• Journal of Advanced Marine Engineering and Technology
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• v.36 no.5
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• pp.674-682
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• 2012

A new intelligent stereo vision sensor system was studied for the motion and depth control of unmanned vehicles. A new bottom-up saliency map model for the human-like active stereo vision system based on biological visual process was developed to select a target object. If the left and right cameras successfully find the same target object, the implemented active vision system with two cameras focuses on a landmark and can detect the depth and the direction information. By using this information, the unmanned vehicle can approach to the target autonomously. A number of tests for the proposed bottom-up saliency map were performed, and their results were presented.

• Journal of Sensor Science and Technology
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• v.10 no.1
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• pp.52-61
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• 2001

We propose a new saccadic eye movement system with visual selective attention. Saliency map models generate the scan pathways in a natural scene, of which the output makes an attended location. Saccadic eye movement model is used for producing the target trajectories to move the attended locations very rapidly. To categorize human saccadic eye movement, saccadic eye movement model was divided into three parts, each of which was then individually modeled using different neural networks to reflect a principal functionality of brain structures related with the saccadic eye movement in our brain. Based on the proposed saliency map models and the saccadic eye movement model, an active vision system using a CCD type camera and BLDC motor was developed and demonstrated with experimental results.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.145-151
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• 2021

In this paper, we propose a novel visualization technique to explain the predictions of deep neural networks. We use knowledge distillation (KD) to identify the interior of a black-box model for which we know only inputs and outputs. The information of the black box model will be transferred to a white box model that we aim to create through the KD. The white box model will learn the representation of the black-box model. Second, the white-box model generates attention maps for each of its layers using Grad-CAM. Then we combine the attention maps of different layers using the pixel-wise summation to generate a final saliency map that contains information from all layers of the model. The experiments show that the proposed technique found important layers and explained which part of the input is important. Saliency maps generated by the proposed technique performed better than those of Grad-CAM in deletion game.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.211-217
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• 2012

Conventional GrabCut algorithm is semi-automatic algorithm that user must be set rectangle window surrounds the object. This paper studied automatic object detection to solve these problem by detecting salient region based on Human Visual System. Saliency map is computed using Lab color space which is based on color opposing theory of 'red-green' and 'blue-yellow'. Then Saliency Points are computed from the boundaries of Low-Frequency region that are extracted from Saliency Map. Finally, Rectangle windows are obtained from coordinate value of Saliency Points and these windows are used in GrabCut algorithm to extract objects. Through various experiments, the proposed algorithm computing rectangle windows of salient region and extracting objects has been proved.

• Journal of Sensor Science and Technology
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• v.13 no.2
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• pp.144-151
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• 2004

In this paper, we propose an active stereo surveillance system with human-like convergence function. The proposed system uses a bottom-up saliency map model with the human-like selective attention visual function to select an interesting region in each camera. and this system compares the landmarks whether the selective region in each camera finds a same region. If the left and right cameras successfully find a same landmarks, the implemented vision system focuses on the landmark. Using the motor encoder information, we can automatically obtain the depth information and resultantly construct a depth map using the depth information. Computer simulation and experimental results show that the proposed convergence method is very effective to implement the active stereo surveillance system.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.157-168
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• 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 Korea Multimedia Society
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• v.14 no.8
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• pp.1082-1093
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• 2011

Traditional rendering of cartoon character cannot revive the feeling of concept drawings properly. In this paper, we propose capture technology to get toon shading model from the concept drawings and with this technique, we provide a new novel system to render 3D cartoon character. Benefits of this system is to cartoonize the 3D character according to saliency to emphasize the form of 3D character and further support the sketch-based user interface for artists to edit shading by post-production. For this, we generate texture automatically by RGB color sorting algorithm to analyze color distribution and rates of selected region. In the cartoon rendering process, we use saliency as a measure to determine visual importance of each area of 3d mesh and we provide a novel cartoon rendering algorithm based on the saliency of 3D mesh. For the fine adjustments of shading style, we propose a user interface that allow the artists to freely add and delete shading to a 3D model. Finally, this paper shows the usefulness of the proposed system through user evaluation.

• Journal of the Ergonomics Society of Korea
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• v.31 no.6
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• pp.705-713
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• 2012

Objective: The aim of this study is to predict human visual target search using ACT-R cognitive architecture in real scene images. Background: Human uses both the method of bottom-up and top-down process at the same time using characteristics of image itself and knowledge about images. Modeling of human visual search also needs to include both processes. Method: In this study, visual target object search performance in real scene images was analyzed comparing experimental data and result of ACT-R model. 10 students participated in this experiment and the model was simulated ten times. This experiment was conducted in two conditions, indoor images and outdoor images. The ACT-R model considering the first saccade region through calculating the saliency map and spatial layout was established. Proposed model in this study used the guide of visual search and adopted visual search strategies according to the guide. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion: The proposed ACT-R model is able to predict the human visual search process in real scene images using salience map and spatial layout. Application: This study is useful in conducting model-based evaluation in visual search, particularly in real images. Also, this study is able to adopt in diverse image processing program such as helper of the visually impaired.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.929-943
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• 2016

This paper proposes the rate control algorithm based on the S-JND (Saliency-Just Noticeable Difference) model for considering perceptual visual quality. The proposed rate control algorithm employs the S-JND model to simultaneously reflect human visual sensitivity and human visual attention for considering characteristics of human visual system. During allocating bits for CTU (Coding Tree Unit) level in a rate control, the bit allocation model calculates the S-JND threshold of each CTU in a picture. The threshold of each CTU is used for adaptively allocating a proper number of bits; thus, the proposed bit allocation model can improve perceptual visual quality. For performance evaluation of the proposed algorithm, the proposed algorithm was implemented on HM 16.9 and tested for sequences in Class B and Class C under the CTC (Common Test Condition) RA (Random Access), Low-delay B and Low-delay P case. Experimental results show that the proposed method reduces the bit-rate of 2.3%, and improves BD-PSNR of 0.07dB and bit-rate accuracy of 0.06% on average. We achieved MOS improvement of 0.03 with the proposed method, compared with the conventional method based on DSCQS (Double Stimulus Continuous Quality Scale).

• Journal of Broadcast Engineering
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• v.20 no.3
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• pp.368-378
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• 2015

3D conversion technology has been studied over past decades and integrated to commercial 3D displays and 3DTVs. The 3D conversion plays an important role in the augmented functionality of three-dimensional television (3DTV), because it can easily provide 3D contents. Generally, depth cues extracted from a static image is used for generating a depth map followed by DIBR (Depth Image Based Rendering) rendering for producing a stereoscopic image. However except some particular images, the existence of depth cues is rare so that the consistent quality of a depth map cannot be accordingly guaranteed. Therefore, it is imperative to make a 3D conversion method that produces satisfactory and consistent 3D for diverse video contents. From this viewpoint, this paper proposes a novel method with applicability to general types of image. For this, saliency as well as edge is utilized. To generate a depth map, geometric perspective, affinity model and binomic filter are used. In the experiments, the proposed method was performed on 24 video clips with a variety of contents. From a subjective test for 3D perception and visual fatigue, satisfactory and comfortable viewing of 3D contents was validated.