• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제5권9호
• /
• pp.1613-1631
• /
• 2011

Several metrics have been reported in the literature to assess stereo image quality, mostly based on visual attention or human visual sensitivity based distortion prediction with the help of disparity information, which do not consider the combined aspects of human visual processing. In this paper, visual attention and depth assisted stereo image quality assessment model (VAD-SIQAM) is devised that consists of three main components, i.e., stereo attention predictor (SAP), depth variation (DV), and stereo distortion predictor (SDP). Visual attention is modeled based on entropy and inverse contrast to detect regions or objects of interest/attention. Depth variation is fused into the attention probability to account for the amount of changed depth in distorted stereo images. Finally, the stereo distortion predictor is designed by integrating distortion probability, which is based on low-level human visual system (HVS), responses into actual attention probabilities. The results show that regions of attention are detected among the visually significant distortions in the stereo image pair. Drawbacks of human visual sensitivity based picture quality metrics are alleviated by integrating visual attention and depth information. We also show that positive correlation with ground-truth attention and depth maps are increased by up to 0.949 and 0.936 in terms of the Pearson and the Spearman correlation coefficients, respectively.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2001년도 하계종합학술대회 논문집(4)
• /
• pp.239-242
• /
• 2001

As systems for real time computer vision are confronted with prodigious amounts of visual information, it has become a priority to locate and analyze just that information essential to the task at hand, while ignoring the vast flow of irrelevant detail. A method of achieving this is to using human visual attention mechanism. In this paper, short review of human visual attention mechanisms and some computation models of visual attention were shown. This paper can be used as the basic data for researches on development of visual attention system that can perform various complex tasks more efficiently.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제10권8호
• /
• pp.3791-3805
• /
• 2016

The visual attention mechanism includes 2 attention models, the bottom-up (B-U) and the top-down (T-D), the physiology of which have not yet been accurately described. In this paper, the visual attention mechanism is regarded as a Bayesian fusion process, and a visual attention model based on particle filter is proposed. Under certain particular assumed conditions, a calculation formula of Bayesian posterior probability is deduced. The visual attention fusion process based on the particle filter is realized through importance sampling, particle weight updating, and resampling, and visual attention is finally determined by the particle distribution state. The test results of multigroup images show that the calculation result of this model has better subjective and objective effects than that of other models.

• Journal of information and communication convergence engineering
• /
• 제10권3호
• /
• pp.300-306
• /
• 2012

An image attention model and its application to image quality assessment are discussed in this paper. The attention model is based on rarity quantification, which is related to self-information to attract the attention in an image. It is relatively simpler than the others but results in taking more consideration of global contrasts between a pixel and the whole image. The visual attention model is used to develop a local distortion predictor, named color visual differences predictor (CVDP), in color images in order to effectively detect luminance and color distortions.

• 한국IT서비스학회지
• /
• 제16권1호
• /
• pp.55-72
• /
• 2017

Many of the studies of visual attention that are currently underway are seeking ways to make application systems that can be used in practice, and obtained good results using not only simulated images but also real-world images. However, despite that previous studies of selective visual attention are models intended to implement the human vision, few experiments verified the models with actual humans and there is no standardized data nor standardized experimental method for actual images. Therefore, in this paper, we propose a new performance evaluation techniques necessary for evaluation of visual attention systems. We developed an evaluation method for evaluating the performance of the visual attention system through comparison with the results of the human experiments on visual attention. Human experiments on visual attention is an experiments where human beings are instinctively aware of the unconscious when images are given to humans. So it can be useful for evaluating performance of the bottom-up attention system. Also we propose a new selective attention system that guides the user to effectively detect ROI regions by using spatial and temporal features adaptively selected according to the input image. We evaluated the performance of proposed visual attention system through the developed performance evaluation method, and we could confirm that the results of the visual attention system are similar to those of the human visual attention.

• 정보처리학회논문지B
• /
• 제17B권1호
• /
• pp.55-62
• /
• 2010

인간의 시각 주의 시스템은 주어진 시각장면을 모두 다 처리하기보다는 주의가 집중되는 일정한 작은 영역들을 순간적으로 선택하여 그 부분만을 순차적으로 처리함으로써 복잡한 시각장면을 단순화시켜 쉽게 분석할 수 있는 능력을 가지고 있다. 본 논문에서는 주의 기반 시각정보 처리체계 시스템 구현을 위한 새로운 특징맵 구성 및 통합 방법을 제안한다. 제안하는 시스템에서는 시각특징으로서 색상, 명도, 방위, 형태 외에 2개의 스테레오 영상 쌍으로부터 얻을 수 있는 깊이 정보를 추가하여 사용하였다. 실험결과를 통해 깊이 정보를 사용함으로써 주의 영역의 정탐지율이 개선됨을 확인하였다.

• 한국의류학회지
• /
• 제44권5호
• /
• pp.789-809
• /
• 2020

Growth in online channels has created fierce competition; consequently, retailers have to invest an increasing amount of effort into attracting consumers. In this study, eye-tracking technology examined consumers' visual behavior to gain an understanding of information searching behavior in exploring product information for fashion products. Product attribute information was classified into two image-based elements (model image information and detail image information) and two text-based elements (basic text information, detail text information), after which consumers' visual behavior for each information element was analyzed. Furthermore, whether involvement affects consumers' information search behavior was investigated. The results demonstrated that model image information attracted visual attention the quickest, while detail text information and model image information received the most visual attention. Additionally, high-involvement consumers tended to pay more attention to detailed information while low-involvement consumers tended to pay more attention to image-based and basic information. This study is expected to help broaden the understanding of consumer behavior and provide implications for establishing strategies on how to efficiently organize product information for online fashion stores.

• 정보과학회 논문지
• /
• 제42권5호
• /
• pp.609-620
• /
• 2015

시청자들은 영상을 시청할 때 화면상 시각이 집중된 곳 주변의 정보를 영향력 있게 받아들일 가능성이 크다. 이러한 사실을 이용하여 최근 연구들은 시각 주의 모델을 영상 제작 및 평가 방법에 이용하고 있다. 본 연구에서는 실제로 사람들의 시각 주의도가 어떠한 인자에 영향을 많이 받는지, 또 시각 주의 모델은 구체적으로 어떠한 형태가 되는지를 통계적 실험 계획법을 이용하여 추정하였다. 분산 분석법을 이용하여 속도, 화면으로부터의 거리, 비초점흐림 정도가 시각 주의에 영향을 미치는 유의한 인자인 것을 확인하였고 반응 표면 계획법을 이용하여 이 세가지 인자들에 따른 시각 주의 점수 모델을 도출하였다. 이 시각 주의 점수 모델로부터 영상 각 픽셀의 시각 주의 확률을 구하였다. 본 연구의 뒷부분에서는 시각주의 확률 모델을 기존의 기울기(gradient) 기반 3차원 영상의 입체감 측정법에 적용하는 방법을 제안하였다. 화면 상에서 시선을 집중할 확률이 큰 부분에 높은 비중을 둠으로써 기존의 방법 보다 시청자가 느끼는 입체감을 더욱 정확하게 측정할 수 있도록 하였다. 제안한 방법의 성능을 검증하기 위해 주관적 평가를 실시하여 피실험자들이 느끼는 입체감과 제안된 방법으로부터 도출한 결과를 비교하였다. 실험 결과 제안한 방법이 기존의 방법에 비해 성능이 높은 것을 확인하였다.

• 한국IT서비스학회지
• /
• 제15권4호
• /
• pp.73-83
• /
• 2016

Pedestrian safety was recently highlighted with a yellow carpet, a yellow-colored pavement material prepared for children waiting for signals for pedestrian crossing, without validation of its efficiency in practice. It was a promising device likely to assist highway safety by stimulating pedestrian to step on the yellow-colored area; it was generally called nudge effects. This paper delivers a study conducted to check the effectiveness of yellow carpet in three different aspects in vehicle driver's perspective by applying the newly introduced information technology (IT) service: Visual Attention Software (VAS). It was assumed that VAS developed by 3M in the United States should be able explain the Korean drivers' visual reaction behaviors since technology embedded in VAS was developed based on and proved by other various international countries and continents in the world. A set of pictures was taken at thirteen different field sites in seven school zone areas in the Seoul metropolitan area before and after the installation of a yellow carpet, respectively. Sets of those pictures were analyzed with VAS, and the results were compared based on the selective safety measures: the likely focusing on standing pedestrians (waiting for a pedestrian's green signal time) affected by its background (yellow-colored pavement) contrasting him or her. The test results from a set of before-and-after comparison analyses showed that the placement of yellow carpet would (1) increase 71% of driver's visual attention on pedestrian crossing areas and (2) change the sequential order of visual attention on that area 2.4 steps ahead. The findings would enhance deployment of such promising efficiency and thus increase children safety in pedestrian crossing. The result was promising to highlight the way to support the changes in conservative traffic safety engineering field by applying the advanced IT services, while much robust research was recommended to overcome the limitation of simplification of this study.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제7권8호
• /
• pp.1843-1859
• /
• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.