Maritime computer vision is a requirement for autonomous surface vehicles and can improve maritime safety if a high level of robustness is achieved. As deep learning dominates the computer vision community, domain-specific datasets are required to obtain well-generalizing and reliable models. However, maritime datasets, especially those containing videos and temporally dense annotations, are still small compared to other domains, such as autonomous driving or generic computer vision datasets. This paper introduces SeaClips, a new maritime video dataset containing 74 videos with an average duration of 14 seconds and with 31k frames in total. Videos were recorded under varying conditions, with three cameras mounted on shore and on boats. SeaClips provides frame-by-frame annotations, encompassing 129k bounding boxes of seven categories, containing vessel and non-vessel classes. SeaClips contributes to a broader coverage of maritime scenarios, leading to more robust computer vision models. Baseline results on the dataset are established by evaluating six image-based models and three models using temporal context, ranging from lightweight models to heavy transformer architectures. It is found that the different scales and shapes at which objects appear in SeaClips pose a challenge to state-of-the-art detectors. SeaClips is accessible for research on maritime obstacle detection at: https://huggingface.co/datasets/SEA-AI/SeaClips.

This paper proposes an approach referred as: iterative refinement of possibility distributions by learning (IRPDL) for pixel-based image classification. The IRPDL approach is based on the use of possibilistic reasoning concepts exploiting expert knowledge sources as well as ground possibilistic seeds learning. The set of seeds is constructed by incrementally updating and refining the possibility distributions. Synthetic images as well as real images from the RIDER Breast MRI database are being used to evaluate the IRPDL performance. Its performance is compared with three relevant reference methods: region growing, semi-supervised fuzzy pattern matching, and Markov random fields. The IRDPL performance (in terms of recognition rate, 87.3%) is close to the Markovian method (88.8%) that is considered to be the reference in pixel-based image classification. IRPDL outperforms the other two methods, respectively, at the recognition rates of 83.9% and 84.7%. In addition, the proposed IRPDL requires fewer parameters for the mathematical representation and presents a reduced computational complexity.

In this paper, an approach for pixel unmixing based on possibilistic similarity is proposed. This approach uses possibility distributions to express both the expert’s semantic knowledge (a priori knowledge) and the contextual information. Dubois-Prade’s probability-possibility transformation is used to construct these possibility distributions starting from statistical information (learning areas delimitated by an expert for each thematic class in the analyzed scene) which serve, first, for the estimation of the probability density functions using the kernel density estimation. The pixel unmixing is then performed based on the possibilistic similarity between a local possibility distribution estimated around the considered pixel and the obtained possibility distributions representing the predefined thematic classes. The obtained similarity values are used in order to obtain the abundances of different classes in the considered pixel. Accuracy analysis of pixels unmixing demonstrates that the proposed approach represents an efficient estimator of their abundances of the predefined thematic classes and, in turn, higher classification accuracy is achieved. Synthetic images are used in order to evaluate the performances of the proposed approach.

To obtain a long lifespan of knee joint implant, it is indispensable to balance the collateral ligaments of the knee and to restore a correct alignment of the mechanical axis of the lower limb during total knee replacement (TKR). Nowadays, a proper mechanical axis alignment of the lower limb is guaranteed by computer assisted orthopaedic surgery (CAOS). To assure a symmetric laxity of these ligaments, a smart tibial baseplate instrumented with three strain gages could be used. Our objective is to study the displacement of the pressure center as a function of ligament balancing represented by the shift of resultant axial force exerted by the two condyles of the femoral component on the tibial tray. Improving the surgeon perception helps to solve the ligament balancing problem. Our first instrumented tibial baseplate has been modeled using ANSYS Workbench 11. As a result, soft tissue balance is possible to be assessed using the pressure center position. In this paper, we present our approach and the preliminary results of instrumented tibial plateau model which would be validated later by the first prototype.