• Korean Journal of Cognitive Science
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• v.10 no.3
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• pp.1.2-1.2
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• 1999

시각적 선택에 대한 과거 정신물리학적, 신경 생리학적 연구결과를 토대로 Feature Gate 라는 신경 망 모형을 제안하였다. 이 모형에는 공간 배치도가 위계 적으로 구성되어 있으며, 정보의 흐름이 위계의 각 수준으로부터 그 다음 수준으로 넘어갈 때 주의 게이트에 의해 조절되도록 되어 있다. 주의 게이트들은 독특한 세부 특징을 가진 위치에 반응하는 상향식 시스템과 표적 세부 특징이 있는 위치에 반응하는 하향식 기제 모두에 의해 조절된다. 본 연구는 Feature Gate 모형의 하향식 기제에 초점을 맞추어 모형을 설명하고, 현재 다른 모형들이 설명하지 못하는 Moran & Desimone(1985)의 연구결과를 이 모형이 어떻게 설명하는지를 제시하고자 한다. Feature Gate 모형은 병렬 적인 세부특징 검색, 계열 적 접합표적 검색, 단서에 의한 주의의 점진적 감소 모형, 세부특징-주도적인 공간적 선택, 주의의 분할, 방해자극 위치의 억제, 주변 억제 등을 포함한 시각적 주의 연구의 여러 가지 많은 현상들을 설명하는데 하나의 일관적인 해석을 제공해 준다. 앞으로 이 모형을 더욱 확장, 발전 시켜 세부특징의 조합된 배열에 반응하는 상위 수준의 유닛을 사용한다면 시각적 선택과정이 포함된 형태 재인 모형으로 개발될 수 있다.

• Journal of Korea Multimedia Society
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• v.14 no.3
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• pp.378-391
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• 2011

In this paper, we suggest a novel method of selecting visual information based on bottom-up visual attention of human. We propose a new model that improve accuracy of detecting attention region by using depth information in addition to low-level spatial features such as color, lightness, orientation, form and temporal feature such as motion. Motion is important cue when we derive temporal saliency. But noise obtained during the input and computation process deteriorates accuracy of temporal saliency Our system exploited the result of psychological studies in order to remove the noise from motion information. Although typical systems get problems in determining the saliency if several salient regions are partially occluded and/or have almost equal saliency, our system is able to separate the regions with high accuracy. Spatiotemporally separated prominent regions in the first stage are prioritized using depth value one by one in the second stage. Experiment result shows that our system can describe the salient regions with higher accuracy than the previous approaches do.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Korean Institute of Interior Design Journal
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• v.25 no.6
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• pp.116-126
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• 2016

In modern society which changes from quantity-seeking society to value-seeking one, people's various lifestyles have great effect on consumption patterns and work as an important factor in choosing hotels. The fact that design hotels, which provide unique experiences with differentiated and sensitive designs by reflecting various lifestyles, recently attract attention can be understood in the same context. As a matter of fact, design hotels recently serve as destinations as they become cultural and artistic icons which reflect customer lifestyles. Especially, the designs of lobby spaces in hotels play deciding role in customers' choices while representing the nature of hotels. In this respect, under the premise that the kinds of accumulated experiences are different depending on lifestyles and preferences for specific interior spaces are influenced by association mechanism formed by experiences, this study analyzed lobby spaces of design hotels which focus on specific lifestyles from the perspective of association mechanism based on experiences. As the method of analysis, this study classified the types of lifestyles and conducted case analysis to investigate what association mechanism works to enhance the preference of design hotels by types. Study classified lifestyles into experiential activity type, social meeting type, fashion-pursuing type and hideout-preferring type and analyzed cases of lobby designs in design hotels. The results of this case analysis are as follows; First, experiential activity type mainly utilized quasi-association and approach association through senses and social meeting type utilized quasi-association and memory association through emotions while fashion-pursuing type utilized quasi-association and presumption association through intuition and hideout-preferring type utilized quasi-association and approach association through thoughts. Second, it was found that most lobby designs are characterized by association mechanism in visual formative nature and that in temporal spatial nature working in complex way, and, through such process of association expansion, space stories are created. Stories of spaces created this way become unique identities of design hotels that provide new experiences for customers.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1001-1004
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• 2021

Pain is an unpleasant experience for the patient. The recognition and assessment of pain help tailor the treatment to the patient, and they are also challenging in the medical. In this paper, we propose an approach for pain recognition through a deep neural network applied to pre-processed physiological. The proposed approach applies the idea of shortcut connections to concatenate the spatial information of a convolutional neural network and the temporal information of a recurrent neural network. In addition, our proposed approach applies the attention mechanism and achieves competitive performance on the BioVid Heat Pain dataset.

• Korean Institute of Interior Design Journal
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• v.27 no.2
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• pp.3-11
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• 2018

This study aims to investigate whether there is intensionally changing eye - gaze on the cafe space images with floor finishing materials. In the Yarbus' experiment, he argued that changing information that an observer is asked to obtain from an image changes pattern of eye movements. Based on the scan path evidence, this research have questions as (1) the difference of visual attention on finishing floor material stimulus, (2) visual attention of initial activity time and type of movement paths on AOIs, and (3) visual relation floor area with another AOIs. Eye movements were recorded with the SMI REDn Scientific, which sampled eye position at 30Hz and lasted 2 minutes(120s). Although viewing was binocular, only the right eye was tracked. Of the 66 observers(mean age 22 years, standard deviation: ${\pm}1.82$) who participated in the experiment done by the four point calibration and validation procedures at the beginning tasks. Analyzing qualitative data from the number of fixation and duration on AOIs divided into four parts (AOI I-Floor, AOI II-Wall, AOI III-Ceiling, and AOI IV-Counter) in the stimulus. The results from this experiment analyzed as follows. First, it was significant in the difference of the average number of AOIs fixation times observed for the spatial image using the wood tile flooring material and the polishing tile. The wood tile flooring of stimulus had higher fixation number on AOI-II, AOI-III, and AOI-IV than the polishing tile. On seeing AOI-I was higher attention in the polishing tile stimulus. Second, the observers examined AOI-II intensively in both stimuli. However, the visual intensity was also followed by on the AOI-IV and AOI-I in the wood tile flooring stimulus, and on AOI-I, AO-IV in the polishing tile. Third, visual attention data on each AOIs have divided into the time range of "5 sec" for both images. In the wood tile stimulus, the horizontal movement path followed by AOI-II, AOI-IV, and AOI-II. In the polished tile stimulus, the movement path followed by moving vertically to AOI-II, AOI-I, and AOI-II. This study approached meaningfully and found out the characteristics of visual attention, according to the different intentions of visual attention, the relationship pathways of visual mechanism appeared and also activated by eye-tracking experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2131-2153
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• 2022

To achieve accurate detection of tuberculosis (TB) areas in chest radiographs, we design a chest X-ray TB area detection algorithm. The algorithm consists of two stages: the chest X-ray TB classification network (CXTCNet) and the chest X-ray TB area detection network (CXTDNet). CXTCNet is used to judge the presence or absence of TB areas in chest X-ray images, thereby excluding the influence of other lung diseases on the detection of TB areas. It can reduce false positives in the detection network and improve the accuracy of detection results. In CXTCNet, we propose a channel attention mechanism (CAM) module and combine it with DenseNet. This module enables the network to learn more spatial and channel features information about chest X-ray images, thereby improving network performance. CXTDNet is a design based on a sparse object detection algorithm (Sparse R-CNN). A group of fixed learnable proposal boxes and learnable proposal features are using for classification and location. The predictions of the algorithm are output directly without non-maximal suppression post-processing. Furthermore, we use CLAHE to reduce image noise and improve image quality for data preprocessing. Experiments on dataset TBX11K show that the accuracy of the proposed CXTCNet is up to 99.10%, which is better than most current TB classification algorithms. Finally, our proposed chest X-ray TB detection algorithm could achieve AP of 45.35% and AP50 of 74.20%. We also establish a chest X-ray TB dataset with 304 sheets. And experiments on this dataset showed that the accuracy of the diagnosis was comparable to that of radiologists. We hope that our proposed algorithm and established dataset will advance the field of TB detection.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.37-46
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• 2012

We tested the effects of endogenous attention and reorienting on the performance of detection task. In the classic detection paradigm of Posner and Cohen (1980), performance on target detection is measured, where target appears either on the same or difference spatial location of cue stimulus after brief period of SOA (stimulus onset asynchrony). In this study, we induced exogenous attention by manipulating predictability of cue for target, and also induced reorientation by inserting additional (reorienting) cue between initial cue and target. Experiment 1 had three conditions of reorienting speed: Early, middle, and late. Facilitation and IOR (inhibition of return) occurred in different forms depending on SOA and reorienting speed, but we were not able to discover interpretable pattern in the results. However, reanalysis of early reorienting condition revealed that facilitation and IOR occurred in a crossed manner where short SOA found facilitation and long SOA did IOR, the typical results of simple detection task. Experiment 2 collected additional data to replicate the results in early reorienting condition of experiment 1. The results obtained that facilitation occurred with short SOA and IOR with long SOA. These results contrast with those of Wright and Richard (2000) where they reported elimination of IOR when cue had predictability of target locations. These results suggest that additional cue (here, orienting cue), which rapidly appears before extinction of IOR by prior cue, brings about double IOR. The present research demonstrates that even when attention is allocated to certain location via endogenous mechanism, rapidly repeating cues in certain location maximizes IOR that offsets the effects of endogenous attention to the same location.

• Geomechanics and Engineering
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• v.23 no.2
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• pp.151-163
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• 2020

Grouting method is an effective way of reinforcing cracked rock masses and plugging water gushing. Current grouting diffusion models are generally developed for horizontal cracks, which is contradictory to the fact that the crack generally occurs in rock masses with irregular spatial distribution characteristics in real underground environments. To solve this problem, this study selected a cement-sodium silicate slurry (C-S slurry) generally used in engineering as a fast-curing grouting material and regarded the C-S slurry as a Bingham fluid with time-varying viscosity for analysis. Based on the theory of fluid mechanics, and by simultaneously considering the deadweight of slurry and characteristics of non-uniform spatial distribution of viscosity of fast-curing grouts, a theoretical model of slurry diffusion in an oblique crack in rock masses at constant grouting rate was established. Moreover, the viscosity and pressure distribution equations in the slurry diffusion zone were deduced, thus quantifying the relationship between grouting pressure, grouting time, and slurry diffusion distance. On this basis, by using a 3-d finite element program in multi-field coupled software Comsol, the numerical simulation results were compared with theoretical calculation values, further verifying the effectiveness of the theoretical model. In addition, through the analysis of two engineering case studies, the theoretical calculations and measured slurry diffusion radius were compared, to evaluate the application effects of the model in engineering practice. Finally, by using the established theoretical model, the influence of cracking in rock masses on the diffusion characteristics of slurry was analysed. The results demonstrate that the inclination angle of the crack in rock masses and azimuth angle of slurry diffusion affect slurry diffusion characteristics. More attention should be paid to the actual grouting process. The results can provide references for determining grouting parameters of fast-curing grouts in engineering practice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2321-2338
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• 2021

Compared with vehicle trajectories, pedestrian trajectories have stronger degrees of freedom and complexity, which poses a higher challenge to trajectory prediction tasks. This paper designs a mode to divide the trajectory of pedestrians at a traffic intersection, which converts the trajectory regression problem into a trajectory classification problem. This paper builds a deep model for pedestrian trajectory prediction at intersections for the task of pedestrian short-term trajectory prediction. The model calculates the spatial correlation and temporal dependence of the trajectory. More importantly, it captures the interactive features among pedestrians through the Attention mechanism. In order to improve the training speed, the model is composed of pure convolutional networks. This design overcomes the single-step calculation mode of the traditional recurrent neural network. The experiment uses Vulnerable Road Users trajectory dataset for related modeling and evaluation work. Compared with the existing models of pedestrian trajectory prediction, the model proposed in this paper has advantages in terms of evaluation indicators, training speed and the number of model parameters.