Range image segmentation by an effective jump-diffusion method

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 26; no. 9; pp. 1138 - 1153
• Main Authors: Han, Feng, Tu, Zhuowen, Zhu, Song-Chun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Bayesian analysis; Bayesian methods; change point detection; Cluster Analysis; Clustering algorithms; Computational modeling; data clustering; edge detection; Equations; Hough transform; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Imaging, Three-Dimensional - methods; Index Terms- Energy minimization; Information Storage and Retrieval - methods; jump-diffusion; Layout; Markov chain Monte Carlo; Models, Statistical; Pattern Recognition, Automated - methods; Photography - methods; Proposals; range segmentation; Reflectivity; Reproducibility of Results; Sensitivity and Specificity; Stochastic processes; Studies; Transforms
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/TPAMI.2004.70

This paper presents an effective jump-diffusion method for segmenting a range image and its associated reflectance image in the Bayesian framework. The algorithm works on complex real-world scenes (indoor and outdoor), which consist of an unknown number of objects (or surfaces) of various sizes and types, such as planes, conics, smooth surfaces, and cluttered objects (like trees and bushes). Formulated in the Bayesian framework, the posterior probability is distributed over a solution space with a countable number of subspaces of varying dimensions. The algorithm simulates Markov chains with both reversible jumps and stochastic diffusions to traverse the solution space. The reversible jumps realize the moves between subspaces of different dimensions, such as switching surface models and changing the number of objects. The stochastic Langevin equation realizes diffusions within each subspace. To achieve effective computation, the algorithm precomputes some importance proposal probabilities over multiple scales through Hough transforms, edge detection, and data clustering. The latter are used by the Markov chains for fast mixing. The algorithm is tested on 100 1D simulated data sets for performance analysis on both accuracy and speed. Then, the algorithm is applied to three data sets of range images under the same parameter setting. The results are satisfactory in comparison with manual segmentations.