• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.93-103
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• 2023

The problem of reconstructing three-dimensional models of people and objects from color and depth images captured by low-cost RGB-D cameras has long been an active research area in computer graphics. Color and depth images captured by low-cost RGB-D cameras are represented as point clouds in three-dimensional space, which correspond to discrete values in a continuous three-dimensional space and require additional surface reconstruction compared to rendering using polygonal models. In this paper, we propose an effective ray-tracing based technique for visualizing point clouds rather than polygonal models. In particular, our method shows the possibility of an effective rendering method even in mobile environment which has limited performance due to processor heat and lack of battery.

• Archives of design research
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• v.18 no.4 s.62
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• pp.45-52
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• 2005

Displays, playing an essential role in HCI, have been developed to deliver more vivid and live pictures. LCD and PDP have the most common usage among various displays to present the dynamic textual information in restricted and open places. Important and unsolicited information is being provided by hundreds of displays while we don't even perceive the information. Watching context, effective color combinations of the text and background, speeds of presentation, contexts, and types of display for leading method, moving words cross the display from right to left, were investigated with readability, comprehensibility, and overall satisfaction were compared. LCD peformed better than PDP on all comparisons u4th typical text (black, white) and background (white, black) color better than other color combinations. Normal and fast text presentation was believed more preferred when only textual information was given while slower presentation was evaluated more efficient if multimedia contents were presented with textual information.

• Journal of Advanced Navigation Technology
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• v.21 no.3
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• pp.251-257
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• 2017

Recently, hybrid positioning system combining GPS, vision sensor, and inertial sensor has drawn many attentions to estimate accurate vehicle positions. Since accurate multi-sensor fusion requires efficient time synchronization, this paper proposes an efficient method to obtain time synchronized measurements of vision sensor, inertial sensor, and OBD device based on GPS time information. In the proposed method, the time and position information is obtained by the GPS receiver, the attitude information is obtained by the inertial sensor, and the speed information is obtained by the OBD device. The obtained time, position, speed, and attitude information is converted to the color information. The color information is inserted to several corner pixels of the corresponding image frame. An experiment was performed with real measurements to evaluate the feasibility of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.2
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• pp.8-17
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• 2008

This paper proposes a robust license plate extraction method from images taken under unconstrained environments. Utilization of the color and the edge information in complementary fashion makes the proposed method deal with not only various lighting conditions, hilt blocking artifacts frequently observed in compressed images. Computational complexity is significantly reduced by applying Hough transform to estimate the skew angle, and subsequent do-skewing procedure only to the candidate regions. The true plate region is determined from the candidates under examination using clues including the aspect ratio, the number of zero crossings from vertical scan lines, and the number of connected components. The performance of the proposed method is evaluated using compressed images collected under various realistic circumstances. The experimental results show 94.9% of correct license plate extraction rate.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.84-90
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• 2020

As the uploading and downloading of data through the Internet is becoming more common, data including personal information are easily exposed to unauthorized users. In this study, we detect a target area in images that contain personal information, except for the background, and we protect the detected target area by using a blocking method suitable for the surrounding situation. In this method, only the target area from color image input containing personal information is segmented based on skin color. Subsequently, blurring of the corresponding area is performed in multiple stages based on the surrounding situation to effectively block the detected area, thereby protecting the personal information from being exposed. Experimental results show that the proposed method blocks the object region containing personal information 2.3% more accurately than an existing method. The proposed method is expected to be utilized in fields related to image processing, such as video security, target surveillance, and object covering.

• Journal of the Korea Computer Graphics Society
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• v.20 no.2
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• pp.13-24
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• 2014

In this study, a graphics-processing-unit (GPU)-based acceleration technique is proposed for the feature-based image morphing. This technique uses the depth-buffer of the graphics hardware to calculate efficiently the shortest distance between a pixel and the control lines. The pairs of control lines between the source image and the destination image are determined by user's input, and the distance function of each control line is rendered using two rectangles and two cones. The distance between each pixel and its nearest control line is stored in the depth buffer through the graphics pipeline, and this is used to conduct the morphing operation efficiently. The pixel-unit morphing operation is parallelized using the compute unified device architecture (CUDA) to reduce the morphing time. We demonstrate the efficiency of the proposed technique using several experimental results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.7
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• pp.582-589
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• 2012

This paper describes the algorithm recognizing car type from the image received from UAV and the recognition results between three types of car images. Using the NCC(Normalized Cross-Correlation) algorithm, geometric information is matched from template images. Template images are obtained from UAV and satellite map and indoor experiment is performed using satellite map. After verification of the possibility, experiment for verification of same car type recognition is performed using small UAV. In the experiment, same type cars are matched with 0.6 point similarity and truck with similar color distribution is not matched with template image of a sedan.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.217-224
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• 2024

With the rapid growth of K-POP, the dance content industry is spreading. With the recent increase in the spread of SNS, they also shoot and share their dance videos. However, it is not easy for dance beginners who are new to dancing to learn dance moves because it is difficult to receive objective feedback when dancing alone while watching videos. This paper describes a system that uses MediaPipe to compare choreography videos and dance videos of users and detect whether they are following the movement correctly. This study proposes a method of giving feedback based on Color Map to users by calculating the similarity of dance movements between user images taken with webcam or camera and choreography images using cosine similarity and COCO OKS. Through this system, objective feedback on users' dance movements can be visually received, and beginners are expected to be able to learn accurate dance movements.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1142-1153
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• 2010

Conjunctival injection is the initial symptom of various eye diseases such as conjunctivitis, keratitis, or uveitis. The quantification of conjunctival injection may help the diagnosis and follow-up evaluation of various eye diseases. The size of the sclera is an important factor for the quantification of conjunctival injection. However, previous manual segmentation is time-consuming.Automatic segmentation is needed to extract the objective region of interest. This paper proposed a method based on the level set algorithm to segment the sclera from an anterior eye image. The initial model of the level set algorithm is calculated using the Lab color space, k-means algorithm and the geometric information. The level set algorithm was applied to the images in which the valley between the eyeball and skin was enhanced using the hessian analysis. This algorithm was tested with 52 images of the anterior eye segment. Results showed that the proposed method performs better than those with the level set algorithm using an arbitrary circle, or the region growing algorithm with color information. The proposed method for the segmentation of sclera may become an important component for the objective measurement of the conjunctival injection.