• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.889-894
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• 2009

Accompanied by the rapid development of Computer Vision, Visual surveillance has achieved great evolution with more and more complicated processing. However there are still many problems to be resolved for robust and reliable visual surveillance, and the cast shadow occurring in motion detection process is one of them. Shadow pixels are often misclassified as object pixels so that they cause errors in localization, segmentation, tracking and classification of objects. This paper proposes a novel cast shadow removal method. As opposed to previous conventional methods, which considers pixel properties like intensity properties, color distortion, HSV color system, and etc., the proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the background scene. Then, the product of the outcomes of application determines whether the blob pixels in the foreground mask comes from object blob regions or shadow regions. The proposed method is simple but turns out practically very effective for Gaussian Mixture Model, which is verified through experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.746-750
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• 2005

This paper describes image processing techniques that can detect, segment, and analyze the mango's physical properties such as size, shape, surface area, and color from images. First, images of mangoes taken by a digital camera are analyzed and segmented. The segmentation is done based on constructed hue model of the sample mangoes. Some morphological and filtering techniques are then applied to clean noises before fitting spline curve on the mango boundary. From the clean segmented image, the mango projected area can be computed. The shape of the mango is then analyzed using some structuring models. Color is also spatially analyzed and indexed in the database for future classification. To obtain the surface area, the mango is peeled. The scanned image of its peels is then segmented and filtered using similar approach. With calibration parameters, the surface area could then be computed. We employed the system to evaluate physical properties of a mango cultivar called "Nam Dokmai". There were sixty mango samples in three various sizes graded by an experienced farmer's eyes and hands. The results show the techniques could be a good alternative and more feasible method for grading mango comparing to human's manual grading.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.5
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• pp.557-563
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• 2016

Most of the existing hand detection methods rely on the contour shape of hand after skin color segmentation. Such contour shape based computations, however, are not only susceptible to noise and other skin color segments but also inherently sequential and difficult to efficiently parallelize. In this paper, we implement and accelerate our in-house distance image based approach using CPU-GPU heterogeneous computing. Using emerging CPU-GPU heterogeneous computing technology, we achieved 5.0 times speed-up for $320{\times}240$ images, and 17.5 times for $640{\times}480$ images and our experiment demonstrates that our proposed distance image based hand detection is robust and fast, reaching up to 97.32% palm detection rate, 80.4% of which have more than 3 fingers detected on commodity processors.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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• pp.930-935
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• 2014

A Gabor filter is a linear filter used for edge detectionas frequency and orientation representations of Gabor filters are similar to those of the human visual system. In this thesis, we propose a pedestrian detection algorithm using a Gabor filter bank. In order to extract the features of the pedestrian, we use various image processing algorithms and data structure algorithms. First, color image segmentation is performed to consider the information of the RGB color space. Second, histogram equalization is performed to enhance the brightness of the input images. Third, convolution is performed between a Gabor filter bank and the enhanced images. Fourth, statistical values are calculated by using the integral image (summed area table) method. The calculated statistical values are used for the feature matrix of the pedestrian area. To evaluate the proposed algorithm, the INRIA pedestrian database and SVM (Support Vector Machine) are used, and we compare the proposed algorithm and the HOG (Histogram of Oriented Gradient) pedestrian detector, presentlyreferred to as the methodology of pedestrian detection algorithm. The experimental results show that the proposed algorithm is more accurate compared to the HOG pedestrian detector.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.30-38
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• 2006

This paper is used the color space distribution HMMD model presented in MPEG-7 in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the video object segmentation. Here, it is applied the wavelet morphology to remove a small part that is regarded as a noise in image and a part excepting for the face image. Also, it did the optimal composition by the rough set. In this paper, tile proposed video object detection algorithm is confirmed to be superior as detecting the face object exactly than the conventional algorithm by applying those to the different size images.put the of paper here.

• Journal of the Korea Society of Computer and Information
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• v.17 no.10
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• pp.43-53
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• 2012

In the this paper, a gesture-based user interface based on object detection using predefined gesture and the tracking of the detected object is proposed. For object detection, moving objects in a frame are computed by comparing multiple previous frames and predefined gesture is used to detect the target object among those moving objects. Any object with the predefined gesture can be used to control. We also propose an object tracking algorithm, namely density based meanshift algorithm, that uses color distribution of the target objects. The proposed object tracking algorithm tracks a target object crossing the background with a similar color more accurately than existing techniques. Experimental results show that the proposed object detection and tracking algorithms achieve higher detection capability with less computational complexity.

• Journal of Biosystems Engineering
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• v.17 no.4
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• pp.354-362
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• 1992

This study was intended to examine feasibility of sizing and color grading of Fuji apple with black/white image processing system, to develop a device with which the whole surface of an apple could be captured by one camera, and to develop an algorithm for a high speed sorting. The results are summarized as follows : 1. The black/white image processing system used in this study showed a maximum error of 1.3% in area measurement with a reference figure while the focusing point of camera and location of the reference figure were changed within a certain range. 2. As the result of evaluating four automatic image segmentation algorithms with apple images, Histogram Clustering Method was the best in terms of computation time and accuracy. 3. The fast algorithm for analyzing size and coloration of apple was developed. 4. The whole surface of an apple could be captured in an image frame with two mirrors installed on the both sides of the sample. The total area of the image representing the whole surface showed a correlation of 0.995 with the weight of apple. 5. The gray level when a particular band pass filter was mounted on the camera showed high correlation with 'L' and 'a' values of Hunt color scale and could represent the coloration of apple.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.145-150
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• 2014

This paper presents an effective vision based method for LED traffic light detection at the daytime. First, the proposed method calculates horizontal coordinates to set region of interest (ROI) on input sequence images. Second, the proposed uses color segmentation method to extract region of green and red traffic light. Next, to classify traffic light and another noise, shape filter and haar-like feature value are used. Finally, temporal delay filter with weight is applied to remove blinking effect of LED traffic light, and state and weight of traffic light detection are used to classify types of traffic light. For simulations, the proposed method is implemented through Intel Core CPU with 2.80 GHz and 4 GB RAM, and tested on the urban and rural road video. Average detection rate of traffic light is 94.50 % and average recognition rate of traffic type is 90.24 %. Average computing time of the proposed method is 11 ms.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.461-463
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• 2023

MZ세대를 중심으로 자기관리를 열심히 하는 사람들이 증가함에 따라 화장의 기본이 되는 개인 피부톤(퍼스널 컬러)을 찾는 것이 중요시되고 있다. 현재 대다수 사람은 자신에게 어울리는 퍼스널 컬러를 찾기 위해 높은 비용을 지불하여 전문가를 이용하거나 객관적이고 정량화된 기준 없이 오랜 시간을 투자하여 스스로 퍼스널 컬러를 찾는 등 시간과 비용 측면에서의 한계점을 가지고 있다. 본 논문에서는 이를 보완하기 위해 이미지 기반 인공지능 기술(객체 탐지, 객체 분할, BeautyGAN)을 적용하여 데이터 기반의 정량적인 기준을 생성하고, 퍼스널 컬러에 알맞은 화장품 추천 웹 서비스를 제안한다.