Fruit trees in orchards are typically placed at equal distances in rows; therefore, their branches are intertwined. The precise segmentation of a target tree in this situation is very important for many agricultural tasks, such as yield estimation, phenotyping, spraying, and pruning. However, our survey on tree segmentation revealed that no study has explicitly addressed this intertwining situation. This paper presents a novel dataset in which a precise tree region is labeled carefully by a human annotator by delineating the branches and trunk of a target apple tree. Because traditional rule-based image segmentation methods neglect semantic considerations, we employed cutting-edge deep learning models. Five recently pre-trained deep learning models for segmentation were modified to suit tree segmentation and were fine-tuned using our dataset. The experimental results show that YOLOv8 produces the best average precision (AP), 93.7 box AP@0.5:0.95 and 84.2 mask AP@0.5:0.95. We believe that our model can be successfully applied to various agricultural tasks.

Preoperative planning is mandatory for successful total hip arthroplasty (THA). In planning, the operating surgeon should decide the best type and size of THA components for the patient. However, most digital templating software only simulates acetate templating by overlaying the shape of the prosthesis components on a radiograph; the selection and positioning of the prostheses are performed manually depending on the operator’s experience. Determining the optimal type and size of THA components is a repetitive and time-consuming task for digital and acetate templating. This study proposes a novel approach to automatically select and position THA components that are most suitable for the patient’s bone anatomy. The approach consists of two phases: segmenting a hip anteroposterior (AP) radiographic image into five predefined anatomical regions using a fully convolutional neural network, and estimating the optimal sizes and positions of THA components using deep learning and computer vision technology. The experiments demonstrated that the accuracy of acetabular and femoral component size prediction within one size error was 78.9% and 70.9%, respectively. Compared with meta-analysis results from previous studies, our results are close to human level. An automated digital templating prototype system was developed using our research results and tested in a clinical setting to evaluate field adaptability. These processes are introduced in this study.

코로나-19(COVID-19)의 확산 및 장기화로 인해 비대면 디지털 시대가 도래하면서 가상공간에 해당하는 메타버스에 대한 관심이 급증하였고, 메타버스와 관련한 기술을 이용하여 다양한 플랫폼이 등장하면서 동시에 급성장을 이루게 되었다. 메타버스와 관련이 있는 디자인으로 화상디자인에 대하여 당시 디자인보호법은 별도의 규정을 두고 있지 않아 물품의 부분에 표현되는 부분디자인으로 보호가 이루어졌는데 2021년 디자인보호법이 개정되면서 법률상 화상이라는 개념이 새롭게 등장하게 되었다. 개정법상 화상은 기기의 조작을 위해 이용되거나 기능이 발휘되는 화상만을 포함하고 있기 때문에 새롭게 등장하는 다양한 화상디자인에 대하여 효과적인 보호 수단이 될 수 없다는 한계를 가지고 있다. 새로운 유형의 화상디자인이 지속적으로 창작되는 상황에서 화상디자인의 가장 기본적인 형태만을 보호하고 있기 때문에 아직까지는 보호 대상에서 제외되는 화상디자인이 많은 것이 현실이다. 이는 특히 현실 세계를 모티브로 하여 제작되는 가상세계에서 창작되는 디자인이 현실 세계에서의 디자인과 그 외형상 유사성이 크기 때문에, 현행법상 화상디자인 보호제도는 가상 공간에서 창작되는 디자인을 보호하는 것과 현실 세계에서 등록된 디자인에 대하여 디자인권자가 아닌 타인이 가상 공간으로 옮겨가는 과정에서 발생하는 권리 침해를 방지하는 것에 큰 걸림돌이 될 수 있다. 따라서 본 논문에서는 선행연구를 통해 메타버스를 유형화하고, 현행법상 화상디자인의 보호 체계를 조사하여, 이 중 현실 세계와 가장 유사한 가상세계에서 화상의 보호를 위한 화상디자인 제도의 문제점과 개선 방향을 제시하고자 한다.With the advent of the non-face-to-face digital era due to the spread and prolonged spread of COVID-19, interest in metaverse, which corresponds to virtual space, has soared, and various platforms have emerged using metaverse-related technologies. As for the design related to the metaverse, the Design Protection Law did not provide a separate regulation for graphic design at the time, so it was protected as a partial design expressed on the part of the product. With the revision of the Design Protection Law in 2021, the concept of graphic emerged. Under the revised law, graphics cannot be an effective means of protection for various newly emerging graphic designs because they contain only graphics that are used or functionally used to operate devices. In a situation where new types of graphic design are continuously created, only the most basic form of graphic design is protected, so there are still many graphic designs that are excluded from protection. In particular, since the design ceated in the virtual world based on the real world motif is very similar in appearance to the design in the real world, the graphic design protection system under the current law can be a major obstacle to protecting the design created in the virtual space and preventing the rights of others. Therefore, this paper aims to categorize metaverse through prior research, investigate the protection system of graphic design under the current law, and present the direction of improvement of the graphic design system for graphic protection in the virtual world most similar to the real world.