth cues to produce popout, region segmentation and depth segregation in one step; and a unified framework for integrating bottom-up and top-down information to produce an object segmentation from spatial and object attention.
We achieve these goals by empowering current spectral graph methods with a principled solution for multiclass spectral graph partitioning; expanded repertoire of grouping cues to include similarity, dissimilarity and ordering relationships; a theory for integrating sparse grouping cues; and a model for representing and integrating higher-order relationships. These computational tools are also useful more generally in other domains where data need to be organized effectively.
|perceptual organization,image segmentation, figure-ground,depth segregation,attention, bias, popout, visual search,clustering, graph partitioning, constrained optimization|
Note: CNBC and HumanID
|Stella Yu, "Computational Models of Perceptual Organization," doctoral dissertation, tech. report CMU-RI-TR-03-14, Robotics Institute, Carnegie Mellon University, May, 2003|
author = "Stella Yu",
title = "Computational Models of Perceptual Organization",
booktitle = "",
school = "Robotics Institute, Carnegie Mellon University",
month = "May",
year = "2003",
address= "Pittsburgh, PA",
Notes = "CNBC and HumanID"
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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