• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.40-41
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• 2023

This paper introduces the design of detection, recognition, and tracking algorithms for VTS radar image-based objects. The detection of objects in radar images utilizes artificial intelligence technology to determine the presence or absence of objects, and can classify the type of object using AI technology. Tracking involves the continuous tracking of detected objects over time, including technology to prevent confusion in the movement path. In particular, for land-based radar, there are unnecessary areas for detection depending on the terrain, so the function of detecting and recognizing vessels within the region of interest (ROI) set in the radar image is included. In addition, the extracted coordinate information is designed to enable various applications and interpretations by calculating speed, direction, etc.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.233-238
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• 2009

In this paper, we propose the methods to select the stable region for the detect region which is required in the system used the face to the ROI in the auto-focus digital camera. this method regards the face region as the ROI in the progressive input frame and focusing the region in the mobile camera embeded ISP module automatically. The learning algorithm to detect the face is the Adaboost algorithm. we proposed the method to detect the slanted face not participate in the train process and postprocessing method for the results of detection, and then we proposed the stabilization method to sustain the region not shake for the region. we estimated the capability for the stabilization algorithm using the RMS between the trajectory and regression curve.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.737-747
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• 2012

Recently, in order to support automatic meter reading for conventional metering devices, an image processing-based approach of recognizing the number meter data in the captured meter images has attracted many researchers' interests. Numerical character segmentation is a very critical process for successful recognition. In this paper, we propose an efficient numeric character segmentation method which can segment numeric characters well for any metering device types under diverse illumination environments. The proposed method consists of two consecutive stages; detection of number area containing all numbers as a tight ROI(Region of Interest) and segmentation of numerical characters in the ROI. Detection of tight ROI is achieved in two steps: extraction of rough ROI by utilizing horizontal line segments after illumination enhancement preprocessing, and making the rough ROI more tight through clipping utilizing vertical and horizontal projection about binarized ROI. Numerical character segmentation in the detected ROI is stably achieved in two processes of 'vertical segmentation of each number region' and 'number segmentation in the each vertical segmented number region'. Through the experiments about a homegrown meter image database containing various meter type images of low contrast, low intensity, shadow, and saturation, it is shown that the proposed numeric character segmentation method performs effectively well for any metering device types under diverse illumination environments.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.2
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• pp.457-463
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• 2012

Changing the environment around detecting areas may lower the performance of a video-based accident detection system. Region of interest(ROI) and background information changing constantly on account of the car headlights at night and a sudden changes in the weather are the biggest factors to increase the ratio of wrong results. Thus, we proposed and implemented the integrated accident detection system combined the video-based system and the laser-based imaging system. In this paper, we were able to overcome the majority problem of video-based system and it was a meaningful results that it can improve the reliability for the system.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.915-924
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• 2002

Conventional Systems for parcel sorting consist of two sequences as loading the parcel into conveyor belt system and post-code input. Using bar code information, the parcels to be recorded and managed are recognized. This paper describes a 32 $\times$ 32 sized mini-block inspection to extract bar code Region of Interest (ROI) from the line Charged Coupled Device (CCD) camera capturing image of moving parcel at 2m/sec speed. Firstly, the Min-Max distribution of the mini-block has been applied to discard the background of parcel and region of conveying belts from the image. Secondly, the diagonal inspection has been used for the extraction of letters and bar code region. Five horizontal line scanning detects the number of edges and sizes and ROI has been acquired from the detection. The wrong detected area has been deleted by the comparison of group size from labeling processes. To correct excluded bar code region in mini-block processes and for analysis of bar code information, the extracted ROI 8 boundary points and decline distribution have been used with central axis line adjustment. The ROI extraction and central axis creation have become enable within 60～80msec, and the accuracy has been accomplished over 99.44 percentage.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.636-639
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• 2003

In this paper, we present a robust method for detecting other vehicles from n forward-looking CCD camera in a moving vehicle. This system uses edge and shape information to detect other vehicles. The algorithm consists of three steps: lane detection, ehicle candidate generation, and vehicle verification. First after detecting a lane from the template matching method, we divide the road into three parts: left lane, front lane, and right lane. Second, we set the region of interest (ROI) using the lane position information and extract a vehicle candidate from the ROI. Third, we use local orientation coding (LOC) edge image of the vehicle candidate as input to a pretrained neural network for vehicle recognition. Experimental results from highway scenes show the robustness and effectiveness of this method.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.191-196
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• 2005

The character strings on chips and components are so tiny and numerous that it is a very difficult work for people to perform. In paper, this we propose a mark automatic checking system, which will determine whether chip is wrong-mark or not by recognizing characters on chips. Lots of faulty detection conditions and template matching methods are used to inspect the faulty mark items. The faulty detection classifies conditions as five kinds-darkness, matching, area, broken and branch. A series of experimentation shan that the method proposed here am offer an effective way to determine wrong-mark on chips.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.577-583
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• 2007

The character strings on chips and components are so tiny and numerous that it is a very difficult work for people to perform. In this paper, we propose a mark automatic checking system, which will determine whether chip is wrong-mark or not by recognizing characters on chips. Lots of faulty detection conditions and template matching methods are used to inspect the faulty mark items. The faulty detection classifies conditions as five kinds-darkness, matching, area, broken and branch. A series of experimentation show that the method proposed here can offer an effective way to determine wrong-mark on chips.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.69-71
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• 2011

본 논문에서는 비전 시스템을 기반으로 한 조명변화에 강인한 실시간 차선 검출 알고리즘을 제안한다. 카메라의 기하학적 특성을 이용하여 ROI 를 설정하고, ROI 내에서 검색범위를 설정하여 한 방향에 대한 차선을 검출하고 도로의 지형적 특성을 고려하여 반대편 차선을 추정한다. 히스토그램에서 상위 5%의 밝기 값을 가지는 픽셀에 대해서 이진화를 진행함으로써 일반적인 조명변화에 대해서 강인한 모습을 보이며, ROI 및 검색 범위를 설정함으로써 기존 알고리즘에 비해 빠른 검출이 가능해진 알고리즘이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.519-521
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• 2015

세라믹 소재 영상에서 결함 영역이 다른 영역보다 명암도가 밝게 나타나는 정보를 이용하여 ROI 영역을 추출한다. 추출된 ROI 영역에서 Blurring 기법을 적용하여 미세 잡음을 제거한다. 미세 잡음이 제거된 ROI 영역에서 Median Filter기법을 적용하여 임펄스 잡음을 제거한다. 임펄스 잡음이 제거된 영역에서 Prewit Mask을 적용하여 수평과 수직 에지를 검출하고 검출된 에지에 윤곽선 추적 기법을 적용하여 결함 영역의 경계를 보정한다. 보정된 영상에서 Blob Labeling 기법을 적용하여 최종적으로 결함 영역을 추출한다. 제안된 방법을 8mm와 10mm 세라믹 소재 영상을 대상으로 실험한 결과, 기존의 결함 검출 방법보다 제안된 검출 방법의 검출 성능이 개선된 것을 확인하였다.