KEYWORDS: Eye models, Education and training, Data modeling, Machine learning, Eye, RGB color model, Information technology, Information science, Diagnostics, Color
Anemia is one of the most common social problem, so much so that one in ten people is said to be anemic. Anemia can be diagnosed by observing the coloration of the eyelid conjunctiva or by drawing blood. However, both cases require specialized knowledge and are not easy methods in terms of time and cost. This paper proposes a new method for estimating anemia from facial images. First, the eyelid conjunctiva region is extracted using a cascade classifier. Next, the obtained images were input into a model using a convolutional neural network (CNN), which had already learned the characteristics of anemia, to create a system that automatically estimates the state of anemia. Compared to a model using the standard multilayer perceptron (MLP), the standard MLP-based model had anemia estimation accuracy of 66.7%, while the CNN-based model had an accuracy of 87.5%. The results confirmed the effectiveness of the proposed method.
We propose an autonomous flight control method for secure navigation of Unmanned Aerial Vehicles (UAVs). We focus on the relation between flight command given to UAV and the response. According to the flight commands sampled in world coordinate system, we acquire the corresponding 3D moving vectors, which represent moving distances and orientations in 3D space, by employing motion capture system. As the input/output relation implemented in UAV is a black box, our approach acquires the sequential input/output relation between flight command and 3D moving vector by using recurrent neural network. Given sequential flight commands, the model predicts the 3D moving vectors that correspond to the command sequence. We demonstrate that our proposed method yields the desirable command sequence for autonomous control.
Mobile robots used in public spaces require safe path planning, such as avoiding obstacles. In this paper, we propose a method of generating a drivable path in a graph from a drivable region excluding moving obstacles, small obstacles, regions such as grooves and slopes that hinder traveling by LiDAR. Then, the effectiveness of the proposed method is shown by experiments.
Unmanned aerial vehicles (UAVs) are applied to various applications due to its maneuverable flight. Formation flight composed of multiple UAVs has obvious advantages in accomplishing effectively and speedily a task. We propose a formation control of multiple UAVs for omnidirectional patrolling. The formation flight adopts a leader-follower structure. Assumed that motion capture system detects the 3D configuration of formation flight, our method stably and safely controls the formation based on geometric constraint in 3D space. When we fly the leader UAV by remote control according to given flight trajectory, the positions and orientations of follower UAVs are automatically adjusted by our control system so that the leader-follower structure retains the geometric formation. We demonstrate that our proposed method offers excellent formation flight to provide practical omnidirectional patrolling.
We aim to provide a large-scale point cloud-based 3D map that reflects the internal structure in a building for autonomous mobile robots. We propose a new method that iteratively updates the 3D map based on self-localization in dynamic environment. We assume that a patrol robot collects 3D points required for constructing a 3D map in database. We adopt multi-layer NDT (Normal Distributions Transform), which handles multiple horizontal and multiple vertical scan lines, to robustly estimate the robot’s 3D position in real environments. Our proposed method estimates a specified floor in the building and determines a 2D localization on the floor. Based on the self-localization, the method detects depth variations by taking advantage of 3D LiDAR. Once our method detects some dynamic changes on patrol, it replaces the previous 3D points corresponding to the space in 3D map with the latest 3D points. As our approach estimates an accurate self-localization in the 3D map, the 3D map updated by the method is seamless without giving uncomfortable feeling. We demonstrate that our iterative update method is an effective way of successively renewing the 3D map for inside a building.
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