Abstract-Obstacle detection plays an important role in unmanned surface vehicles (USV). The USVs operate in highly diverse environments in which an obstacle may be a floating piece of wood, a scuba diver, a pier, or a part of a shoreline, which presents a significant challenge to continuous detection from images taken onboard. This paper addresses the problem of online detection by constrained unsupervised segmentation. To this end, a new graphical model is proposed that affords a fast and continuous obstacle image-map estimation from a single video stream captured onboard a USV. The model accounts for the semantic structure of marine environment as observed from USV by imposing weak structural constraints. A Markov random field framework is adopted and a highly efficient algorithm for simultaneous optimization of model parameters and segmentation mask estimation is derived. Our approach does not require computationally intensive extraction of texture features and comfortably runs in real-time. The algorithm is tested on a new, challenging, dataset for segmentation and obstacle detection in marine environments, which is the largest annotated dataset of its kind. Results on this dataset show that our model outperforms the related approaches, while requiring a fraction of computational effort.
A quality of image match is usually estimated by measuring image similarity. Unfortunately, similarity measures assess only such transformations that change appearance of the deformed image, and in the case of non-rigid registration the results of the similarity measurement depend on the registration direction. This asymmetric relation leads to registration inconsistency and reduces the quality of registration. In this work we propose a symmetric registration approach, which improves the registration by measuring similarity in both registration directions. The solution presented in this paper is based on the interaction of both images involved in the registration process. Images interact with forces, which are according to the Newton's action-reaction law forming a symmetric relationship. These forces may transform both of the images, although in our implementation one of the images remains fixed. The experiments performed to demonstrate the advantages of the symmetric registration approach involve registration of simple objects, recovering synthetic deformation, and interpatient registration of real images of head. The results show improvements of registration consistency and also indicate the improvement of registration correctness.
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