Kue-Bum Lee

An Implementation of Multi-Modal Game Interface Based on PDAs

In computer animation and interactive computer games, gesture and speech modality can be a powerful interface between humans and computers. In this paper, we propose a personal digital assistant (PDA)- based multi-modal network game interface using speech, gesture and touch sensations. To verify the validity of our approach, we implement a multi-modal omok game using TCP/IP on a PDA network. The experimental results using the proposed multi-modal network game resulted in an average recognition rate of 97.4%, and accordingly as the weaknesses of uni- modality, such as incorrect command processing by recognition error, are offset by the strengths of other modalities, the user can enjoy a more interactive mobile game interface in any given environment.

An Implementation of Leaf Recognition System Based on Leaf Contour and Centroid for Plant Classification

In this paper, we propose a leaf recognition system based on the leaf contour and centroid that can be used for plant classification. The proposed approach uses frequency domain data by performing a Fast Fourier transform (FFT) for the leaf recognition system. Twenty leaf features were extracted for leaf recognition. First, the distance between the centroid and all points on the leaf contours were calculated. Second, an FFT was performed using the calculated distances. Ten features were extracted using the calculated distances, FFT magnitude, and its phase. Ten features were also extracted based on the digital morphological features using four basic geometric features and five vein features. To verify the validity of the approach, images of 1907 leaves were used to classify 32 kinds of plants. In the experimental results, the proposed leaf recognition system showed an average recognition rate of 95.44 %, and we can confirm that the recognition rate of the proposed advanced leaf recognition method was better than that of the existed leaf recognition method.

An implementation of SVM-based gaze recognition system using advanced eye region detection

There have been many recent studies on gaze recognition in the field of Human-Computer Interaction (HCI). Gaze recognition and other biomedical signals will be a very natural and intuitive part of Human-Computer Interaction. In studies on gaze recognition, identifying the user is the most applicable task, and it has had a lot of attention from many different studies. Most existing research on gaze recognition has problems with universal use because the process requires a head-mounted infrared Light Emitting Diode (LED) and a camera, both expensive pieces of equipment. Cheaper alternatives like webcams have the disadvantage of poor recognition performance. This paper proposes and implements the Support Vector Machine-based (SVM) gaze recognition system using one webcam and an advanced eye region detection method. In this paper, we detected the face and eye regions using Haar-like features and the AdaBoost learning algorithm. Then, we used a Gabor filter and binarization for advanced eye region detection. We implemented a Principal Component Analysis (PCA) and Difference Image Entropy-based (DIE) gaze recognition system for the performance evaluation of the proposed system. In the experimental results, the proposed system shows 97.81% recognition of 4 directions, 92.97% recognition of 9 directions, demonstrating its effectiveness.

An Implementation of Gaze Recognition System Based on SVM

ABSTRACT The researches about gaze recognition which current user gazes and finds the location have increasingly developed to have many application. The gaze recognition of existence all about researches have got problems because of using equipment that Infrared(IR) LED, IR camera and head-mounted of high price. This study propose and implement the gaze recognition system based on SVM using a single PC Web camera. The proposed system that divide the gaze location of 36 per 9 and 4 to recognize gaze location of 4 direction and 9 direction recognize users gaze. Also, the proposed system had apply on image filtering method using difference image entropy to improve performance of gaze recognition. The propose system was implements experiments on the comparison of proposed difference image entropy gaze recognition system, gaze recognition system using eye corner and eyes center and gaze recognition system based on PCA to evaluate performance of proposed system. The experimental results, recognition rate of 4 direction was 94.42% and 9 direction was 81.33% for the gaze recognition system based on proposed SVM. 4 direction was 95.37% and 9 direction was 82.25%, when image filtering method using difference image entropy implemented. The experimental results proved the high performance better than existed gaze recognition system.Keywords:Support Vector Machine : SVM, Gaze Recognition, Difference Image Entropy

A Pattern Recognition-Based Multi-Modal HCI Using 3 Sensory Channels

This paper describes a PDA-based MMCR (Multi- Modal Command Recognizer for PDA control and handling) using double-touching with a finger by coupling embedded speech and KSSL recognizer, and suggests an improved synchronization method between multi-modalities for simultaneous multi-modality, for a pattern recognition-based neo multi-modal HCI. The MMCR fuses and recognizes 16 word-based command models that are represented by stylus, speech and KSSI (Korean Standard Sign language), and then translates the recognition result into synthetic speech and visual illustration, for multi-modal PDA handling and interaction.