Purpose: To evaluate the feasibility of iPad for visual impairment by utilizing the magnification functions on the iPad compared to conventional optical magnifier when looking at near object. Methods: 18 participants (9 males and 9 females, 42 through to 71 years of age, with a mean age of 63.2 ± 12.3 years.)were asked to perform timed tasks of viewing bills and photographs to find the certain information. They were then asked to compare between their optical aid and the iPad in terms of comfort, ease of use and clarity when viewing bills and photographs. Results: Participants spent significantly less time completing the tasks when using their own optical magnifiers compared to the iPad (p<0.01). In the photograph viewing task, optical aids were perceived to be significantly clearer than the iPad (p=0.02). Conclusion: Overall, the iPad were not shown to be significantly more comfortable or offer more visual clarity when compared to traditional optical magnifying systems. Conversely, we also could not show that the iPad is any worse than traditional optical magnifying systems. Due to the likely benefits of training on the outcome, the iPad may have great potential as a low vision aid, but it may also need additional accessory such as stand for better image stabilization.

Achievement degree can be improved by display offering more effective process about cognitive, pattern recognition than making observers use memory, integration, and cognitive process of control. And this research is proved by several scholars' researches [4][5][7][9]. In this study, researches was conducted about cognition according to layout of object in instrument panel. To decide layout of instrument panel, Cognition value was preferentially decided about all location. And then, objects are arranged to correct position of low cognition following the inferior procedure about each location. As a result, we get conclusion that gauge location is taken in high importance order through mechanical importance degree bringing huge damage during driving forklift-truck.

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

Collision avoidance is a fundamental and important task of an autonomous mobile robot for safe navigation in real environments with high uncertainty. Obstacles are classified into static and dynamic obstacles. It is difficult to avoid dynamic obstacles because the positions of dynamic obstacles are likely to change at any time. This paper proposes a scheme for vision-based avoidance of dynamic obstacles. This approach extracts object candidates that can be considered moving objects based on the labeling algorithm using depth information. Then it detects moving objects among object candidates using motion vectors. In case the motion vectors are not extracted, it can still detect the moving objects stably through their color information. A robot avoids the dynamic obstacle using the dynamic window approach (DWA) with the object path estimated from the information of the detected obstacles. The DWA is a well known technique for reactive collision avoidance. This paper also proposes an algorithm which autonomously registers the obstacle color. Therefore, a robot can navigate more safely and efficiently with the proposed scheme.

In this paper, we introduce visual contexts in terms of types and utilization methods for robust object recognition with intelligent mobile robots. One of the core technologies for intelligent robots is visual object recognition. Robust techniques are strongly required since there are many sources of visual variations such as geometric, photometric, and noise. For such requirements, we define spatial context, hierarchical context, and temporal context. According to object recognition domain, we can select such visual contextx. We also propose a unified framework which can utilize the whole contexts and validates it in real working environment. Finally, we also discuss the furture research directions of object recognition technologies for intelligent robots.