Structured Scene Decoding with Finite State Machines

Abstract : We introduce in this work a novel stochastic inference process, for scene annotation and object class segmentation, based on finite state machines (FSMs). The design principle of our framework is generative and based on building, for a given scene, finite state machines that encode annotation lattices, and inference consists in finding and scoring the best configurations in these lattices. Different novel operations are defined using our FSM framework including reordering, segmentation, visual transduction, and label dependency modeling. All these operations are combined together in order to achieve annotation as well as object class segmentation.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-02325831
Contributor : Hichem Sahbi <>
Submitted on : Tuesday, October 22, 2019 - 12:53:03 PM
Last modification on : Thursday, October 24, 2019 - 9:28:22 AM

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Hichem Sahbi. Structured Scene Decoding with Finite State Machines. IEEE International Conference on Image Processing, ICIP, Oct 2018, Athens, Greece. pp.485-489, ⟨10.1109/ICIP.2018.8451821⟩. ⟨hal-02325831⟩

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