• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.539-557
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• 2013

A novel multifocus image fusion algorithm based on NSCT is proposed in this paper. In order to not only attain the image focusing properties and more visual information in the fused image, but also sensitive to the human visual perception, a local multidirection variance (LEOV) fusion rule is proposed for lowpass subband coefficient. In order to introduce more visual saliency, a modified local contrast is defined. In addition, according to the feature of distribution of highpass subband coefficients, a direction vector is proposed to constrain the modified local contrast and construct the new fusion rule for highpass subband coefficients selection The NSCT is a flexible multiscale, multidirection, and shift-invariant tool for image decomposition, which can be implemented via the atrous algorithm. The proposed fusion algorithm based on NSCT not only can prevent artifacts and erroneous from introducing into the fused image, but also can eliminate 'block effect' and 'frequency aliasing' phenomenon. Experimental results show that the proposed method achieved better fusion results than wavelet-based and CT-based fusion method in contrast and clarity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.769-786
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• 2016

Object recognition and object location have always drawn much interest. Also, recently various computational models have been designed. One of the big issues in this domain is the lack of an appropriate model for extracting important part of the picture and estimating the object place in the same environments that caused low accuracy. To solve this problem, a new Salient Based Bag of Visual Word (SBBoVW) model for object recognition and object location estimation is presented. Contributions lied in the present study are two-fold. One is to introduce a new approach, which is a Salient Based Bag of Visual Word model (SBBoVW) to recognize difficult objects that have had low accuracy in previous methods. This method integrates SIFT features of the original and salient parts of pictures and fuses them together to generate better codebooks using bag of visual word method. The second contribution is to introduce a new algorithm for finding object place based on the salient map automatically. The performance evaluation on several data sets proves that the new approach outperforms other state-of-the-arts.

• Journal of Korea Game Society
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• v.10 no.1
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• pp.15-23
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• 2010

In action games such as shooting games or platform games, dodging enemy objects is crucial since the player character dies or loses energy when it collides with any enemy object. In this paper, we investigates how the difficulty of these games changes according to the existence of salient objects. Since salient objects attract the player's attention, other non-salient objects may be unattended by the player, resulting in failing to dodge them. We experimented on the influence of salient objects on the difficulty of a game, and found out that the subjects who played the game without salient objects performed better than the subjects who played the game with salient objects. This paper investigates a human perceptual issue that could affect the game difficulty and suggest a potential guideline for game design and planning.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.486-489
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• 2012

본 논문에서는 인간의 주의시각(Human Visual Attention)에 기반하여 영상에서 가장 유용하다고 생각되는 관심 영역(Salient Region)을 새로운 방식으로 탐지해내고 관심-객체를 검출하는 방법을 제안한다. 제안하는 시스템은 인간의 주의시각 특성인 주파수와 명도, 색상 특징을 이용하는데, 먼저 주파수-명도 정보를 이용한 특징 지도(Feature map)와 색상 정보를 이용한 특징 지도를 각각 생성 한 후 영상의 특징 점(Saliency Point)을 추출한다. 이렇게 생성된 특징 지도와 특징 점을 이용하여 집중 윈도우의 위치와 크기를 결정하고 집중 윈도우 내에 특징 지도를 결합하여 관심 영역을 탐지하고 해당하는 영역에 대해 관심-객체를 추출한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.8-10
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• 2015

최근 2D 와 3D 콘텐츠의 급격한 수요 증가로 인하여 2D 와 3D 공간에서 사람이 인지하는 물체의 시각적 정보량을 정량화할 필요성이 대두되었다. 본 논문에서는 정보이론에 기초하여 엔트로피 관점에서 2D 와 3D 영상의 시각적 정보량을 측정하는 방법을 제시한다. 시각적 정보량을 측정할 때, 기존의 연구에서는 고려되지 않았던 집중영역(saliency), 시각세포의 불균형으로 인한 주변영역 흐림현상인 포비에이션(foveation), 양안합성(binocular fusion)등 인간의 시각적 특성을 반영하였다는 점에서 기존의 연구들과 차이를 둔다. 2D 콘텐츠의 시각적 엔트로피는 단안시에 근거한 질감(texture) 엔트로피와 깊이 엔트로피로 구성되어 있다. 그리고 3D 콘텐츠의 시각적 엔트로피는 2D 에서의 시각적 엔트로피와 양안시에 의한 깊이 엔트로피를 포함한다. 본 논문의 시각적 엔트로피는 2D 와 3D 영상의 시각적 피로도를 측정할 때 사용될 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.314-317
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• 2011

본 논문은 상향식 현저함 모델을 이용하여 입력 영상으로부터 시각적 주의를 갖는 영역들을 자동으로 검출하는 방법을 제안한다. 제안한 방법에서는 인간의 시각 시스템과 같이 사전 지식 없이 시각정보의 공간적인 분포에 근거하여 장면을 해석하는 상향식 현저함 모델 방법을 입력 영상에 적용하여 관심 물체 영역을 검출하는 연구이다. 상향식 현저함 방법은 Treisman의 세부특징이론 연구에서 제시한 바와 같이 시각적 주의를 갖는 영역은 시각정보의 현격한 대비차이를 가지는 영역으로 집중되어 배경에서 관심영역을 구분할 수 있다. 입력 영상에서 현저함 모델을 통해 3차원 현저함 맵을 생성한다. 그리고 생성된 현저함 맵으로부터 실제 관심영역들을 검출하기 위해 제안한 방법에서는 적응적 임계치 방법을 적용하여 관심영역을 검출한다. 제안한 방법을 관심영역 분할에 적용한 결과, 영역 분할 정확도 및 정밀도가 약 88%와 89%로 제시되어 관심 영상분할 시스템에 적용이 가능함을 알 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.115-122
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• 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 Engineers of Korea SC
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• v.49 no.4
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• pp.90-101
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• 2012

The landmark selection is crucial to successful perform in SLAM(Simultaneous Localization and Mapping) with a mono camera. Especially, in unknown environment, automatic landmark selection is needed since there is no advance information about landmark. In this paper, proposed visual attention system which modeled human's vision system will be used in order to select landmark automatically. The edge feature is one of the most important element for attention in previous visual attention system. However, when the edge feature is used in complicated indoor area, the response of complicated area disappears, and between flat surfaces are getting higher. Also, computation cost increases occurs due to the growth of the dimensionality since it uses the responses for 4 directions. This paper suggests to use a corner feature in order to solve or prevent the problems mentioned above. Using a corner feature can also increase the accuracy of data association by concentrating on area which is more complicated and informative in indoor environments. Finally, this paper will prove that visual attention system based on corner feature can be more effective in SLAM compared to previous method by experiment.

• Smart Media Journal
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• v.8 no.2
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• pp.39-45
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• 2019

As deep learning has become popular, researches which can help explaining the prediction results also become important. Superpixel based multi-scale combining technique, which provides the advantage of visual pleasing by maintaining the shape of the object, has been recently proposed. Based on the principle of prediction difference, this technique computes the saliency map from the difference between the predicted result excluding the superpixel and the original predicted result. In this paper, we propose a new technique of both excluding and including super pixels. Experimental results show 3.3% improvement in IoU evaluation.

• Journal of Broadcast Engineering
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• v.11 no.2 s.31
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• pp.207-221
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• 2006

A fully automatic and computationally efficient method is proposed for intelligent display of soccer video on small multimedia mobile devices. The rapid progress of the multimedia signal processing has contributed to the extensive use of multimedia devices with a small LCD panel. With these emerging small mobile devices, the video sequences captured for standard- or HDTV broadcasting may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera technique, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Thus, an intelligent display technique is needed for small-display-viewers. To this end, one of the key technologies is to determine region of interest (ROI), which is a part of the scene that viewers pay more attention to than other regions. In this paper, the focus is on soccer video display for mobile devices. Instead of taking visual saliency into account, we take domain-specific approach to exploit the characteristics of the soccer video. The proposed scheme includes three modules; ground color learning, shot classification, and ROI determination. The experimental results show the propose scheme is capable of intelligent video display on mobile devices.