Eun Yi Kim

Automatic video segmentation using genetic algorithms

The current paper proposes a genetic algorithm (GA)-based segmentation method that can automatically extract and track moving objects. The proposed method mainly consists of spatial and temporal segmentation; the spatial segmentation divides each frame into regions with accurate boundaries, and the temporal segmentation divides each frame into background and foreground areas. The spatial segmentation is performed using individuals that evolve distributed genetic algorithms (DGAs). However, unlike standard DGAs, the individuals are initiated from the segmentation result of the previous frame, then only unstable individuals corresponding to actual moving object parts are evolved by mating operators. For the temporal segmentation, adaptive thresholding is performed based on the intensity difference between two consecutive frames. The spatial and temporal segmentation results are then combined for object extraction, and tracking is performed using the natural correspondence established by the proposed spatial segmentation method. The main advantages of the proposed method are twofold: first, proposed video segmentation method does not require any a priori information; second, the proposed GA-based segmentation method enhances the search efficiency and incorporates a tracking algorithm within its own architecture. These advantages were confirmed by experiments where the proposed method was successfully applied to well-known and natural video sequences.

Vision based interface system for hands free control of an intelligent wheelchair

BackgroundDue to the shift of the age structure in todays populations, the necessities for developing the devices or technologies to support them have been increasing. Traditionally, the wheelchair, including powered and manual ones, is the most popular and important rehabilitation/assistive device for the disabled and the elderly. However, it is still highly restricted especially for severely disabled. As a solution to this, the Intelligent Wheelchairs (IWs) have received considerable attention as mobility aids. The purpose of this work is to develop the IW interface for providing more convenient and efficient interface to the people the disability in their limbs.MethodsThis paper proposes an intelligent wheelchair (IW) control system for the people with various disabilities. To facilitate a wide variety of user abilities, the proposed system involves the use of face-inclination and mouth-shape information, where the direction of an IW is determined by the inclination of the users face, while proceeding and stopping are determined by the shapes of the users mouth. Our system is composed of electric powered wheelchair, data acquisition board, ultrasonic/infra-red sensors, a PC camera, and vision system. Then the vision system to analyze users gestures is performed by three stages: detector, recognizer, and converter. In the detector, the facial region of the intended user is first obtained using Adaboost, thereafter the mouth region is detected based on edge information. The extracted features are sent to the recognizer, which recognizes the face inclination and mouth shape using statistical analysis and K-means clustering, respectively. These recognition results are then delivered to the converter to control the wheelchair.Result & conclusionThe advantages of the proposed system include 1) accurate recognition of users intention with minimal user motion and 2) robustness to a cluttered background and the time-varying illumination. To prove these advantages, the proposed system was tested with 34 users in indoor and outdoor environments and the results were compared with those of other systems, then the results showed that the proposed system has superior performance to other systems in terms of speed and accuracy. Therefore, it is proved that proposed system provided a friendly and convenient interface to the severely disabled people.

Automatic textile image annotation by predicting emotional concepts from visual features

This paper presents an emotion prediction system that can automatically predict certain human emotional concepts from a given textile. The main application motivating this study is textile image annotation, which has recently rapidly expanded in relation to the Web. In the proposed method, color and pattern are used as cues to predict the emotional semantics associated with an image, where these features are extracted using a color quantization and a multi-level wavelet transform, respectively. The extracted features are then applied to three representative classifiers: K-means clustering, Naive Bayesian, and a multi-layered perceptron (MLP), all of which are widely used in data mining. When evaluating the proposed emotion prediction method using 3600 textile images, the MLP produces the best performance. Thereafter, the proposed MLP-based method is compared with other methods that only use color or pattern, and the proposed method shows the best performance with an accuracy of above 92%. Therefore, the results confirm that the proposed method can be effectively applied to the commercial textile industry and image retrieval.

Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating

This paper presents the novel development of an embedded system that aims at digital TV content recommendation based on descriptive metadata collected from versatile sources. The described system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. TV content items are ranked using a combined multimodal approach integrating classification-based and keyword-based similarity predictions so that a user is presented with a limited subset of relevant content. Observable user behaviors are discussed as instrumental in user profiling and a formula is provided for implicitly estimating the degree of user appreciation of content. A new relation-based similarity measure is suggested to improve categorized content rating precision. Experimental results show that our system can recommend desired content to users with significant amount of accuracy.

Emotion-Based textile indexing using colors and texture

For a given product or object, predicting human emotions is very important in many business, scientific and engineering applications. There has been a significant amount of research work on the image-based analysis of human emotions in a number of research areas because human emotions are usually dependent on human vision. However, there has been little research on the computer image processing-based prediction, although such approach is naturally very appealing. In this paper, we discuss challenging issues in how to index images based on human emotions and present a heuristic approach to emotion-based image indexing. The effectiveness of image features such as colors, textures, and objects (or shapes) varies significantly depending on the types of emotion or image data. Therefore, we propose adaptive and selective techniques. With respect to six adverse pairs of emotions such as weak-strong, we evaluated the effectiveness of those techniques by applying them to the set of about 160 images in a commercial curtain pattern book obtained from the Dongdaemoon textile shopping mall in Seoul. Our preliminary experimental results showed that the proposed adaptive and selective strategies are effective and improve the accuracy of indexing significantly depending on the type of emotion.

Intelligent wheelchair (IW) interface using face and mouth recognition

between the user and the wheelchair. To facilitate a wide variety of user abilities, the proposed system uses faceinclination and mouth-shape information as users intention, where the direction of an IW is determined by the inclination of the users face, while proceeding and stopping are determined by the shape of the users mouth. This mechanism requires minimal motion, thereby making the system more comfortable and adaptable for the severely disabled. Furthermore, to fully guarantee users safety, the 10 range-sensors are used to detect obstacles in environment and avoid them. To assess the effectiveness of the proposed IW, it was tested with 34 users and the results show that it can provide a user unable to drive a standard joystick with friendly and convenient system

HMM-Based gesture recognition for robot control

In this paper, we present a gesture recognition system for an interaction between a human being and a robot. To recognize human gesture, we use a hidden Markov model (HMM) which takes a continuous stream as an input and can automatically segments and recognizes human gestures. The proposed system is composed of three modules: a pose extractor, a gesture recognizer, and a robot controller. The pose extractor replaces an input frame by a pose symbol. In this system, a pose represents the position of users face and hands. Thereafter the gesture recognizer recognizes a gesture using a HMM, which performs both segmentation and recognition of the human gesture simultaneously [6]. Finally, the robot controller handles the robot as transforming the recognized gesture into robot commands. To assess the validity of the proposed system, we used the proposed recognition system as an interface to control robots, RCB-1 robot. The experimental results verify the feasibility and validity of the proposed system.

Genetic algorithms for video segmentation

The current paper presents a new genetic algorithm (GA)-based method for video segmentation. The proposed method is specifically designed to enhance the computational efficiency and quality of the segmentation results compared to standard GAs. The segmentation is performed by chromosomes that independently evolve using distributed genetic algorithms (DGAs). However, unlike conventional DGAs, the chromosomes are initiated using the segmentation results of the previous frame, instead of random values. Thereafter, only unstable chromosomes corresponding to moving object parts are evolved by crossover and mutation. As such, these mechanisms allow for effective solution space exploration and exploitation, thereby improving the performance of the proposed method in terms of speed and segmentation quality. These advantages were confirmed based on experiments where the proposed method was successfully applied to both synthetic and natural video sequences.

Emotion-based textile indexing using colors, texture and patterns

We propose a textile indexing system which can classify textile images based on human emotions. The emotions can be regarded as emotional reactions of human beings when they view specific textile images. The evaluation system starts with extracting features of textile images such as colors, texture and patterns using various image processing techniques. The proposed system utilizes both fuzzy rules and neural networks. The fuzzy rules are determined for six emotional features which can be formulated with respect to color and texture. On the other hand, the neural network is used for recognizing patterns which can be used in classifying textile images based on the 4 other emotional features. For the machine learning component of the system, we selected 70 subjects so that they could view and annotate 160 textile images using ten pairs of emotional features. The fuzzy rule based component of the system uses color features and texture features in order to predict six pairs of emotional features such as (warm, cold), (gay, sober), (cheerful, dismal), (light, dark), (strong, weak), and (hard, soft). The neural-network based component of the system can predict four pairs of emotional features such as (natural, unnatural), (dynamic, static), (unstable, stable) and (gaudy, plain). Our experimental results showed that the proposed system was effective for predicting human emotions based on textile images and improving the accuracy of indexing the textile images based on emotional features.

Discovering and Browsing of Power Users by Social Relationship Analysis in Large-Scale Online Communities

Community Web sites on specific topics are very popular on the Web. Some active Web communities are so huge and diverse that it becomes a challenging issue to efficiently mine meaningful knowledge from the Web communities. In this paper, we develop schemes to discover and browse power users by their activities in online communities. The novelties of this work are two-fold. 1) We define new features to describe users social activities: statistical features to summarize userspsila activities and relationship-based features to describe interactions between individual users. And, through extensive user study and experiments to compare the performances of the ranking models based on various features, it is shown that the cross reference (CR) feature plays an unique and effective role in discovering power users in post-dominant online communities. 2) Thereafter, we develop a novel interface for effective exploration of power users based on the CR rank. Two schemes are proposed to incrementally navigate a large number of candidate power users with higher CR values: threshold-based navigation and traversal-based one. Experimental results shows that the proposed CR rank can be used for effective browsing of power users: about 70% precision is maintained while retrieving all the power users, which means that we can discover all the power users with relatively small number of false alarms.