• Korean Institute of Interior Design Journal
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• v.25 no.5
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• pp.71-81
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• 2016

This research has been carried out with the objects of sporting goods shops to find out what structure of those shops raises more interest from customers. The tracking eyes on the objects which are the same but seen to have different structures has revealed the followings. Customers' visual appreciation of Reverse left/right Images (11.1) was found to be higher than that of Original Images (10.6). Furthermore, the reverse left/right image of the space also was found to attract more interest from customers, which led them to have longer observation. The below is about the interpretation of the spatial exploration by observation time and the appreciation of its visual content in line with the experiment objects of selling spaces. The longer the space was observed, (1)the higher the expansive searching of space was, (2)the more spots were observed as if they did not know what to see after they first observed at early hours, (3)later (in the time range of 64~73 seconds) they came to look at the spots in which they got interested, (4)and then again they suddenly got lost what to see. When the change of observation characteristics by time range is reviewed, it can be seen that the searching of original images is changed from Divergent Feature to Convergent Feature when the observation time increases from the early stage of observation to the later. On the contrary, the reverse left/right images were found to have the opposite searching features, that is, from convergent exploration to divergent exploration. These findings show that the reverse left/right images of the sporting goods shops, which were the experiment objects, have more factors attracting customers' attention and interest and that it is the very shop-structure which makes customers have better visual appreciation of those shops.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.21-29
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• 2017

Previous visual analytics researches has focused on reducing the uncertainty of predicted results using a variety of interactive visual data exploration techniques. The main purpose of the interactive search technique is to reduce the quality difference of the predicted results according to the level of the decision maker by understanding the relationship between the variables and choosing the appropriate model to predict the unknown variables. However, it is difficult to create a predictive model which forecast time series data whose overall trends is unknown such as youth physical growth data. In this paper, we pro pose a novel predictive analysis technique to forecast the physical growth value in small pieces of time series data with un certain trends. This model estimates the distribution of data at a particular point in time. We also propose a visual analytics system that minimizes the possible uncertainties in predictive modeling process.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.1-10
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• 2024

Individuals with visual impairments face numerous challenges in their daily lives, with navigating streets and public spaces being particularly daunting. The inability to identify safe crossing locations and assess the feasibility of crossing significantly restricts their mobility and independence. Globally, an estimated 285 million people suffer from visual impairment, with 39 million categorized as blind and 246 million as visually impaired, according to the World Health Organization. In Saudi Arabia alone, there are approximately 159 thousand blind individuals, as per unofficial statistics. The profound impact of visual impairments on daily activities underscores the urgent need for solutions to improve mobility and enhance safety. This study aims to address this pressing issue by leveraging computer vision and deep learning techniques to enhance object detection capabilities. Two models were trained to detect objects: one focused on street crossing obstacles, and the other aimed to search for objects. The first model was trained on a dataset comprising 5283 images of road obstacles and traffic signals, annotated to create a labeled dataset. Subsequently, it was trained using the YOLOv8 and YOLOv5 models, with YOLOv5 achieving a satisfactory accuracy of 84%. The second model was trained on the COCO dataset using YOLOv5, yielding an impressive accuracy of 94%. By improving object detection capabilities through advanced technology, this research seeks to empower individuals with visual impairments, enhancing their mobility, independence, and overall quality of life.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.221-228
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• 2021

This study aims to provide basic data for developing hand functional training programs using a keyboard to see if there is a relationship between the smart keyboard input speed using both hands, the Dexterity of the hand and the eye-hand coordination ability. The smartphone keyboard input speed, Purdue Pegboard, Grooved Pegboard Test, and Korean-Developmental-Test of Visual Perception-Adolescent were evaluated for 40 university students Province. An independent sample t-test and one-way ANOVA were conducted to identify differences in smartphone keyboard input speed, dexterity, eye-hand coordination ability and visual-motion using both hands according to the general characteristics of the subjects. Pearson correlation was also conducted to find out the relationship between hand-used smartphone keyboard input speed, hand dexterity, eye-hand coordination ability and visual-motor. As a result, the smartphone keyboard input speed using both hands showed a correlation with the dominant hand in the Purdue Pegboard Test (r=-.313, p<.05). In addition, the input speed of the smartphone keyboard is Copying(r=-.333, p<.05), Visual Motor Search(r=.455, p<.01), Visual Motor speed(r=-.453, p<.01) and Form Constancy (r=-.341, p<.05) in the item of K-DTVP-A. Therefore, it is believed that it will be helpful in the development of a treatment program using a smartphone, and it is expected that the effectiveness of a treatment program using a smartphone will be proven through additional experimental studies in the future.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.61-68
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• 2016

In this paper, an implementation method of augmented reality (AR) technology using Web 3.0 information services in the field of disaster response is proposed. The structure and characteristics of semantic web-based Web 3.0 are realized and a AR based mobile visual search (MVS) applied in the disaster sites is described. Based on Web 3.0 and AR MVS, a semantic web ontology oriented configuration scheme for disaster-related information and the communication scheme of information provided by AR technology are proposed. For the purpose of providing disaster-related and customized information to the disaster response site quickly and accurately, a method of leveraging Web 3.0 information services in AR technology is presented.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.388-399
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• 2012

In the paper, we propose enhanced feature extraction and matching methods for a mobile environment based on modified SURF. We propose three methods to reduce the computational complexity in a mobile environment. The first is to reduce the dimensions of the SURF descriptor. We compare the performance of existing 64-dimensional SURF with several other dimensional SURFs. The second is to improve the performance using the sign of the trace of the Hessian matrix. In other words, feature points are considered as matched if they have the same sign for the trace of the Hessian matrix, otherwise considered not matched. The last one is to find the best distance-ratio which is used to determine the matching points. We find the best distance-ratio through experiments, and it gives the relatively high accuracy. Finally, existing system which is based on normal SURF method is compared with our proposed system which is based on these three proposed methods. We present that our proposed system shows reduced response time while preserving reasonably good matching accuracy.

• Korean Institute of Interior Design Journal
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• v.24 no.6
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• pp.128-136
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• 2015

This research, which has been planned to appreciate the features of continuous observation of space, has applied the procedure of acquiring continuous visual information when the act of watching takes place along the time to analyze the space characteristics through the scenes and time so that the features of attention shown in the process of acquiring visual information at the time of observing continuous scenes might be estimated. For analysis of the features of continuous observation was set up the premise that the features of observation and perception vary depending on gender, when the women shops in department stores were selected as research objects. The observation features found at the time of continuous observation of selling spaces in department stores were focused on two analysis methods in order to compare the differences and characteristics of the two. The followings are the findings. First, the area with predominant observation was found to be 87.1% in both methods. It was found that the analysis of observation features by "Analysis I" was useful for inter-sectional comparison of continuous images. Second, in case of extracting predominant sections, the ceiling or the structures which are the backgrounds rarely attracted any eyes. Depending on analysis method, there was the gap of 14.3%~25.0% between observed sections. Third, in case that the hall is curved, the eyes were found to be expanded from side to side and up and down. The review of observation numbers of predominant sections makes it possible to decide whether it should be regarded as (1) unstability or (2) expanding search, and when the images are enlarged from distant view to close-range view, the weakening vanishing point results in the increase of expanded search of surroundings. Accordingly, it was found that the characteristics of images has effects on the observation features when any space was continuously observed. Furthermore, the difference of analysis methods also was found to be likely to cause big differences in the results of analyzing observation features.

• Korean Institute of Interior Design Journal
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• v.24 no.2
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• pp.125-133
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• 2015

Gaze induction characteristics in space vary depending on characteristics of spatial components and display. This study analyzed dominant eye-fixation characteristics of three zones of department store space. Eye-fixation characteristics depending on spatial components and positional relationship can be defined as follows. First, [**.jpg] was used as an extension in the process of storing the image photographed during image data processing for analysis in pixels and due to compressed storage of image data, the image produced with a clear boundary was stored in neutral colors. To remove this problem, the image used in operation was re-processed in black and white and stored in the [**.bmp] format with large capability, at the same time. As the result, the effort caused by unnecessary colors in the program operation process was corrected. Second, the gaze ratio to space area can be indicated as a strength of each gaze zone and when analyzing the gaze strength of the three zones, the left store was a zone with a "little strong" gaze strength of "102.8", the middle space was a zone with an "extremely weak" gaze strength of "89.6" and the right store was a zone with an "extremely strong" gaze strength of "117.2". Third, the IV section had a strong strength of gaze on the middle space and the right store and the V section showed a markedly strong strength of gaze on the left and right stores. This tendency was the same as the VI section with the strongest gaze strength and the right store had a little strong gaze strength than the left store.

• Journal of Korean Ophthalmic Optics Society
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• v.11 no.1
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• pp.17-26
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• 2006

Video display units (VDU) are interfaces between computers and human operators. This study was performed to have a good understanding of the potential causes of eye problems in 50 students to enable them to perform an accurate assessment of the patient's visual performance while looking at a computer screen. The TSS Optometric Toolkit for Computer Users provides a powerful set of tools to enable eye care practitioners to assess visual performance under conditions that closely simulate a computer user's normal working conditions. The program consists of seven tests: Near vision chart, Sample text display, Spreadsheet display, Fixation disparity, Duochrome, Number search, Randolt Ring search. The results of this study show that 55% of the subjects do not have computer-related eye problems. It is apparent from this data that approximately 25% of all computer-related eye problems are attributable to the display screen, the workstation or inappropriate workpractices. Of the remaining cases, 20% of them will require general purpose spectacles and spectacles specifically for work at the computer. This study suggests that computer program, The TSS Optometric Toolkit for Computer Users, is very useful for assessment of computer-related eye problems.