• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.1
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• pp.123-128
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• 2018

In this paper, we propose a multi-vehicle object detection algorithm using feature point matching that tracks efficient vehicle objects. The proposed algorithm extracts the feature points of the vehicle using the FAST algorithm for efficient vehicle object tracking. And True if the feature points are included in the image segmented into the 5X5 region. If the feature point is not included, it is processed as False and the corresponding area is blacked to remove unnecessary object information excluding the vehicle object. Then, the post processed area is set as the maximum search window size of the vehicle. And A minimum search window using the outermost feature points of the vehicle is set. By using the set search window, we compensate the disadvantages of the search window size of mean-shift algorithm and track vehicle object. In order to evaluate the performance of the proposed method, SIFT and SURF algorithms are compared and tested. The result is about four times faster than the SIFT algorithm. And it has the advantage of detecting more efficiently than the process of SUFR algorithm.

• Journal of the Korean Society of Costume
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• v.64 no.7
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• pp.57-67
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• 2014

This study aims to provide visual data from analysis of the Depaysement approaches with new viewpoints to inspire and develop new fashion window design ideas. The literature and existing researches related to Depaysement were analyzed for theoretical review, and Depaysement expression approaches were identified by expression characteristics. Theme concepts using traditional Korean images, which could be applied to fashion window displays in Korea, were established, and K(Korean)-fashion design was created to develop fashion window display design. Then, the Depaysement fashion window display was executed using Adobe Illustrator and Photoshop. The results of this study are summarized below. 'Change of forms and materials' could visualize the factors inducing curiosity, which can directly stimulate the consumption sentiment lying latent in the mind of observers by assigning new values to fashion goods displayed inside windows. Unconscious experience and remarkable stories, which are not possible to encounter in an everyday setting, can be visualized through the window display in 'heterogeneous combination of objects.' 'The location change of an object' could express the refreshing and shocking scene to give weird anxiety and mental contradiction to observers by fashion window display, which could break fixed idea of human beings. 'The change of object awareness' could express contradiction and denial, which could liberate the unconsciousness lying latent inside observers through fashion window display. 'Change of spatial awareness' could create the design which maximized the fashion images of goods displayed by helping the observers to change the space of their unconsciousness selectively at their will through the fashion window display with hidden, strange, ambiguous and variable image like a riddle.

• Korean Institute of Interior Design Journal
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• no.38
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• pp.233-240
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• 2003

The purpose of this study was to find the coordination trend of the Image of the color and object from the department show-window display locating in the kangnam area. The result of this study were summarized as following: 1) Show window display determinating the first image of department-store marketing service, playing the role of visual marketing provides the valuable impressions with shoppers in the times of sensibility, 2) In contemporary recognition of "Design is namely culture", show window display proceeds on the direction of concrete and practical presentation of merchandise as many customers are pursuing the high graded luxury brands while their life styles change. 3) Main concept is represented by the coordinated fashion goods on the mannequin or the body and the main theme is displayed variously in the circumferential area or on the articles with the abstract and concrete objects of diverse forms of dominant color and accent color conveying the seasonal theme precisely. 4) The compositive element of color Is the decisive factor of the visual sense of space In the coordination-trends of show window display specially representing the seasonal theme or the intentional messages and conduces to the psychological and mental desire in human and the circumstances, 5) Following the color, the compositive element of object presents the concrete image of theme or the abstract and geometrical sense of space besides the visual sense of space and shows the proportionality and the activity in displaying the show window space.dow space.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.3 s.357
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• pp.12-19
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• 2007

This paper proposes the enhanced auto-focus algorithm detecting several objects and selecting the target object. Proposed algorithm first detects some objects distributed in the image using focus measure operator and multi-window and then selects the target object through fuzzy reasoning with three fuzzy membership functions. Implementation can be simple because it only needs image sensor instead of infrared or ultrasonic equipment. Experimental result shows that the proposed algorithm can improve the quality of image by focusing to the target object.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.426-429
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• 2004

To reconstruct the 3-D information of a irregular object, this paper proposes a new method applying the composition focus measure to time-series image. A focus measure function is carefully selected because a focus measure is apt to be affected by the working environment and the characteristics of an object. The proposed focus measure function combines the variance measure which is robust to noise and the Laplacian measure which, regardless of an object shape, has a good performance in calculating the focus measure. And the time-series image, which considers the object shape, is proposed in order to efficiently applying the interesting window. This method, first, divides the image frame by the window. Second, the composition focus measure function be applied to the windows, and the time-series image is constructed. Finally, the 3-D information of an object is reconstructed from the time-series images considering the object shape. The experimental results have shown that the proposed method is suitable algorithm to 3-D reconstruction of an irregular object.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.166-172
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• 1994

This paper presents an image processing method for two dimensional position measurement of a floating structure. This method is based on image processing technique using concept of window and threshold processing to track the target object. The experimental results for position measurement of the target object in two dimensional water tank demonstrate the validity of this method.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.2
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• pp.64-76
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• 2022

Praat is a useful analysis tool for linguists, engineers, doctors, speech-language pathologits, music majors, and natural scientists. Basic parameters including duration, pitch, energy and perturbation parameters such as jitter and shimmer can be easily measured and manipulated in the sound editor. When a more in-depth analysis is needed, it is recommended to understand the advanced menus of the object window and learn how to use them. Among the object window menus, vowel formant analysis, spectrum analysis, and cepstrum analysis can be cited as useful ones in the clinical field. The spectrum object can be usefully used for voice quality measurement and diagnosis of patients with voice disorders by showing the energy distribution according to frequency axis (domain). A cepstrum object is useful for speech analysis when periodicity of the sound object is not measurable. The low to high ratio obtained from the spectral object and the CPPs measured from the cepstrum object have attracted many researchers, and it has been proven that the CPPs measured in Praat are relatively excellent.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.433-434
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• 2006

A new disparity estimation method in hierarchical frameworks is proposed. The two main ideas for improving accuracy are to obtain an object boundary map for distinction of homogeneous/object boundary region and to choose adaptive window size/shapes. Moreover, for the reduction of computational complexity, we change reference regions in hierarchical framework. The experimental results show that the proposed method can acquire good results which are robust to homogeneous and object boundary regions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.