• Journal of Broadcast Engineering
• /
• v.19 no.3
• /
• pp.362-371
• /
• 2014

Blur variation caused by camera de-focusing provides a proper cue for depth estimation. Depth from Defocus (DFD) technique calculates the blur amount present in an image considering that blur amount is directly related to scene depth. Conventional DFD methods use two defocused images that might yield the low quality of an estimated depth map as well as a reconstructed infocused image. To solve this, a new DFD methodology based on infocused and defocused images is proposed in this paper. In the proposed method, the outcome of Subbaro's DFD is combined with a novel edge blur estimation method so that improved blur estimation can be achieved. In addition, a saliency map mitigates the ill-posed problem of blur estimation in the region with low intensity variation. For validating the feasibility of the proposed method, twenty image sets of infocused and defocused images with 2K FHD resolution were acquired from a camera with a focus control in the experiments. 3D stereoscopic image generated by an estimated depth map and an input infocused image could deliver the satisfactory 3D perception in terms of spatial depth perception of scene objects.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.16 no.3
• /
• pp.115-122
• /
• 2016

This paper proposes a system to detect driver's cognitive state by internal and external information of vehicle. The proposed system can measure driver's eye gaze. This is done by concept of information delivery and mutual information measure. For this study, we set up two web-cameras at vehicles to obtain visual information of the driver and front of the vehicle. We propose Gestalt principle based selective attention model to define information quantity of road scene. The saliency map based on gestalt principle is prominently represented by stimulus such as traffic signals. The proposed system assumes driver's cognitive resource allocation on the front scene by gaze analysis and head pose direction information. Then we use several feature algorithms for detecting driver's characteristics in real time. Modified census transform (MCT) based Adaboost is used to detect driver's face and its component whereas POSIT algorithms are used for eye detection and 3D head pose estimation. Experimental results show that the proposed system works well in real environment and confirm its usability.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.51 no.7
• /
• pp.157-168
• /
• 2014

We propose an novel artificial visual attention model that is capable of automatic detection and segmentation of saliency region on natural images in this paper. The proposed model is based on human visual perceptions in biological vision and contains there are main contributions. Firstly, we propose a novel framework of artificial visual attention model based on the opponent process theory using intensity and color features, and an entropy filter is designed to perceive salient regions considering the amount of information from intensity and color feature channels. The entropy filter is able to detect and segment salient regions in high segmentation accuracy and precision. Lastly, we also propose an adaptive combination method to generate a final saliency map. This method estimates scores about intensity and color conspicuous maps from each perception model and combines the conspicuous maps with weight derived from scores. In evaluation of saliency map by ROC analysis, the AUC of proposed model as 0.9256 approximately improved 15% whereas the AUC of previous state-of-the-art models as 0.7824. And in evaluation of salient region segmentation, the F-beta of proposed model as 0.7325 approximately improved 22% whereas the F-beta of previous state-of-the-art models.

• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.983-991
• /
• 2019

This study aims to analyze movies which contain various stories according to the size of their shots. To achieve this, it is needed to classify dataset according to the shot size, such as extreme close-up shots, close-up shots, medium shots, full shots, and long shots. However, a typical video storytelling is mainly composed of close-up shots, medium shots, full shots, and long shots, it is not an easy task to construct an appropriate dataset for extreme close-up shots. To solve this, we propose an image cropping method based on the region of interest (ROI) detection. In this paper, we use the face detection and saliency detection to estimate the ROI. By cropping the ROI of close-up images, we generate extreme close-up images. The dataset which is enriched by proposed method is utilized to construct a model for classifying shots based on its size. The study can help to analyze the emotional changes of characters in video stories and to predict how the composition of the story changes over time. If AI is used more actively in the future in entertainment fields, it is expected to affect the automatic adjustment and creation of characters, dialogue, and image editing.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.7
• /
• pp.3258-3266
• /
• 2011

This paper empirically explores how resources and family saliency of women entrepreneurs in IT business influence their business performance. Building up on prior research and Shelton[2006], we develop hypothesis on how the strategies of resolving wok family conflict relate to determinants of performance and business performance. The central hypothesis states that role sharing would be the strategy raising up the business performance. We also posit that the role sharing strategy would be adopted by women who show high saliency in both family and work. and women who have high levels of resources. Results suggest that financial resources have significant impact on role sharing strategy and the sales. The hypothesis of positive role sharing effect on the performance is not supported, indicating that combining family role might be an obstacle to business performance of women.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.445-450
• /
• 2008

본 논문에서는 객체의 인식성을 높이기 위해 가상의 지역 광원을 사용한 카툰 렌더링 기법을 제안한다. 지역 광원은 각 메쉬의 기하 정보를 분석하여 배치되며 객체의 지역적인 음영 대비를 증가시켜 객체의 모양과 특정이 눈에 잘 띄도록 한다. 하지만 지역 광원을 사용한 쉐이딩 기법은 객체의 일부 영역에서 갈라지고 불연속적인 음영을 만들어 이미지의 질적 하락을 초래한다. 이러한 현상을 막기 위하여 곡률과 샐리언시의 개념을 사용하여 영역의 특성에 따라 차등적으로 지역 광원을 객체에 적용하였다. 곡률은 해당 영역의 기하적 특성을 구분하여 지역 광원에 의한 음영 대비 증감을 조정하고, 샐리언시는 영역의 중요도를 판별하여 곡률이 쉐이딩에 미치는 가중치를 조절한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2013.11a
• /
• pp.244-245
• /
• 2013

본 논문에서는 회귀도 기반의 중요도 검출 기법을 제안한다. 제안하는 기법은 각 군집의 분포 정보를 이용하여 중요도를 검출한다. 우선, 이를 입력 영상에 군집 기법을 수행한다. 그리고 각 군집의 분포를 분석하여 각 군집에 대한 회귀도, 응집도, 그리고 중심밀집도를 추출한다. 마지막으로 회귀도, 응집도, 그리고 중심밀집도를 곱함으로써 중요도를 검출한다. 실험 결과 제안하는 알고리즘이 기존의 기법들 보다 중요도를 정확하게 검출하는 것을 확인할 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.11a
• /
• pp.259-260
• /
• 2014

본 논문에서는 영상에 존재하는 운동정보를 이용하여 관심맵을 얻는 방법을 제안한다. 운동의 크기, 방향, 그리고 영상의 색을 이용한다. 이를 이용함으로써 기존의 static image에서 구하는 관심맵보다 향상된 결과를 얻을 수 있다. 실험에서는 다양한 모션이 존재하는 영상을 이용하여 제안방법의 우수성을 증명하였다.

• Proceedings of the KIEE Conference
• /
• 2006.04a
• /
• pp.306-308
• /
• 2006

We propose an attention based object recognition system, to recognize object fast and robustly. For this we calculate visual stimulus degrees and make saliency maps. Through this map we find a strongly attentive part of image by stimulus degrees, where local features are extracted to recognize objects.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2014.11a
• /
• pp.254-255
• /
• 2014

본 논문에서는 중요도 지도에 기반한 관심 영역 동영상 압축 방법에 대해 고찰한다. 동영상 압축은 손실 프로세스이기 때문에 관심 영역에서의 정보 손실 최소화가 필요하며, 이를 위해 중요도 감지 과정에서 추출되는 중요도 지도의 신뢰도가 중요하다. 따라서 다양한 다른 기법의 중요도 지도 적용 결과를 비교함으로써 중요도 지도 추출 알고리즘의 요건에 대해 추론하고, 추출된 중요도 지도를 이용하여 적절하게 동영상을 부호화하는 방법에 대해 제안한다. 마지막으로 실험결과를 통해 보완되어야 할 부분을 제시한다.