The LEGO brick system is one of the most popular toys in the world. It can stimulate one's creativity while being lots of fun. It is however very hard for the naive user to assemble complex models without instructions. In this work, we propose a method that converts 3D polygonal models into LEGO brick building instructions automatically. The most important part of the conversion is that the connectivity between the bricks should be assured. For this, we introduce a graph structure named "legograph" that allows us to generate physically sound models that do not fall apart by managing the connections between the bricks. We show some experimental results and evaluation results. These show that the 3D brick models generated following the instructions generated by our method do not fall apart and that one can learn how to efficiently build 3D structures from our instructions.
The dominant colors in an image can be used for image search, color editing, palette generation, and several other applications. Conventionally, dominant colors are extracted using clustering or histogram-based methods. However, these methods cannot extract the dominant colors of small regions, which are essential for the analysis of color schemes. This study developed an approach to automatically extract dominant colors based on color features that are typically considered by human observers when analyzing color schemes. The proposed method first calculates the initial dominant color candidates using the K-means algorithm in the CIELAB color space and the graph cut of a region adjacency graph (RAG) of the segmented image. Next, the algorithm calculates the color features such as the saturation, contrast, and area of each cluster, based on which it extracts the dominant colors. Our method can extract prominent colors from small image regions as the dominant colors, which is not possible using conventional methods.
INDEX TERMS color, image color analysis, image processing
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