• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.210-216
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• 2014

The edge in the image is a part which the brightness changes rapidly between the object and the object or objects and background, and includes information of the features such as size, position, orientation, and texture of the object. The edge detection is the technique that acquires these information of the images, and now the researches to detect edges are making steady progress. Typical conventional edge detection methods are Sobel, Prewitt, Roberts using the first derivative operator and Laplacian method using the second derivative operator and so on. These methods is more or less insufficient that the characteristics of the edge detection in the image added salt and pepper noise. therefore, in this paper, an edge detection algorithm using modified mask that applies different size mask according to noise density of local mask is proposed.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.33-40
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• 2017

In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.1
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• pp.1-9
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• 2003

The CNN-UM algorithm which performs the analog parallel subtraction of images has been developed and its application study to the moving target detection has been done. The CNN-UM is the state of the art computation architecture with high computational potential of analog parallel processing. It is one of the strong candidates for the next generation of computing system which fulfills requirement of the real-time image processing. One weakness of the CNN-UM is that its analog parallel processing function is not fully utilized for the inter frame processing. If two subsequent image frames are superimposed with opposite signs on identical capacitors for short time period, the analog subtraction between them is achieved. The Principle of such temporal inter-frame processing algorithm has been described and its mathematical analysis has been done. Practical usefulness of the proposed algorithm has also been verified through the application for moving target detection.

• Journal of Biomedical Engineering Research
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• v.22 no.6
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• pp.503-510
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• 2001

In this paper. we describe a 32-channel bioimpedance measurement system It consists of 32 independent constant current sources of 50 kHz sinusoid. The amplitude of each current source can be adjusted using a 12-bit MDAC. After we applied a pattern of injection currents through 32 current injection electrodes. we measured induced boundary voltages using a variable-gain narrow-band instrumentation amplifier. a Phase-sensitive demodulator. and a 12-bit ADC. The system is interfaced to a PC for the control and data acquisition. We used the system to detect anomalies with different resistivity values in a saline Phantom with 290mm diameter The accuracy of the developed system was estimated as 2.42% and we found that anomalies larger than 8mm in diameter can be detected. We Plan to improve the accuracy by using a digital oscillator improved current sources by feedback control, Phase-sensitive A/D conversion. etc. to detect anomalies smaller than 1mm in diameter.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.736-738
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• 2004

최근 사용자의 제스처를 이용한 게임 시스템에 대한 연구가 많은 관심을 받고 있다. 사용자의 얼굴 및 손의 움직임을 이용하여 게임을 제어하기 위해서는 복잡한 배경 및 조명에 강건한 얼굴 및 손 영역의 추출이 필수적이다. 본 논문에서는 조명 변화에 강건한 피부색 검출을 이용한 게임 인터페이스를 제안한다. 이를 위해 제안된 시스템은 다음의 두 단계로부터 얼굴 및 손 영역을 추출한다. 먼저, 피부색과 유사한 물건들을 제거하기 위해 배경 영상과 현재 영상의 차영상으로부터 전경물체를 추출한다. 그 다음, 조명에 의한 깜박임이나 잡음을 줄이기 위해서 SCT 알고리즘을 이용하여 전경물체 영역 안에서 피부색 영역만을 정확하게 검출한다. 추출된 얼굴 및 손의 움직임으로부터 얻어지는 제스처는 은닉마르코프 모델을 사용하여 인식된다. 복잡한 환경에서 실험한 결과, 제안된 시스템은 정확한 피부색 영역 검출을 제공하고 이를 통한 보다 정확한 인식률을 제공할 수 있다는 것이 증명되었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.507-509
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• 2023

최근 딥러닝은 도로 CCTV 동영상의 교통사고 검출에 널리 사용되지만 일인칭 동영상의 교통사고 검출은 분명히 어렵다. 일인칭 동영상은 역동적이고 시야가 제한되어 있기 때문이다. 본 논문에서는 일인칭 동영상을 분석하여 교통사고를 검출하는 방법을 제시한다. 이 방법은 교통 표현 특성을 분석하는 것 외에도 의미를 이해하고 교통 장면을 인코딩한다. 프레임의 표현 특징은 각 프레임 상의 물체의 특징과 물체의 위치 관계의 공간적 숨겨진 특진을 학습함으로써 얻어진다. 그 후에 프레임 표현 특징과 교통 장면의 특징이 연결되어 GRU 실행기에 공급된다. 여러 GRU 실행기는 분석한 후 사고가 발생했는지 확인된다. 이 방법은 높은 역학과 제한된 시야 문제를 효과적으로 해결한다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.249-256
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• 2015

Shadow is a common physical phenomenon in natural images and may cause problems in computer vision tasks. Therefore, shadow removal is an essential preprocessing process for effective moving object tracking in video image. In this paper, we proposed the method of shadow removal algorithm using chromaticity, brightness distortion and direction of shadow candidate. The proposed method consists of two steps. First, removal process of primary shadow candidate region by using chromaticity, brightness and distortion. The second stage applies the final shadow candidate region to obtain a direction feature of shadow which is estimated by the thinning algorithm after calculating the lowest pixel position of the moving object. To verify the proposed approach, some experiments are conducted to draw a compare between conventional method and that of proposed. Experimental results showed that proposed methodology is simple, but robust and well adaptive to be need to remove a shadow removal operation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.929-931
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• 2015

As the digital image processing technology develops, the edge in the image is widely utilized in various fields such as the object recognition and detection. Most of the current methods to detect the edge use the fixed weighting mask of Sobel or Roberts. Such current methods have an advantage that the implementation is simple but have a disadvantage that the characteristics of the edge detection are more or less insufficient. Thus, an algorithm using the modified morphology is proposed in order to supplement such problems of the current edge detection methods and obtain the excellent edge detection, and also a simulation using this algorithm is conducted to compare with such current methods.

• 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.

• Journal of Broadcast Engineering
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• v.1 no.2
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• pp.165-175
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• 1996

In this paper, the image segmentation method based on the hierarchical pyramid image structure of reduced resolution versions of the image for solving the problems in the conventional methods is presented. This method is described the object detection and delineation by statistical approach. In the object detection method, IFSVR( Inverse-father-son variance ratio) method and FSVR(father-son variance ratio ) method are proposed for solving clustering validity problem occurred In the hierarchical pyramid image structure. An optimal object pixel Is detected at some level by this method. In the object delineation method, the iterative algorithm by top-down traversing method is proposed for moving the optimal object pixel to levels of higher resolution. Using the computer simulation, the results by the proposed statistical methods and object traversing method are investigated for the binary Image and the real image. At the results of computer simulation, the proposed methods of image segmentation based on the hierarchical pyramid Image structure seem to have useful properties and deserve consideration as a possible alternative to existing methods of image segmentation. The computation for the proposed method is required 0(log n) for n${\times}$n input image.