Scene Segmentation and Reasoning under Uncertainty
Abstract
Segmentation of range images has long been considered in computer vision as an important but extremely difficult problem. A new paradigm for the segmentation of range images into piecewise continuous patches is presented. Data aggregation is performed via model recovery in terms of variable-order bi- variate polynomials using iterative regression. All the recovered models are potential candidates for the final description of the data. Selection of the models is achieved through a maximization of quadratic Boolean problem.. The procedure can be adapted to prefer certain kinds of descriptions (one which describes more data points, or has smaller error, or has lower order model). They have developed a fast optimization procedure for model selection. The major novelty of the approach is in combining model extraction and model selection in a dynamic way. Partial recovery of the models is followed by the optimization (selection) procedure where only the best models are allowed to develop further. The results obtained in this way are comparable with the results obtained when using the selection module only after all the models are fully recovered, while the computational complexity is significantly reduced. The procedure was tested on several real range images.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 30, 1991
- Accession Number
- ADA248889
Entities
People
- Ruzena Bajcsy
Organizations
- Air Force Office of Scientific Research