Manbae Kim

Depth Scaling of Multiview Images for Automultiscopic 3D Monitors

In this paper, we present a depth scaling method for multiview images that could provide an effective control of stereoscopic depth range. Unlike the previous works that change a camera configuration, our proposed method utilizes depth data in order to carry out the scaling of a depth range requested by users. In particular, our method can deal with multiview images captured by multiple cameras, and can be expanded from stereoscopic to multiview images. Our experimental results tested on automultiscopic 3D displays show that the perceived depth is appropriately scaled according to a users preferred depth.

Multiview Video Service Framework for 3D Mobile Devices

Multiview broadcasting has recently gained much attraction from academic and commercial fields because it can deliver the immersive viewing of natural scenes. Therefore, various researches implementing multi-view video broadcasting have been conducted with a variety of different system architectures. However, the previous systems have focused on wired broadcasting. On the contrary, our system is designed to be applied to mobile 3DTV broadcasting. We present the system adaptable to mobile clients where the load on the client is reduced as much as possible. MPEG-21 multiview DIA description schemes are presented for the efficient adaptation between the serer and client.

Interactive Multi-View Video Adaptation for 3DTV

This paper presents the descriptions related to users preferences for multi-view video presentation, which can be utilized in MPEG-21 digital item adaptation. Our description tools support the functionalities for presentation preferences and terminal capabilities. The description of presentation preferences supports users preference for the view presentation such as view selection, 3D depth adjustment, and special visual effects as well as the multi-view camera geometry conversion. Furthermore, the description of terminal capabilities supports capabilities for various display devices. Therefore, users can enjoy among monoscopic, stereoscopic, and multiscopic video they prefer. The syntax of the descriptions is represented in XML (extensible Markup Language) schema. We offer an interactive communication between DIA server and client through these descriptions related to users preferences. Their feasibility has been examined and verified in our experiments, where test sequences are adapted by view preferences.

Resource consumption-aware QoS in cluster-based VOD servers

For Video-On-Demand (VOD) systems, it is important to provide Quality of Service (QoS) to more clients under limited resources. In this paper, the performance scalability in cluster-based VOD servers is studied with several grouping configurations of cluster nodes. To find performance bottlenecks, the monitoring functions are employed and the maximum QoS streams are measured under the various requests including VCR functions. To support more user friendly interface, an embedded set-top model is suggested for the QoS of TV clients. From our detailed experiment results, a new admission control method is proposed that is based on available system resources and the actual amount of resource consumed for QoS streams. The proposed method provides not only more scalable QoS in cluster-based VOD servers but also the enhancement of resource utilization by guaranteeing the maximum number of QoS streams.

Disparity-based Depth Scaling of Multiview Images

In this paper, we present a depth scaling method for multiview images that could provide an 3D depth that a user prefers. Unlike previous works that change a camera configuration, the proposed method utilizes depth data in order to carry out the scaling of a depth range requested by users. From multivew images and their corresponding depth data, depth data is transformed into a disparity and the disparity is adjusted in order to control the perceived depth. In particular, our method can deal with multiview images captured by multiple cameras, and can be expanded from stereoscopic to multiview images. Based upon a DSCQS subjective evaluation test, our experimental results tested on an automultiscopic 3D display show that the perceived depth is appropriately scaled according to user’s preferred depth.

People Counting Method using Moving and Static Points of Interest

Among available people counting methods, map-based approaches based on moving interest points have shown good performance. However, the stationary people counting is challenging in such methods since all static points of interest are considered as background. To include stationary people in counting, it is needed to discriminate between the static points of stationary people and the background region. In this paper, we propose a people counting method based on using both moving and static points. The proposed method separates the moving and static points by motion information. Then, the static points of the stationary people are classified using foreground mask processing and point pattern analysis. The experimental results reveal that the proposed method provides more accurate count estimation by including stationary people. Also, the background updating is enabled to solve the static point misclassification problem due to background changes.

No-Reference Image Quality Assessment Using Complex Characteristics of Shearlet Transform

화질 평가 방법은 그동안 많은 방법이 소개되어 왔다. 특히 우수한 성능을 보여주는 무참조 평가에서 기법에서 발전이 지속되어 왔다. 본 논문에서는 쉬어렛 영역에서 자연영상의 통계적 특성에 기반한 무참조 영상화질 평가 방법을 제안한다. 제안 방법은 쉬어릿 계수의 통계 특성으로부터 왜곡에 민감한 특징을 추출한다. 쉬어렛 변환의 복소수 계수로부터 위상과 크기 특징을 얻어낸다. 또한 쉬어렛 변환은 다양한 스케일로 영상을 분석할 수 있기 때문에, 스케일간의 계수의 의존성에 대한 왜곡의 영향을 분석한다. 화질 예측을 위해서 특징들은 SVM(support vector machine)을 이용하여 영상 왜곡 분류 및 화질 예측에 활용된다. 실험결과는 제안 방법이 주관적 평가와의 높은 상관도를 보여주고, 또한 기존 참조 및 무참조 방법보다 우수한 성능을 보여준다.

Detecting Foreground Objects Under Sudden Illumination Change Using Double Background Models

배경 모델과 배경 차분화로 구성되어 있는 전경객체 추출은 다양한 컴퓨터 비젼 응용에서 중요한 기능이다. 조명 변화를 고려하지 않은 기존 방법들은 급격한 조명 변화에서는 성능이 저하된다. 본 레터에서는 이 문제를 해결할 수 있는 조명 변화에 강인한 배경 모델링 방법을 제안한다. 제안 방법은 다른 적응률을 가진 두 개의 배경 모델을 사용함으로써 조명 조건에 신속하게 적응할 수 있다. 본 논문의 제안 방법은 non-parametric 기법으로서 실험에서는 기존 non-parametric 기법들보다 우수한 성능 및 낮은 복잡도를 보여줌을 증명하였다.

Estimating Human Size in 2D Image for Improvement of Detection Speed in Indoor Environments

The performance of human detection system is affected by camera location and view angle. In 2D image acquired from such camera settings, humans are displayed in different sizes. Detecting all the humans with diverse sizes poses a difficulty in realizing a real-time system. However, if the size of a human in an image can be predicted, the processing time of human detection would be greatly reduced. In this paper, we propose a method that estimates human size by constructing an indoor scene in 3D space. Since the human has constant size everywhere in 3D space, it is possible to estimate accurate human size in 2D image by projecting 3D human into the image space. Experimental results validate that a human size can be predicted from the proposed method and that machine-learning based detection methods can yield the reduction of the processing time. Keyword : human size estimation, camera calibration, image projection, depth map a) 강원대학교 컴퓨터정보통신공학과(Dept. of Computer and Communications Engineering, Kangwon National University) ‡Corresponding Author : 김만배(Manbae Kim) E-mail: manbae@kangwon.ac.kr Tel: +82-33-250-6395 ORCID: http://orcid.org/0000-0002-4702-8276 ※2015년도 강원대학교 대학회계 학술연구조성비로 연구하였음 (관리번호-520150466). ・Manuscript received January 14, 2016; Revised March 3, 2016; Accepted March 3, 2016. 일반논문 (Regular Paper) 방송공학회논문지 제21권 제2호, 2016년 3월 (JBE Vol. 21, No. 2, March 2016) http://dx.doi.org/10.5909/JBE.2016.21.2.252 ISSN 2287-9137 (Online) ISSN 1226-7953 (Print) 길종인 외 1인: 실내 환경에서 검출 속도 개선을 위한 2D 영상에서의 사람 크기 예측 253 (Jong in Gil et al.: Estimating Human Size in 2D Image for Improvement of Detection Speed in Indoor Environments)

Enhancement of Saliency Map Using Motion and Affinity Model

Over the past decades, a variety of spatial saliency methods have been introduced. Recently, motion saliency has gained much interests, where motion data estimated from an image sequence are utilized. In general, motion saliency requires reliable motion data as well as image segmentation for producing satisfactory saliency map which poses difficulty in most natural images. To overcome this, we propose a motion-based saliency generation that enhances the spatial saliency based on the combination of spatial and motion saliencies as well as motion complexity without the consideration of complex motion classification and image segmentation. Further, an affinity model is integrated for the purpose of connecting close-by pixels with different colors and obtaining a similar saliency. In experiment, we performed the proposed method on eleven test sets. From the objective performance evaluation, we validated that the proposed method produces better result than spatial saliency based on objective evaluation as well as ROC test.