• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.41 no.4
• /
• pp.93-101
• /
• 2004

A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.

• Korean Institute of Interior Design Journal
• /
• v.25 no.2
• /
• pp.30-40
• /
• 2016

This study analyzed the attention concentration tendencies of one(1) subject who showed convergent exploratory acts actively through the gaze-brainwave measurement experiment of cafe space images and our research findings are as follows. First, the areas of interest (AOIs) that the subject gazed visually by paying attention to it and concentrating on it at a cafe space include counter&menu area, sign area, partition area, image wall area, stairs area, and movable furniture area, and built-in furniture area: seven areas in total. Second, conscious gaze frequency appeared the highest in counter&menu area, and conscious gaze appeared more later than in initial times. Third, conscious gaze pattern was divided into the zone that explored various areas dispersely (distributed exploratory zone) and the zone that explored between particular areas concentratedly (intensive exploratory zone). Fourth, as a result of analyzing the brainwave attention concentration, it was found that the attention concentration in prefrontal lobe (Fp1, Fp2) and frontal lobe (F3, F4) rose to a higher level in the zone of 15 to 16 seconds and this time zone was considered to be a zone where gazing at counter&menu area was very active. In addition, the attention concentration appeared higher in the initial zone than in the later zone, among the entire experimental time zones. Finally, as a result of analyzing the changes in activation by brain portion of the SMR wave expressed when maintaining the arousal and attention concentration, it was found that the right prefrontal lobe and the frontal lobe became activated in the time zone when the intensive exploration of "counter&menu area" and "movable furniture${\leftrightarrow}$built-in furniture area" had occurred and the time zone when the intensive exploration of "image wall${\leftrightarrow}$partition area" and "counter&menu${\leftrightarrow}$sign area" had occurred.

• Science of Emotion and Sensibility
• /
• v.16 no.3
• /
• pp.409-416
• /
• 2013

Attention is one of important cognitive functions in human affecting on the selectional concentration of relevant events and ignorance of irrelevant events. The discrimination of attentional and inattentional status is the first step to manage human's attentional capability using computer assisted device. In this paper, we newly combine the non-linear recurrence pattern analysis and spectrum analysis to effectively extract features(total number of 13) from the electroencephalographic signal used in the input to classifiers. The performance of diverse types of attention-inattention classifiers, including supporting vector machine, back-propagation algorithm, linear discrimination, gradient decent, and logistic regression classifiers were evaluated. Among them, the support vector machine classifier shows the best performance with the classification accuracy of 81 %. The use of spectral band feature set alone(accuracy of 76 %) shows better performance than that of non-linear recurrence pattern feature set alone(accuracy of 67 %). The support vector machine classifier with hybrid combination of non-linear and spectral analysis can be used in later designing attention-related devices.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.10 no.2
• /
• pp.398-405
• /
• 2006

We propose a pattern classification model using a selective perception ability of human beings. Generally, human beings recognize an object by putting a selective concentration on it in the region of interest. Much better classification and recognition could be possible by adapting this phenomenon in pattern classification. First, the pattern classification model creates some reference cluster patterns in a usual way. Then it generates an SPM(Selective Perception Map) that reflects the mutual relation of the reference cluster patterns. In the recognition phase, the model applies the SPM as a weight for calculating the distance between an input pattern and the reference patterns. Our experiments show that the proposed classifier with the SPM acquired the better results than other approaches in pattern classification.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.4
• /
• pp.29-37
• /
• 2021

In this paper, we propose a deep learning-based retinal vessel segmentation model for handling multi-scale information of fundus images. we integrate the selective kernel convolution into U-Net-based convolutional neural network. The proposed model extracts and segment features information with various shapes and sizes of retinal blood vessels, which is important information for diagnosing eye-related diseases from fundus images. The proposed model consists of standard convolutions and selective kernel convolutions. While the standard convolutional layer extracts information through the same size kernel size, The selective kernel convolution extracts information from branches with various kernel sizes and combines them by adaptively adjusting them through split-attention. To evaluate the performance of the proposed model, we used the DRIVE and CHASE DB1 datasets and the proposed model showed F1 score of 82.91% and 81.71% on both datasets respectively, confirming that the proposed model is effective in segmenting retinal blood vessels.

• KIPS Transactions on Software and Data Engineering
• /
• v.10 no.9
• /
• pp.349-358
• /
• 2021

Recently, a method of finding the attention area or localization area for an object of an image using CAM (Class Activation Map)[1] has been variously carried out as a study of WSOL (Weakly Supervised Object Localization). The attention area extraction from the object heat map using CAM has a disadvantage in that it cannot find the entire area of the object by focusing mainly on the part where the features are most concentrated in the object. To improve this, using CAM and Selective Search[6] together, we first expand the attention area in the heat map, and a Gaussian smoothing is applied to the extended area to generate retraining data. Finally we train the data to expand the attention area of the objects. The proposed method requires retraining only once, and the search time to find an localization area is greatly reduced since the selective search is not needed in this stage. Through the experiment, the attention area was expanded from the existing CAM heat maps, and in the calculation of IOU (Intersection of Union) with the ground truth for the bounding box of the expanded attention area, about 58% was improved compared to the existing CAM.

• The Mathematical Education
• /
• v.61 no.2
• /
• pp.339-357
• /
• 2022

This study explores pre-service secondary mathematics teachers (PSTs)' noticing competency. 17 PSTs participated in this study as a part of the mathematics teaching method class. Individual PST's essays regarding the question 'what effective mathematics teaching would be?' that they discussed and wrote at the beginning of the course were collected as the first data. PSTs' written analysis of an expert teacher's teaching video, colleague PSTs' demo-teaching video, and own demo-teaching video were also collected and analyzed. Findings showed that most PSTs' noticing level improved as the class progressed and showed a pattern of focusing on each key aspect in terms of the Teaching for Robust Understanding of Mathematics (TRU Math) framework, but their reasoning strategies were somewhat varied. This suggests that the TRU Math framework can support PSTs to improve the competency of 'what to attend' among the noticing components. In addition, the instructional reasoning strategies imply that PSTs' noticing reasoning strategy was mostly related to their interpretation of noticing components, which should be also emphasized in the teacher education program.

• The KIPS Transactions:PartB
• /
• v.17B no.1
• /
• pp.55-62
• /
• 2010

Human visual attention system has a remarkable ability to interpret complex scenes with the ease and simplicity by selecting or focusing on a small region of visual field without scanning the whole images. In this paper, a novel feature map generation and integration method for attention based visual information processing system is proposed. The depth information obtained from a stereo pair of images is exploited as one of spatial visual features to form a set of topographic feature maps in our approach. Comparative experiments show that correct detection rate of visual attention regions improves by utilizing depth feature compared to the case of not using depth feature.

• Proceedings of the Korean Information Science Society Conference
• /
• 1998.10c
• /
• pp.688-690
• /
• 1998

직업 선택의 과정에 있어 능력, 적성만큼 중요시 해야 할 것은 그 직업에 대해 흥미도이다. 특히 장애인들의 경우 직업을 준비하는 단계에서부터 어느 영역에 직업적 흥미가 있는가를 파악해야 하는 일은 중요하다. Text, Audio/Voice, Image등 멀티미디어 data를 이용, 흥미와 주의 집중을 높혀 직업적 흥미영역에 대한 정확한 data를 얻을 수 있는 시스템 개발이 필요하다.

• Korean Journal of Social Welfare
• /
• v.64 no.1
• /
• pp.299-324
• /
• 2012

After social service voucher schemes were introduced in Korea, many controversies have been concentrated on market-based approach, which encourage competition and choice while amount of funding, number of providers, and types of services have been rapidly expanded. This also could mean that transform of social service paradigm from provider-centered approach to user-cantered approach, one of the trends found internationally. Therefore, this is the study to reveal development of social service discourses in Britain where various models of social services have been attempted in their modern history. Then, through the historical analysis, the trends toward more user-cantered approach were found to be distinguished based on the division of individualism and collectivism. This could provide theocratical implication to the current discussion on social services in Korea.