• Proceedings of the Korean Society of Computer Information Conference
• /
• 2010.07a
• /
• pp.151-152
• /
• 2010

WWW images locate in structural, networking documents, so the importance of a word can be indicated by its location, frequency. There are two patterns for multi-cues ingegration annotation. The multi-cues integration algorithm shows initial promise as an indicator of semantic keyphrases of the web images. The latent semantic automatic keyphrase extraction that causes the improvement with the usage of multi-cues is expected to be preferable.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.589-590
• /
• 2010

All these images consist a considerable database. What's more, the semantic meanings of images are well presented by the surrounding text and links. But only a small minority of these images have precise assigned keyphrases, and manually assigning keyphrases to existing images is very laborious. Therefore it is highly desirable to automate the keyphrases extraction process. In this paper, we first introduce WWW image annotation methods, based on low level features, page tags, overall word frequency and local word frequency. Then we put forward our method of multi-cues integration image annotation. Also, show multi-cue image annotation method is more superior than other method through an experiment.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06b
• /
• pp.456-458
• /
• 2012

손 추적은 HCI 분야에 있어 손쉽게 이용 가능한 정보 전달 방식 중 하나이며 현재까지도 다양한 연구가 진행되고 있다. 이러한 연구의 공통점은 멀티 큐(Multi-cue)를 기반으로 추적한다는 것이다. 멀티 큐를 사용할 경우 큐들을 어떻게 효율적으로 결합하느냐에 따라 추적 성능이 달라진다. 본 논문에서는 강인한 손 추적을 위해 MSEPF(Mean Shift Embedded Particle Filter) 알고리즘에서 적응적인 멀티 큐 통합 방법을 제안한다. 이는 MSEPF 내부에서 각 파티클에 대한 가중치를 적용할 때, 큐들의 계수를 불확실성 기반으로 계산하여 사용하는 것으로 기존의 멀티 큐 통합 방식의 손 추적보다 강인한 추적을 가능하게 한다. 본 논문에서는 컬러, 깊이 정보에서 얻을 수 있는 피부색, 모션, 깊이 정보 기반 큐를 활용하여 손 추적을 수행하며, 실험 결과, 제안하는 방법은 갑작스런 환경 변화에도 강인한 성능을 보였다.

• KIISE Transactions on Computing Practices
• /
• v.23 no.2
• /
• pp.104-109
• /
• 2017

We propose a new machine learning algorithm which analyzes daily activity patterns of users from multi-modal wearable sensor data. The proposed model learns and extracts activity patterns using input from wearable devices in real-time. Inspired by cue integration of human's property, we constructed gated multi-modal neural networks which integrate wearable sensor input data selectively by using gate modules. For the experiments, sensory data were collected by using multiple wearable devices in restaurant situations. As an experimental result, we first show that the proposed model performs well in terms of prediction accuracy. Then, the possibility to construct a knowledge schema automatically by analyzing the activation patterns in the middle layer of our proposed model is explained.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.4
• /
• pp.79-86
• /
• 2008

As the rapid development of the Internet, the embedded images in HTML web pages nowadays become predominant. For its amazing function in describing the content and attracting attention, images become substantially important in web pages. All these images consist a considerable database. What's more, the semantic meanings of images are well presented by the surrounding text and links. But only a small minority of these images have precise assigned keyphrases. and manually assigning keyphrases to existing images is very laborious. Therefore it is highly desirable to automate the keyphrases extraction process. In this paper, we first introduce WWW image annotation methods, based on low level features, page tags, overall word frequency and local word frequency. Then we put forward our method of multi-cues integration image annotation. Also, show multi-cue image annotation method is more superior than other method through an experiment.

• Journal of KIISE:Software and Applications
• /
• v.37 no.1
• /
• pp.80-84
• /
• 2010

Tracking hands is an essential technique for hand gesture recognition which is an efficient way in Human Computer Interaction (HCI). Recently, many researchers have focused on hands tracking using a 3D hand model and showed robust tracking results compared to using 2D hand models. In this paper, we propose a novel 3D hand tracking method based on a coupled particle filter. This provides robust and fast tracking results by estimating each part of global hand poses and local finger motions separately and then utilizing the estimated results as a prior for each other. Furthermore, in order to improve the robustness, we apply a multi-cue based method by integrating a color-based area matching method and an edge-based distance matching method. In our experiments, the proposed method showed robust tracking results for complex hand motions in a cluttered background.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.100.3-100
• /
• 2002

This paper presents the method for detection and tracking of multiple humans robustly in mobile platform. The perception of human is performed in real time through the processing of images acquired from a moving stereo vision system. We performed multi-cue integration such as human shape, skin color and depth information to detect and track each human in moving background scene. Human shape is measured by edge-based template matching on distance transformed image. Improving robustness for human detection, we apply the human face skin color in HSV color space. And we could increase the accuracy and the robustness in both detection and tracking by applying random sampling stochastic estimati...