Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect. https://github.com/patrykcieslak/stonefish

This paper addresses the challenge of developing techniques for the effective teaching of computer vision. It reports on the experience of using the technique of project-based learning as a motivating method for teaching computer vision strategies within the context of a Master's program module in Visual Perception at the University of Girona (Spain). Students were asked to generate projects which involved the use of invariant features, i.e. algorithms to detect and describe local features in images. Within this project-based context, students were encouraged to search for information resources that would accompany their project ideas. Every student team was supervised by a tutoring instructor and periodic meetings were scheduled during this six-week project to follow their progress.

This paper presents a novel approach for combining a set of registered images into a composite mosaic with no visible seams and minimal texture distortion. To promote execution speed in building large area mosaics, the mosaic space is divided into disjoint regions of image intersection based on a geometric criterion. Pair-wise image blending is performed independently in each region by means of watershed segmentation and graph cut optimization. A contribution of this work – use of watershed segmentation to find possible cuts over areas of low photometric difference – allows for searching over a much smaller set of watershed segments, instead of over the entire set of pixels in the intersection zone. The proposed method presents several advantages. The use of graph cuts over image pairs guarantees the globally optimal solution for each intersection region. The independence of such regions makes the algorithm suitable for parallel implementation. The separated use of the geometric and photometric criteria frees the need for a weighting parameter. Finally, it allows the efficient creation of large mosaics, without user intervention. We illustrate the performance of the approach on image sequences with prominent 3D content and moving objects. 1