• Journal of the Korea Society of Computer and Information
• /
• v.19 no.2
• /
• pp.31-37
• /
• 2014

In conventional methods for car navigational path detection using Hough transform, navigational path deviation of a car is decided in car navigational images with simple background. But in case of car navigational images having complex background with obstacles on the road, shadows, other cars, and so on, it is very difficult to detect navigational path because these obstacles obstruct correct detection of car navigational path. In this paper, I proposed an effective navigational path detection method having better performance than conventional navigational path detection methods using Hough transform only, and fuzzy binarization method and Canny mask are applied in the proposed method for the better performance. In order to evaluate the performance of the proposed method, I experimented with 20 car navigational images and verified the proposed method is more effective for detection of navigational path.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.485-488
• /
• 2005

The comparison of 3 conventional binarization methods for insect footprints and the result of performance evaluation using a proposed performance criterion are introduced in this paper. The 3 different binarization algorithms for comparison are based on different category each, and the proposed performance criterion is based on the characteristics of insect footprints which have very smaller foreground area than background area. In the experiments, average performance results using 71 test images are compared and analyzed. The higher-order entropy binarization algorithm proposed by Abutaleb showed the best result for pattern recognition applications of insect footprints.

• Proceedings of the KAIS Fall Conference
• /
• 2003.11a
• /
• pp.88-95
• /
• 2003

The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. We proposed and evaluated the novel recognition algorithm of container identifiers that overcomes effectively the hardness and recognizes identifiers from container images captured in the various environments. The proposed algorithm, first, extracts the area including only all identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper and by applying contour tracking method to the binarized area, container identifiers which are targets of recognition are extracted. We proposed and applied the ART2-based RBF network for recognition of container identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm has more improved performance in the extraction and recognition of container identifiers than the previous algorithms.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2019.01a
• /
• pp.23-26
• /
• 2019

본 논문에서는 X-Ray 영상에서 용접한 부분의 기공이나 균열 등의 결함 영역을 추출하는 새로운 방법을 제안한다. 제안된 방법은 세라믹 X-Ray 영상에서 비등방성 확산 필터를 적용하여 영상의 잡음을 제거하고, 수직 및 수평 히스토그램을 각각 적용하여 용접 영역을 추출한 후, 최소 자승법을 적용하여 배경 밝기를 제거하고, 사다리꼴 형태의 Fuzzy Stretching기법을 적용하여 명암 값을 강조하여 결함 영역과 그 외의 영역간의 명암 대비를 강조한다. 그리고 Fuzzy C_Means 알고리즘을 적용하여 결함 영역을 세분화한 후, Fuzzy C_Means을 적용하여 생성된 클러스터들의 중심 명암 값을 이용하여 ${\alpha}_-cut$을 설정한 후에 임계구간을 구하고 영상을 이진화하여 최종적으로 결함 영역을 추출한다. 제안된 방법의 결함 추출 성능을 확인하기 위하여 세라믹 X-Ray 영상을 대상으로 실험한 결과, 기존의 방법보다 결함 영역이 정확히 추출되는 것을 확인할 수 있었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2021.05a
• /
• pp.623-625
• /
• 2021

Rubber o-rings are produced by conventional injection molding methods. In this case, products that are not normally molded are determined to be defective. However, if images acquired during image-based reading are read as original, there is a problem of poor accuracy. We have thus learned from convolutional neural networks using adaptive binarization and Convex Hull algorithms by extracting only rubber oring parts from the original images through pre-processing. During the test process, it was confirmed that the defect detection performance of the learning method applied pre-processing was better than the standard suggested.

• Journal of Korea Multimedia Society
• /
• v.19 no.10
• /
• pp.1759-1766
• /
• 2016

We can implement the virtual touch recognition system by mounting the virtual touch algorithm into an embedded device connected to a depth camera. Since the computing performance is limited in embedded system, the real-time processing of recognizing the virtual touch is difficult when the resolution of the depth image is large. In order to resolve the problem, this paper improves the algorithms of binarization and labeling that occupy a lot of time in all processing of virtual touch recognition. It processes the binarization and labeling in only necessary regions rather than all of the picture. By appling the proposed algorithm, the system can recognize the virtual touch in real-time as about 31ms per a frame in the depth image that has 640×480 resolution.

• International Journal of Knowledge Content Development & Technology
• /
• v.5 no.2
• /
• pp.11-24
• /
• 2015

It is common for archive libraries to provide public access to historical and ancient document image collections. It is common for such document images to require specialized processing in order to remove background noise and become more legible. Document images may be contaminated with noise during transmission, scanning or conversion to digital form. We can categorize noises by identifying their features and can search for similar patterns in a document image to choose appropriate methods for their removal. In this paper, we propose a hybrid binarization approach for improving the quality of old documents using a combination of global and local thresholding. This article also reviews noises that might appear in scanned document images and discusses some noise removal methods.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.11 no.3
• /
• pp.211-216
• /
• 2011

The detection of defective parts in a factory is usually performed by the human eye. Therefore, heavy manpower is in demand for minor enterprises. An image processing system is desired to solve this drawback. However, due to the variety of the products characteristics, an general algorithm is needed that can adapt to these characteristics. Therefore, in this paper, the key pad parts' characteristics which need to be dealt with are analyzed in order to embody the image processing algorithm that is suggested. The experimental results show the probability of detecting a defective part is 95% with a detection time of 0.203 seconds, on the average.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.52 no.1
• /
• pp.35-41
• /
• 2003

In this paper, a machine vision system for recognizing and classifying the patterns and marks engraved by die molding or laser marking on the glass panels of computer monitors is suggested and evaluated experimentally. The vision system is equipped with a neural network and an NGC pattern classifier including searching process based on normalized grayscale correlation and adaptive binarization. This system is found to be applicable even to the cases in which the segmentation of the pattern area from the background using ordinary blob coloring technique is quite difficult. The inspection process is accomplished by the use of the NGC hypothesis and ANN verification. The proposed pattern recognition system is composed of three parts: NGC matching process and the preprocessing unit for acquiring the best quality of binary image data, a neural network-based recognition algorithm, and the learning algorithm for the neural network. Another contribution of this paper is the method of generating the training patterns from only a few typical product samples in place of real images of all types of good products.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.79-81
• /
• 2018

혈관내 초음파(Intravascular Ultrasound, IVUS)는 혈관 내벽의 단면을 보여주는 검사 방법으로 관상 동맥 내의 내강, 죽상 경화반, 그리고 혈관벽의 변화에 관한 직접적이고 구체적인 정보를 제공한다. 본 논문에서는 IVUS 영상에서 내막과 외막을 추출하고 각 막의 지름을 자동적으로 추출하는 방법을 제안한다. 제안된 방법은 IVUS 영상에 Histogram Equalization 기법을 적용하여 명암 대비를 강조한 후에 퍼지 이진화 기법과 평균 이진화 기법을 각각 적용하여 내막과 외막을 추출하기 위해 이진화한다. 이진화된 내막과 외막의 각 영역 중에서 혈관내 초음파 영상 중심에서 가장 큰 영역의 정보를 이용하여 라벨링 기법을 적용하여 내막과 외막 영역을 추출하고 각 막의 지름을 계산한다. 제안된 방법을 IVUS 영상을 대상으로 실험한 결과, 내막과 외막의 지름이 비교적 정확히 추출되는 것을 실험을 통하여 확인하였다.