• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.113-119
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• 2013

The requirement of rehabilitation is increasing from the stroke, spinal cord injury. One of the most difficult part is the upper limb rehabilitation because of its nervous complexity. A rehabilitation has effectiveness when a professional therapist treats in work at facility, but it has problems of an accessibility, a constant availability, a self-participation and taking lots of cost and time. In this paper, we test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it on the upper limb function from the 3D trajectory information which is gathered from stereo vision sensor(Kinect). From the result, PCA, ICA have low accuracy, but LDA, SVM have good accuracy to use for physical rehabilitation on the upper limb function.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.2
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• pp.86-94
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• 2009

This paper deals with the creation of a new, low-cost point/position tracking system that can measure deformations in engineering structures with simple commercially widespread cameras. Though point tracking systems do exist today, such as Stereo Pattern Recognition (SPR) and Projection Moir$\acute{e}$ Interferometry (PMI) systems, they are far too costly to use to analyze small, simple structures because complex optical components such as large flashes, high-resolution cameras and data acquisition systems with several computers are required. We developed a point tracking system using commercial cameras. This system used IR LEDs and commercial IR CCD cameras to minimize the interference posed by other extraneous light sources. The main algorithm used for this system is an optical point tracking algorithm, which is composed of the point extraction algorithm and the point matching algorithm for 3-D motion estimation. a series of verification tests were performed. Then, the developed point tracking system was applied to measure deformations of an acrylic plate under a mechanical load. The measured deformations of the acrylic plate matched well with the numerical analysis results. The results indicate that the developed point tracking system is reliable enough to measure continuous deformed shapes of various engineering structures.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.35-42
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• 2014

It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.

• 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.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.475-488
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• 2018

Surveying urban tributary vegetation is based mainly on field sampling at present. The tributary vegetation survey integrating UAV NIR(Unmanned Aerial Vehicle Near Infrared Radiance) imagery and in-situ point photo has received only limited attentions from the field ecologist. The reason for this could be the largely undemonstrated applicability of UAV NIR imagery by the field ecologist as a monitoring tool for urban tributary vegetation. The principal advantage of UAV NIR imagery as a remote sensor is to provide, in a cost-effective manner, information required for a very narrow swath target such as urban tributary (10m width or so), utilizing very low altitude flight, real-time geo-referencing and stereo imaging. An exhaustive and realistic comparison of the two techniques was conducted, based on operational customer requirement of urban tributary vegetation survey: synoptic information, ground detail and quantitative data collection. UAV NIR imagery made it possible to identify area-wide patterns of the major plant communities subject to many different influences (e.g. artificial land use pattern), which cannot be acquired by traditional field sampling. Although field survey has already gained worldwide recognition by plant ecologists as a typical method of urban tributary vegetation monitoring, this approach did not provide a level of information that is either scientifically reliable or economically feasible in terms of urban tributary vegetation (e.g. remedial field works). It is anticipated that this research output could be used as a valuable reference for area-wide information obtained by UAV NIR imagery in urban tributary vegetation survey.