A probabilistic sparse skeleton based object detection

Date

2016-11

Author

Altinoklu, Burak
Ulusoy, İlkay
Tarı, Zehra Sibel

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We present a Markov Random Field (MRF) based skeleton model for object shape and employ it in a probabilistic chamfer-matching framework for shape based object detection. Given an object category, shape hypotheses are generated from a set of sparse (coarse) skeletons guided by suitably defined unary and binary potentials at and between shape parts. The Markov framework assures that the generated samples properly reflect the observed or desired shape variability. As the model employs a sparsely sampled skeleton, the shape hypotheses are in the form of linear boundary segments; hence, matching can be performed using Directional Chamfer Matching. As the number of states that each MRF node can take is small, the matching process is efficient. Experiments with giraffe and swan categories of the ETHZ Dataset demonstrate that the method perform well in the case of articulated objects.

Subject Keywords

Markov random field, Generative shape model, Shape skeletons, Shape-based object detection

URI

https://hdl.handle.net/11511/28314

Journal

Pattern Recognition Letters

DOI

https://doi.org/10.1016/j.patrec.2016.07.009

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

B. Altinoklu, İ. Ulusoy, and Z. S. Tarı, “A probabilistic sparse skeleton based object detection,” Pattern Recognition Letters, pp. 243–250, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/28314.