• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.75-78
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• 2022

Deep Neural Networks (DNN) has become an essential data processing architecture for the implementation of multiple computer vision tasks. Recently, DNN-based algorithms achieve much higher recognition accuracy than traditional algorithms based on shallow learning. However, training and inference DNNs require huge computational capabilities than daily usage purposes of computers. Moreover, with increased size and depth of DNNs, CPUs may be unsatisfactory since they use serial processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. In this paper, we analyze the inference time complexity of DNNs using well-known computer vision library, OpenCV. We measure and analyze inference time complexity for three cases, CPU, GPU-Float32, and GPU-Float16.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.8
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• pp.175-183
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• 1996

This paper presents three-dimensional robot task using the vision control method. A minimum of two cameras is required to place points on end dffectors of n degree-of-freedom manipulators relative to other bodies. This is accomplished using a sequential estimation scheme that permits placement of these points in each of the two-dimensional image planes of monitoring cameras. Estimation model is developed based on a model that generalizes known three-axis manipulator kinematics to accommodate unknown relative camera position and orientation, etc. This model uses six uncertainty-of-view parameters estimated by the iteration method.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.1
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• pp.37-55
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• 2023

In this paper we propose a new algorithm for detecting and counting flowers in a complex background based on digital images. The algorithm mainly includes the following parts: edge contour extraction of flowers, edge contour determination of overlapped flowers and flower counting. We use a contour detection technique in Computer Vision (CV) to extract the edge contours of flowers and propose an improved algorithm with a concave point detection technique to find accurate segmentation for overlapped flowers. In this process, we first use the polygon approximation to smooth edge contours and then adopt the second-order central moments to fit ellipse contours to determine whether edge contours overlap. To obtain accurate segmentation points, we calculate the curvature of each pixel point on the edge contours with an improved Curvature Scale Space (CSS) corner detector. Finally, we successively give three adaptive judgment criteria to detect and count flowers accurately and automatically. Both experimental results and the proposed evaluation indicators reveal that the proposed algorithm is more efficient for flower counting.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.55-56
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• 2020

The construction industry has the highest occupational fatality and injury rates related to accidents of any industry. Accordingly, safety managers closely monitor to prevent accidents in real-time by installing surveillance cameras at construction sites. However, due to human cognitive ability limitations, it is impossible to monitor many videos simultaneously, and the fatigue of the person monitoring surveillance cameras is also very high. Thus, to help safety managers monitor work and reduce the occupational accident rate, a study on object recognition in construction sites was conducted through surveillance cameras. In this study, we applied to the instance segmentation to identify the classification and location of objects and extract the size and shape of objects in construction sites. This research considers ways in which deep learning-based computer vision technology can be applied to safety management on a construction site.

• Journal of The Korean Association For Science Education
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• v.27 no.5
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• pp.456-464
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• 2007

The aims of this research are to analyze the representations and conceptual sequence of eye vision in Korean science textbooks and to suggest a contents construct about eye vision where the conceptual sequence is considered. Research method was literature review, and the literatures that were used for analysis were the 7th Korean science curriculum which was revised in 1997, and the science and physics textbooks developed based on the 7th Korean science curriculum. The research results are as follows: 1) Although the science curriculum seems to have no problem on sequence in the eye vision concepts, the science and physics textbooks based on the curriculum reveal problems on the sequence in the eye vision concepts; 2) Some Korean science textbooks explain retinal image formation according to the Alhazen's idea, except in inverse image; 3) Some Korean science textbooks explain about the reasons of near- and far-sightedness without consistency between the textbooks for 7th and 8th grade students; 4) A few Korean science textbooks give an inappropriate explanation about the principle of eye sight correction by eye glasses; 5) According to the analysis result, the concepts related to eye vision should be presented in the order of explanation about light refraction phenomena, image formation process by convex lens, structure of human eye and retinal image formation process, correction of eye sight using lens.

• Journal of Korean Academy of Nursing
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• v.23 no.4
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• pp.678-693
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• 1993

The purposes of this study were to describe the level of vision and hearing impairments, depression and functional capacity, among Korean institutionalized elderly and to examine the relation-ship between sensory impairments, depression, and functional capacity in these people. The final pupose was to test the cognitive function path model using sensory competencies as predictors. A convenience sample of thirty nine male and 90 female subjects with a mean age of 80.5 were the subjects of this study. The subjects were tested for cognitive function, and vision and hearing impairments. Physical function and social function were measured by observation of designated task performance by the subjects. Their level of de-pression was measured using a Geriatric Depression Scale administered through an interview. Individual subjective ratings of hearing and vision were marked by the subjects, on a ladder scale. The results of the study showed that 48.8% of the subjects had a hearing impairment, 63.5% had a vision impairement, and 36.4% had both a vision and hearing impairement. The four sensory groups (no sensory impairement, hearing impairement, vision impairement, hearing and vision impairement) were tested for differences in depression, physical function, social behavior and cognitive function. The only significant difference that was found was in cognitive function, between the no sensory impairement group and the hearing and vision impairement group(F=3.25, P<.05), Subjective ratings of hearing showed a significant correlation with cognitive function(r=.34, p<.001) and with social behavior(r=.31, p<.001). There was no correlation between subjective vision ratings and cognitive function or social behavior. However there was a significant correlation between vision and hearing(r=.49, p<.001). There was also a significant negative correlation between age and vision(r=-.21, p<.01) and between age and hear-ing(r=-.34, p<.001). There was a significant correlation between depression and physical function (r=-.32, p<.001) but there was no correlation between depression and cognitive function or social behavior. Based on the literature review and the result, this study, a path model of sensory competence-> cognitive function- >social behavior was developed and tested : Perceived vision and perceived hearing were the exogenous variahles and cognitive function and social behavior were the endogeneous variables in the model. The path analysis result demonstrated an accept-able fit (GFI=.997, AGFI=.972, X$^2$=.72 (p=.396), RMSR=.019) between the data and the model. There was a significant direct effect($\beta$=.38) of perceived hearing on cognitive function. There was a significant direct effect ($\beta$=.32) of cognitive function on social behavior. The total effect of hearing on social behavior was $\beta$=.32 including the indirect effect ($\beta$=.12) . However perceived vsion had little effect ($\beta$=-.08) on cognitive function. The result of path analysis confirms that hearing levels influence cognitive function, and both hearing and cognitive function levels influence social behavior. However, vision has little effect on cognitive function or on social behavior. For the next study, a combined model of the pre viously developed environment - >depression- > physical and social function model, and the present cognitive function model, should be tested to further refine the functional capacity model. There also a need for longitudinal study of functional capacity and sencory competence in order to better understand how declining sensory competence influences functional capacity and how it effects in-creasing dependency and nursing needs in the elderly.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.273-282
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• 2022

MPEG-VCM(Video Coding for Machine) aims to standardize video codec for machines. VCM provides data sets and anchors, which provide reference data for comparison, for several machine vision tasks including object detection, object segmentation, and object tracking. The evaluation template can be used to compare compression and machine vision task performance between anchor data and various proposed video codecs. However, performance comparison is carried out separately for each machine vision task, and information related to performance evaluation of multiple machine vision tasks on a single bitstream is not provided currently. In this paper, we propose a performance evaluation method of a video codec for AI-based multi-tasks. Based on bits per pixel (BPP), which is the measure of a single bitstream size, and mean average precision(mAP), which is the accuracy measure of each task, we define three criteria for multi-task performance evaluation such as arithmetic average, weighted average, and harmonic average, and to calculate the multi-tasks performance results based on the mAP values. In addition, as the dynamic range of mAP may very different from task to task, performance results for multi-tasks are calculated and evaluated based on the normalized mAP in order to prevent a problem that would be happened because of the dynamic range.

• Journal of Korean Clinical Nursing Research
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• v.27 no.1
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• pp.54-65
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• 2021

Purpose: The purpose of this study was to identify influencing factors on vision-related quality of life in patients with retinal diseases receiving intravitreal injections by examining relationships among anxiety, depression, coping, eye health behaviors and vision-related quality of life. Methods: One hundred and five outpatients who were diagnosed with macular degeneration and diabetic retinopathy were recruited from one university hospital during August 16, 2019 to March 25, 2020. Data were analyzed using descriptive statistics (frequency and percentage, mean, standard deviation), and t-tests, ANOVA, Scheffé test, Pearson's correlations, and stepwise multiple regressions using the IBM SPSS Statistics 25.0. Results: The vision-related quality of life according to general characteristics of retinal disease patients with intravitreal injection showed significant differences in age (F=3.01, p=.034), subjective economic status (F=5.83, p=.004), types of retinal disease (t=2.62, p=.010), and disease in both eyes (t=-3.04, p=.003). The vision-related quality of life showed a significant positive correlation with age (r=.24, p=.012), and negative correlations with anxiety (r=-.66, p<.001), depression (r=-.48, p<.001), and emotion-focused coping (r=-.20, p=.036). The hierarchical regression analysis indicated that factors affecting vision-related quality of life in patients with retinal diseases were anxiety and subjective economic status, accounting for 47.0% of the variances of the vision-related quality of life. Conclusion: Based on our results, health professionals need to pay attention to patients with low socioeconomic status due to frequent treatments. Also, a program needs to be developed to decrease anxiety for outpatients receiving intravitreal injections to improve their vision-related quality of life.

• Agricultural and Biosystems Engineering
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• v.3 no.1
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• pp.35-43
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• 2002

The spectral color resolution of an image is very important in color image analysis. Two factors influencing the spectral color resolution of an image are illumination intensity and lens aperture for a selected vision system. An optimal combination of illumination intensity and lens aperture for color image analysis was determined in the study. The method was based on a model of dynamic range defined as the absolute difference between digital values of selected foreground and background color in the image. The role of illumination intensity in machine vision was also described and a computer program for simulating the optimal combination of two factors was implemented for verifying the related algorithm. It was possible to estimate the non-saturating range of the illumination intensity (input voltage in the study) and the lens aperture by using a model of dynamic range. The method provided an optimal combination of the illumination intensity and the lens aperture, maximizing the color resolution between colors of interest in color analysis, and the estimated color resolution at the combination for a given vision system configuration.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.3-10
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• 2003

This paper describes two kinds of neural networks for stereoscopic vision, which have been applied to an identification of human speech. In speech recognition based on the stereoscopic vision neural networks (SVNN), the similarities are first obtained by comparing input vocal signals with standard models. They are then given to a dynamic process in which both competitive and cooperative processes are conducted among neighboring similarities. Through the dynamic processes, only one winner neuron is finally detected. In a comparative study, with, the average phoneme recognition accuracy on the two-layered SVNN was 7.7% higher than the Hidden Markov Model (HMM) recognizer with the structure of a single mixture and three states, and the three-layered was 6.6% higher. Therefore, it was noticed that SVNN outperformed the existing HMM recognizer in phoneme recognition.