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3. Suture: Stitching Safety onto Kubernetes Operators

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4. Stitching Together the Experiences of Disabled Kniters

   Taylor Gotfrid, Kelly Mack (January 2021, ACM)

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   Knitting is a popular craft that can be used to create customized fabric objects such as household items, clothing and toys. Additionally, many knitters fnd knitting to be a relaxing and calming exercise. Little is known about how disabled knitters use and beneft from knitting, and what accessibility solutions and challenges they create and encounter. We conducted interviews with 16 experienced, disabled knitters and analyzed 20 threads from six forums that discussed accessible knitting to identify how and why disabled knitters knit, and what accessibility concerns remain. We additionally conducted an iterative design case study developing knitting tools for a knitter who found existing solutions insufcient. Our innovations improved the range of stitches she could produce. We conclude by arguing for the importance of improving tools for both pattern generation and modifcation as well as adaptations or modifcations to existing tools such as looms to make it easier to track progress 

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5. Semi-Supervised Image Stitching from Unstructured Camera Arrays

   https://doi.org/10.3390/s23239481  

   Nghonda_Tchinda, Erman; Panoff, Maximillian Kealoha; Tchuinkou_Kwadjo, Danielle; Bobda, Christophe (December 2023, Sensors)

   Image stitching involves combining multiple images of the same scene captured from different viewpoints into a single image with an expanded field of view. While this technique has various applications in computer vision, traditional methods rely on the successive stitching of image pairs taken from multiple cameras. While this approach is effective for organized camera arrays, it can pose challenges for unstructured ones, especially when handling scene overlaps. This paper presents a deep learning-based approach for stitching images from large unstructured camera sets covering complex scenes. Our method processes images concurrently by using the SandFall algorithm to transform data from multiple cameras into a reduced fixed array, thereby minimizing data loss. A customized convolutional neural network then processes these data to produce the final image. By stitching images simultaneously, our method avoids the potential cascading errors seen in sequential pairwise stitching while offering improved time efficiency. In addition, we detail an unsupervised training method for the network utilizing metrics from Generative Adversarial Networks supplemented with supervised learning. Our testing revealed that the proposed approach operates in roughly ∼1/7th the time of many traditional methods on both CPU and GPU platforms, achieving results consistent with established methods. 

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