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This lecture presents research on a general framework for perceptual organization that was contacted mainly at the Institute for Robotics and Intelligent Systems of the University of Southern California. It is not written as a historical recount of the work, since the sequence of the presentation is not in chronological order. It aims at presenting an approach to a wide range of problems in computer vision and machine learning that is data-driven, local and requires a minimal number of assumptions. The tensor voting framework combines these properties and provides a unified perceptual organization methodology applicable in situations that may seem heterogeneous initially. Authors Philippos Mordohai and Gerard Medioni show how several problems can be posed as the organization of the inputs into salient perceptual structures, which are inferred via tensor voting. The book extends the original tensor voting framework with the addition of boundary inference capabilities, a novel re-formulation of the framework applicable to high-dimensional spaces and the development of algorithms for computer vision and machine learning problems. The authors provide complete analysis for some problems and briefly outline the approach for other applications and provide references to relevant sources.