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ABSTRACT:3D models have been widely used by spread of many available free-software. Additionally, enormous images can be easily acquired, and images are utilized for creating the 3D models recently. The creation of 3D models by using huge amount of images, however, takes a lot of time and effort, and then efficiency for 3D measurement are required. In the efficient strategy, the accuracy of the measurement is also required. This paper develops an image selection method based on network design that means surveying network construction. The proposed method uses image connectivity graph. The image connectivity graph consists of nodes and edges. The nodes correspond to images to be used. The edges connected between nodes represent image relationships with costs as accuracies of orientation elements. For the efficiency, the image connectivity graph should be constructed with smaller number of edges. Once the image connectivity graph is built, the image selection problem is regarded as combinatorial optimization problem and the graph cuts technique can be applied. In the process of 3D reconstruction, low quality images and similar images are also extracted and removed. Through the experiments, the significance of the proposed method is confirmed. It implies potential to efficient and accurate 3D measurement.

To ensure the coexistence of autonomous personal mobility vehicles (PMVs) and pedestrians in a pedestrian zone, they should be able to smoothly pass across and avoid each other. Studies suggest that it is possible that PMVs and pedestrians can pass each other in a short period of time without compromising their comfort; this can be achieved through understanding how pedestrians react to the behavior of PMVs and by modifying the autonomous navigation of PMVs accordingly. Therefore, in this study, the avoidance behavior characteristics of pedestrians were investigated. Experiments were conducted to understand the influence of the selected avoiding behavior parameters and to understand the behavior characteristics of pedestrians in relation to the behavior of PMVs. Furthermore, a path planning strategy that enables smooth passing was developed based on these characteristics. The usefulness of this method was evaluated. The avoidance time and the avoiding angular velocity at the start and end of the avoidance behavior were the parameters that contributed to smooth autonomous navigation. The results show that pedestrian tolerance improves and the avoidance width decreases depending on these parameters. Furthermore, smooth autonomous navigation can be achieved using the characteristics of pedestrians’ cognition against PMVs.

To estimate garment impressions, we verified three regression models using design parameters. Using three-dimensional apparel simulation, we generated 375 images of a men's outdoor jacket by changing design parameters: length, waist, hem circumference, and sleeve circumference. Nine people evaluated cool-uncool (kakkoī-kakkowarui in Japanese) impressions of the garment images using a semantic differential method. With the design parameters, we obtained the estimated image impression using three regression models: multiple linear regression (MLR), neural network (NN), and light gradient boosting machine (LightGBM). We used correlation coefficient(𝑐𝑜𝑟𝑟) and adjusted coefficient of determination between evaluated and estimated impression values to evaluate estimation performance. As a result, the LightGBM with the design parameters showed the highest mean 𝑐𝑜𝑟𝑟 for all participants. It was thus found that the design parameters are effective in estimating the garment impression with LightGBM.