• Journal of Institute of Control, Robotics and Systems
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• v.9 no.5
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• pp.361-367
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• 2003

We propose an algorithm to detect the face region using a variable ellipsoidal mask and a neural network. Since outlines of human faces are similar to ellipsoid, the ellipsoidal mask that has the fixed ratio of major and minor axis can be used to detect the candidate area. The positions of eyes and lips are extracted in this candidate area, and then the morphological analysis is applied to make features which are consist of six parameters, such as the geometrical ratio of eyes and lips. A back-propagation neural network is used as a classifier to determine the most possible face region. The experimental result is conducted to verify its efficiency compared with those of previous works.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.251-256
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• 1993

In KACC'91 and '92 conference, we proposed a method of automatically detecting the shape of the faulty holes in a shadow mask by use of CCD ca.mera and image data processing technic. In this method, two adjoining test areas from one image data. of the shadow mask are taken and comparing the shape of holes in these two areas, we can detect the faults in the shadow mask. In this paper, a method is described by use of spatial filtering of effectively finding the faulty holes from the difference image data between the two tested image data. The main role of the filter is to remove sampling errors occurring at the edge of the holes. And the second role is not only to find the existence of faulty holes but also exactly express the shape of faulty holes. Computer simulations and actual experiments with shadow masks have shown that this method of fault detection is very effective for practical use.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.171-176
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• 1998

This paper resents a spatio-temporal video segmentation method. The algorithm segments each frame of video sequences captured by a static or moving camera into moving objects (foreground) and background using a statistical hypothesis test. In the proposed method, three consecutive image frames are exploited and a hypothesis testing is performed by comparing two means from two consecutive difference images, which results in a T-test. This hypothesis test yields change detection mask that indicates moving areas (foreground) and non-moving areas (background). Moreover, an effective method for extracting object mask form change detection mask is proposed.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.97-98
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• 2022

코로나바이러스-19 팬데믹 이후 매일 수만 명의 환자가 발생하고 있다. 보건당국은 사람들의 생활 안전을 보호하기 위해 공항, 정류장 등 공공장소에서는 반드시 마스크를 착용하라고 지시하고 있다. 마스크를 착용하는 목적은 감염으로부터 신체를 보호하고 바이러스 전파와 확산을 막기 위한 것이다. 공공장소에서는 많은 인원에 대한 일괄적인 마스크 착용 검사를 하기 어렵고, 육안으로 확인하는 마스크 착용 검사 방법은 인파가 몰리는 장소에서 검사 효율이 떨어지며 누락되는 경우도 많이 발생한다. 본 연구에서는 입력 이미지에 존재하는 얼굴 영역을 YOLOv4와 YOLOv5 모델을 통해 예측하여 마스크의 착용 여부를 판단하되, 앙상블 기법을 적용하여 보다 효과적인 BB(Bounding Box) 추출 및 마스크 착용 탐지 기법을 적용한다. 따라서 공공장소의 마스크 착용실태를 효과적으로 모니터링 할 수 있는 방법을 제안한다.

• Journal of Broadcast Engineering
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• v.25 no.6
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• pp.944-953
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• 2020

Object detection plays a crucial role in a self-driving system. With the advances of image recognition based on deep convolutional neural networks, researches on object detection have been actively explored. In this paper, we proposed a lightweight model of the mask R-CNN, which has been most widely used for object detection, to efficiently predict location and shape of various objects on the road environment. Furthermore, feature maps are adaptively re-calibrated to improve the detection performance by applying an attention module to the neural network layer that plays different roles within the mask R-CNN. Various experimental results for real driving scenes demonstrate that the proposed method is able to maintain the high detection performance with significantly reduced network parameters.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2441-2449
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• 2015

In this digital information era, utilization of images are essential for various media, and the edge is an important characteristical information of an object in images that includes the size, location, direction and etc. Many domestic and international studies are being conducted in order to detect these edge. Existing edge detection methods include Sobel, Prewitt, Roberts, Laplacian, LoG and etc. which apply fixed weight value. As these existing edge detection methods apply fixed weight mask to the image, edge detection characteristic appears slightly insufficient. Accordingly, in order to supplement these problems, this study used bottom-hat transformation from mathematical morphology and opening operation in improving the image and proposed an algorithm that detects for the edge after calculating mask-based gradient. And to evaluate the performance of the proposed algorithm, a comparison was made against the existing Sobel, Roberts, Prewitt, Laplacian, LoG edge detection methods, in illustrating visual images, and similarities were compared by calculating the MSE value based on the standard of each image.

• Journal of the Korea Society of Computer and Information
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• v.15 no.2
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• pp.55-64
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• 2010

This paper proposes a face detection method using Free Rectangle feature which possesses a quick execution time and a high efficiency. The proposed mask of Free Rectangle feature is composed of two separable rectangles with the same area. In order to increase the feature diversity, Haar-like feature generally uses a complex mask composed of two or more rectangles. But the proposed feature mask can get a lot of very efficient features according to any position and scale of two rectangles on the feature window. Moreover, the Free Rectangle feature can largely reduce the execution time since it is defined as the only difference of the sum of pixels of two rectangles irrespective of the mask type. Since it yields a quick detection speed and good detection rates on real world images, the proposed face detection method based on Adaboost algorithm is easily applied to detect another object by changing the training dataset.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.630-632
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• 2012

Contemporary society has evolved in the digital information age. Because of this, use of various digital images has been increased. To process these images, various digital image processing methods are used. Edge detection methods, one of those, are utilized to various areas of application such as object recognition, line detection. To detect edge, there are many methods such as Sobel, Prewitt, Laplacian. Because images which are dealt with existing methods are processed in same methods regardless the distribution of gray-level in image, edge detection property is insufficient. Therefore, In this study, to improve shortcomings of existing methods an algorithm using modified expansion mask is proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.718-723
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• 2013

In general, the boundary portion of the background and objects are the rapidly changing point and an important elements to analyze characteristics of image. Using these boundary parts, information about the position or shape of an object in the image are detected, and many studies have been continued in order to detect it. Existing methods are that implementation of algorithm is comparatively simple and its processing speed is fast, but edge detection characteristics is insufficient because weighted values are applied to all the pixels equally. Therefore, in this paper, we proposed an algorithm using region segmentation of the mask in order to adaptive edge detection according to image, and the results processed by proposed algorithm indicated superior edge detection characteristics in edge area.