Inha University
RANSAC versus CS-RANSAC
Jo, Geun Sik (Inha University) | Lee, Kee-Sung (INHA University) | Chandra, Devy (INHA University) | Jang, Chol-Hee (INHA University) | Ga, Myung-Hyun (INHA University)
A homography matrix is used in computer vision field to solve the correspondence problem between a pair of stereo images. RANSAC algorithm is often used to calculate the homography matrix by randomly selecting a set of features iteratively. CS-RANSAC algorithm in this paper converts RANSAC algorithm into two-layers. The first layer is addressing sampling problem which we can describe our knowledge about degenerate features by mean of Constraint Satisfaction Problems (CSP). By dividing the input image into a N X N grid and making feature points into discrete domains, we can model the image into the CSP model to efficiently filter out degenerate feature samples using CSP in the first layer, so that computer has knowledge about how to skip computing the homography matrix in the model estimation step for the second layer. The experimental results show that the proposed CS-RANSAC algorithm can outperform the most of variants of RANSAC without sacrificing its execution time.
A Unified Framework for Augmented Reality and Knowledge-Based Systems in Maintaining Aircraft
Jo, Geun-Sik (Inha University) | Oh, Kyeong-Jin (INHA University) | Ha, Inay (INHA University) | Lee, Kee-Sung (INHA University) | Hong, Myung-Duk (INHA University) | Neumann, Ulrich (University of Southern California) | You, Suya (University of Southern California)
Aircraft maintenance and training play one of the most important roles in ensuring flight safety. The maintenance process usually involves massive numbers of components and substantial procedural knowledge of maintenance procedures. Maintenance tasks require technicians to follow rigorous procedures to prevent operational errors in the maintenance process. In addition, the maintenance time is a cost-sensitive issue for airlines. This paper proposes intelligent augmented reality (IAR) system to minimize operation errors and time-related costs and help aircraft technicians cope with complex tasks by using an intuitive UI/UX interface for their maintenance tasks. The IAR system is composed mainly of three major modules: 1) the AR module 2) the knowledge-based system (KBS) module 3) a unified platform with an integrated UI/UX module between the AR and KBS modules. The AR module addresses vision-based tracking, annotation, and recognition. The KBS module deals with ontology-based resources and context management. Overall testing of the IAR system is conducted at Korea Air Lines (KAL) hangars. Tasks involving the removal and installation of pitch trimmers in landing gear are selected for benchmarking purposes, and according to the results, the proposed IAR system can help technicians to be more effective and accurate in performing their maintenance tasks.