• The Korean Journal of Community Living Science
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• v.16 no.2
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• pp.51-59
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• 2005

The purpose of the present study is to seek measures to increase the quality of farmers lives by considering the results of a survey on the welfare demand for them when setting up policy. The results are as follows: First, though many concerns and policies to control the rural exodus have been exerted in terms of maintaining agricultural communities, which is one of critical components for the development of Korean agriculture and farming villages, the rural exodus became more and more serious. It is urgently required, therefore, for the Korean administration to correspond actively to the reasons why farmers depart their villages, such as bad prospects, low incomes or problems with their children's education. Second, as the number of females who take part in farming has increased, it is necessary to reform the rural educational environment. In addition, as the agricultural community develops, and other industries flow in, the greater the worries about crime, and consequently, it is necessary to equip a local safety system, share information on job opportunities, and provide educational opportunities. Third, since rural districts nay have financial difficulties because of old-aged and small-sized farmhouse, it is necessary to mainly support the welfare of the aged and lower-income farmers, considering the characteristics of each group and area. Fourth, over half of the farmers responded that they cannot bear to pay the national pension and health insurance premiums, and therefore it should be considered to reduce the pension and premium, or to readjust the insurance value appropriate to the level of the farmer's income. Fifth, despite a number of agricultural policies now in place, farmers desire a policy which can promise a prosperous vision for Korean agriculture and the facilities needed for education and health. It is necessary for the administration to set up policies that are characterized by locality with the view that farmers are not mere recipients, but participants in the policies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2163-2170
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• 2009

We present an automated, energy minimized-based method for Lung parenchyma segmenting Chest Computed Tomography(CT) datasets. Deformable model is used for energy minimized segmentation. Quantitative knowledge including expected volume, shape of Chest CT provides more feature constrain to diagnosis or surgery operation planning. Segmentation subdivides an lung image into its consistent regions or objects. Depends on energy-minimizing, the level detail image of subdivision is carried. Segmentation should stop when the objects or region of interest in an application have been detected. The deformable model that has attracted the most attention to date is popularly known as snakes. Snakes or deformable contour models represent a special case of the general multidimensional deformable model theory. This is used extensively in computer vision and image processing applications, particularly to locate object boundaries, in the mean time a new type of external force for deformable models, called gradient vector flow(GVF) was introduced by Xu. Our proposed algorithm of deformable model is new external energy of GVF for exact segmentation. In this paper, Clinical material for experiments shows better results of proposal algorithm in Lung parenchyma segmentation on Chest CT.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.371-384
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• 2019

Motion perception has been tremendously improved in neuroscience and computer vision. The baseline motion perception model is mediated by the dorsal visual pathway involving the cortex areas the primary visual cortex (V1) and the middle temporal (V5 or MT) visual area. However, few works have been done on the extension of neural models to improve the efficacy and robustness of motion perception of real sequences. To overcome shortcomings in situations, such as varying illumination and large displacement, an adaptive V1-MT motion perception (Ad-V1MTMP) algorithm enriched to deal with real sequences is proposed and analyzed. First, the total variation semi-norm model based on Gabor functions (TV-Gabor) for structure-texture decomposition is performed to manage the illumination and color changes. And then, we study the impact of image local context, which is processed in extra-striate visual areas II (V2), on spatial motion integration by MT neurons, and propose a V1-V2 method to extract the image contrast information at a given location. Furthermore, we take feedback inputs from V2 into account during the polling stage. To use the algorithm on natural scenes, finally, multi-scale approach has been used to handle the frequency range, and adaptive pyramidal decomposition and decomposed spatio-temporal filters have been used to diminish computational cost. Theoretical analysis and experimental results suggest the new Ad-V1MTMP algorithm which mimics human primary motion pathway has universal, effective and robust performance.

• Journal of Aerospace System Engineering
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• v.16 no.4
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• pp.109-115
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• 2022

The CNN (Convolution Neural Network) algorithm which combines a deep learning technique, and a computer vision technology, makes image classification feasible with the high-performance computing system. In this thesis, the CNN algorithm is applied to the classification problem, by using a typical deep learning framework of TensorFlow and machine learning techniques. The data set required for supervised learning is generated with the same type of bolts. some of which have undamaged threads, but others have damaged threads. The learning model with less quantity data showed good classification performance on detecting damage in a bolt image. Additionally, the model performance is reviewed by altering the quantity of convolution layers, or applying selectively the over and under fitting alleviation algorithm.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.133-146
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• 2023

Traffic states in urban areas are essential to implement effective traffic operation and traffic control. However, installing traffic sensors on numerous road sections is extremely expensive. Accordingly, estimating the traffic state using a vehicle-mounted camera, which shows a high penetration rate, is a more effective solution. However, the previously proposed methodology using object tracking or optical flow has a high computational cost and requires consecutive frames to obtain traffic states. Accordingly, we propose a method to detect vehicles and lanes by object detection networks and set the region between lanes as a region of interest to estimate the traffic density of the corresponding area. The proposed method only uses less computationally expensive object detection models and can estimate traffic states from sampled frames rather than consecutive frames. In addition, the traffic density estimation accuracy was over 90% on the black box videos collected from two buses having different characteristics.

• Geomechanics and Engineering
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• v.33 no.2
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• pp.175-182
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• 2023

In general, the design of structures and its construction processes are fundamentally dependent on their foundation and supporting ground. Thus, it is imperative to understand the behavior of the soil under certain stress and drainage conditions. As it is well known that certain characteristics and behaviors of soils with fines are highly dependent on water content, it is critical to accurately measure and identify the status of the soils in terms of water contents. Liquid limit is one of the important soil index properties to define such characteristics. However, liquid limit measurement can be affected by the proficiency of the operator. On the other hand, dynamic properties of soils are also necessary in many different applications and current testing methods often require special equipment in the laboratory, which is often expensive and sensitive to test conditions. In order to address these concerns and advance the state of the art, this study explores a novel method to determine the liquid limit of cohesive soil by employing video-based vibration analysis. In this research, the modal characteristics of cohesive soil columns are extracted from videos by utilizing phase-based motion estimation. By utilizing the proposed method that analyzes the optical flow in every pixel of the series of frames that effectively represents the motion of corresponding points of the soil specimen, the vibration characteristics of the entire soil specimen could be assessed in a non-contact and non-destructive manner. The experimental investigation results compared with the liquid limit determined by the standard method verify that the proposed method reliably and straightforwardly identifies the liquid limit of clay. It is envisioned that the proposed approach could be applied to measuring liquid limit of soil in practical field, entertaining its simple implementation that only requires a digital camera or even a smartphone without the need for special equipment that may be subject to the proficiency of the operator.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.938-958
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• 2024

Unsupervised Video Object Segmentation (UVOS) is a highly challenging problem in computer vision as the annotation of the target object in the testing video is unknown at all. The main difficulty is to effectively handle the complicated and changeable motion state of the target object and the confusion of similar background objects in video sequence. In this paper, we propose a novel deep Dual-stream Co-enhanced Network (DC-Net) for UVOS via bidirectional motion cues refinement and multi-level feature aggregation, which can fully take advantage of motion cues and effectively integrate different level features to produce high-quality segmentation mask. DC-Net is a dual-stream architecture where the two streams are co-enhanced by each other. One is a motion stream with a Motion-cues Refine Module (MRM), which learns from bidirectional optical flow images and produces fine-grained and complete distinctive motion saliency map, and the other is an appearance stream with a Multi-level Feature Aggregation Module (MFAM) and a Context Attention Module (CAM) which are designed to integrate the different level features effectively. Specifically, the motion saliency map obtained by the motion stream is fused with each stage of the decoder in the appearance stream to improve the segmentation, and in turn the segmentation loss in the appearance stream feeds back into the motion stream to enhance the motion refinement. Experimental results on three datasets (Davis2016, VideoSD, SegTrack-v2) demonstrate that DC-Net has achieved comparable results with some state-of-the-art methods.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.531-536
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• 2024

Control drones, which are recently classified as smart agricultural machines in the agricultural field, are striving to build smart control and automatic control systems by combining hardware and software in order to shorten working hours and increase the effectiveness of control in the aging era of rural areas. In this paper, the characteristics of the nozzle dedicated to the control drone were analyzed as a basic study for the establishment of management control and automatic control systems. In order to consider various variables such as the type of various drone models, controller, wind, flight speed, flight altitude, weather conditions, and UAV pesticide types, related studies are needed to be able to present the drug spraying criteria in consideration of the characteristics and versatility of the nozzle. Therefore, to enable the consideration of various variables, flow analysis (CFD) simulation was conducted based on the self-designed nozzle, and the theoretical and experimental values of the droplet distribution were compared and analyzed through water reduction experiments. In the future, we intend to calculate accurate scattering in consideration of various variables according to drone operation and use it in management control and automatic control systems.

• International Journal of Highway Engineering
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• v.10 no.2
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• pp.159-166
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• 2008

Video image processing systems (VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on a wide-area detection algorithm provide traffic parameters such as flow and velocity as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. However, unlike human vision, VIPS cameras have difficulty in recognizing vehicle movements over a detection zone longer than 100 meters. Over such a distance, the camera operators need to zoom in to recognize objects. As a result, vehicle tracking with a single camera is limited to detection zones under 100m. This paper develops a methodology capable of monitoring individual vehicle trajectories based on image processing. To improve traffic flow surveillance, a long distance tracking algorithm for use over 200m is developed with multi-closed circuit television (CCTV) cameras. The algorithm is capable of recognizing individual vehicle maneuvers and increasing the effectiveness of incident detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.135-140
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• 2012

The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer's range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.