Kwang-Hyun Park

Continuous gesture recognition system for Korean sign language based on fuzzy logic and hidden Markov model

Reports some early results of our study on continuous Korean sign language (KSL) recognition using color vision. In recognizing gesture words such as sign language, it is very difficult to segment a continuous sign into individual sign words since the patterns are very complicated and diverse. To solve this problem, we disassemble the KSL into 18 hand motion classes according to their patterns and represent the sign words as some combination of hand motions. Observing the speed and the change of speed of hand motion and using fuzzy partitioning and state automata, we reject unintentional gesture motions such as preparatory motion and meaningless movement between sign words. To recognize 18 hand motion classes we adopt the hidden Markov model. Using these methods, we recognize 15 KSL sentences and obtain 94% recognition ratio.

A User Adaptation Method for Hand Shape Recognition Using Wrist-Mounted Camera

This paper proposes a robust hand segmentation method using view-invariant characteristic of a wrist-mounted camera, and deals with a hand shape recognition system based on segmented hand information. We actively utilize the advantage of the proposed camera device that provides view-invariant images physically, and segment hand region using a Bayesian rule based on adaptive histograms. We construct HSV histograms from RGB histograms, and update HSV histograms using hand region information from a current image. We also propose a user adaptation method by which hand models gradually approach user-dependent models from user-independent models as the user uses the system. The proposed method was evaluated using 16 Korean manual alphabet, and we obtained increases of 27.91% in recognition success rate.

Multi sensors-based approach for intention reading with soft computing techniques

Humans intention plays a key role in human-machine interaction as in the case of a robot serving for a handicapped person. The quality of a service robot will be much enhanced if the robot can infer the humans intension during the interaction process. In this paper, we propose a soft computing-based technique to read a users intention using some multisensors-based approach. We have tested the technique by a scenario of serving a drink to the user. With such force/torque or vision sensor, the robot can effectively infer the users intention to drink the beverage or not to drink. As an application, this intention technique is employed for building a rehabilitation robot, called KARES II, to perform various human-friendly human-robot interaction.

LEARNING TECHNIQUES IN SERVICE ROBOTIC ENVIRONMENT

This presentation addresses the problems of realizing human-friendly man-machine interaction in service robotic environment with emphasis on learning capability. After briefly reviewing the issues of human-robot interaction and various learning techniques from engineering point of view, we report our experiences in case studies where some learning techniques are successfully implemented in service robotic environment and discuss open issues of learning systems such as adaptivity and life-long learning capability.

Buffer Management Scheme for Interactive Video Streaming

In this paper, we propose a buffer management scheme suitable for interactive multimedia services. We consider a typical delay optimization environment so that receiver buffer lengths vary according to the round trip time estimation. In this environment, we propose an optimization technique for minimizing the loss of information that may occur when a reduced buffer length forces I/P/B frames in the buffer to drop. We modeled our problem as a Knapsack Problem for which we used dynamic programing in order to find an approximate solution. The proposed technique is compared with the existing buffer management techniques. Through simulation studies, we found that our approach could increase PSNR, which is important to video quality.

Personalized Media Control Method using Probabilistic Fuzzy Rule-based Learning

Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user`s intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user`s explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.