We described a real-time hair segmentation method based on a fully convolutional network with the basic structure of an encoder–decoder. In one of the traditional computer vision techniques for hair segmentation, the mean shift and watershed methodologies suffer from inaccuracy and slow execution due to multi-step, complex image processing. It is also difficult to execute the process in real-time unless an optimization technique is applied to the partition. To solve this problem, we exploited Mobile-Unet using the U-Net segmentation model, which incorporates the optimization techniques of MobileNetV2. In experiments, hair segmentation accuracy was evaluated by different genders and races, and the average accuracy was 89.9%. By comparing the accuracy and execution speed of our model with those of other models in related studies, we confirmed that the proposed model achieved the same or better performance. As such, the results of hair segmentation can obtain hair information (style, color, length), which has a significant impact on human-robot interaction with people.

A connected component analysis from a binary image is a popular character segmentation method but occasionally fails to segment the characters owing to image noise and uneven illumination. A multimethod binarization scheme that incorporates two or more binary images is a novel solution, but selection of binarization methods has never been analyzed before. This paper reveals the best combination of binarization methods and parameters and presents an in-depth analysis of the multimethod binarization scheme for better character segmentation. We carry out an extensive quantitative evaluation, which shows a significant improvement over conventional single-method binarization methods. Experiment results of six binarization methods and their combinations with different test images are presented.

The increase in elderly population is emerging as a serious social issue. The coronavirus pandemic has increased the number of elderly people suffering from depression and loneliness owing to the lack of face-to-face activities. In this study, we developed an integrated system for the human-care robot service, considering cognitive and emotional support for elderly people, and verified its stability and usefulness in the real world. We recruited 40 elderly people for an apartment testbed environment experiment and two elderly people living alone for a long time participated in the experiment at their homes. Quantitative experimental results were analyzed by comparing service success rates and user satisfaction in two different test environments to verify the stability of the service. Qualitative evaluations were also conducted through surveys and interviews to assess the usefulness of the service.

In this paper, an algorithm for human head detection over a distance exceeding 2.5 m between a camera and an object is described. This algorithm is used for the control of a robot, which has the additional limits of a moving camera, moving objects, various face orientations, and unfixed illuminations. With these circumstances, only the assumption that human head and body contours have an omega (Omega) shape is made. In order to separate the background from these omega shapes, the three basic features of gray, color and edge are used, and combined. The skin color cue is very useful when the image stream is frontal and has large face regions, and additionally has no background objects similar to the skin color. The gray cue is also important when captured faces have a lower gray level than background objects. The edge cue is helpful when captured background objects have similar gray levels and colors to those of a head, but can be discriminated by edges. Since these three methods have roughly orthogonal failure results, they serve to complement each other. The next step is a splitting method between the head and body region using the proposed method. The final step is an ellipse fitting and a head region verification algorithm. The results of this algorithm provide robustness for head rotation, illumination changing, and variable head sizes. Furthermore, it is possible to carry out real time processing