15 September 1995 Dynamic adjustment of regularization parameters for the fusion of edge features and noisy dense surfaces
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Proceedings Volume 2589, Sensor Fusion and Networked Robotics VIII; (1995); doi: 10.1117/12.220946
Event: Photonics East '95, 1995, Philadelphia, PA, United States
Abstract
Data fusion provides tools for solving problems which are characterized by distributed and diverse information sources. Many robotic applications need to retrieve particular properties from a scene; so it is necessary to use multiple knowledge sources since a single sensory modality cannot capture all of the physical causes of a given edge feature. In this paper we focus on the problem of extracting features such as image discontinuities from both synthetic and real images. Since edge detection and surface reconstruction are ill-posed problems according to Hadamard, Tikhonov's regularization paradigm is proposed as the basic tool for solving this inversion problem and restoring well-posedness. The proposed framework includes (1) a review of 2D regularization, (2) extension of the standard Tikhonov regularization method by allowing space-variant regularization parameters, and (3) further extension of the regularization paradigm by adding multiple data sources for different sensing modalities. The theoretical approach is complemented by developing a regularized hybrid fusion algorithm for solving the early vision problems of edge detection and surface reconstruction. An evaluation of these methods reveals that this new analytical data fusion technique reconstructs a smooth filtered surface in noisy regions while preserving edge characteristics needed for extracting object features. Results indicate the fusion technique is beneficial for combining edge features from different types of sensory data to locate and identify objects of interest.
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C. Richardson, Renato A. Salinas Silva, Mongi A. Abidi, "Dynamic adjustment of regularization parameters for the fusion of edge features and noisy dense surfaces", Proc. SPIE 2589, Sensor Fusion and Networked Robotics VIII, (15 September 1995); doi: 10.1117/12.220946; https://doi.org/10.1117/12.220946
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KEYWORDS
Sensors

Data fusion

Edge detection

Image fusion

Fusion energy

Algorithm development

Image segmentation

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