• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.335-338
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• 2002

This paper proposes some technical approaches for automatic detection of pulmonary nodules in chest X-ray images. We applied threshold technique for the lung field segmentation and extended the lung field by using morphological methods. A template matching technique was employed for automatic detecting nodules in lung area. Genetic algorithm(GA) was used in template matching(TM) to select a matched image from various reference patterns(simulated typical nodules). We eliminated the false-positive candidates by using histograms and contrasts. We used standard databases published by Japanese Society of Radiological Technology (JSRT) for correct results. Also we employ two-dimensional Gaussian distribution for some reference images because the shadow of lung nodules in radiogram generally shows the distributions. Nodules of about 89% were correctly detected by our scheme. The simulation results show that it is an effective method to indicate lesions on chest radiograms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.308-314
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• 2006

A line scan camera is applied to enhance COG(Chip On Glass) inspection accuracy to be measured a few micro unit. The foreign substance detection among various faulty factors has been the most difficult technology in the faulty automatic inspection step since COG pattern is very miniature and complexity. In this paper, we proposed two step area segmentation template matching method to increase matching speed. Futhermore to detect foreign substance(such as dust, scratch) with a few micro unit, the new method using gradient mask and AND operation was proposed. The proposed 2 step template matching method increased 0.3 - 0.4 second matching speed compared with conventional correlation coefficient. Also, the proposed foreign substance applied masks enhanced $5-8\%$ faulty detection rate compared with conventional no mask application method.

• The Journal of the Korea Contents Association
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• v.18 no.4
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• pp.398-405
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• 2018

In the HCI, hand gesture recognition is attracting attention as a method for interaction and information exchange between users and devices along with the development of IT devices. In hand gesture recognition through image processing, palm region detection is a key process contributing to improvement of processing speed and recognition rate. In this paper, we propose a new method for image segmentation between the hand and wrist for palm area detection. The anatomical characteristics of the hand are used to calculate the distance between the iliac bones of the thumb and little finger, which have the widest width, by the horizontal projection histogram of the hand image, and then the palm area is detected by drawing a circle having the width as the diameter. In order to verify the superiority of this method, multiple stage template matching is used to compare and evaluate recognition performance against the four conventional methods for 10 hand gestures. Note that the literatures to offer palm area detection performance evaluation are few although there are many studies on hand gesture recognition.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.203-208
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• 2005

The CGO (Chip on Glass) to be measured a few micro unit is captured by line scan camera for the accuracy of chip inspection. But it is very sensitive to scan speed and lighting conditions. In this paper, we propose the methods to increase the accuracy of faulty detection by image subtraction. Image subtraction is detected faultiness by subtracting the image of a ' perfect ' COG from trot of the sample under tests. For image subtraction to be successful, the two images must be pre챠sely registered The two images is registered by the area segmentation pattern matching, and the result image get by operating the gradient mask image and the image to practice subtraction. A series of experimentation showed that the proposed algorithm shows substantial improvement over the other image subtraction methods.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.7-12
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• 2011

In this paper, we propose the technique detecting interest object region effectively in the images from periscope of submarine based on self-generated template. First, we extract the sea-sky line, and divide it into sky and sea area from background region based on the sea-sky line. In each divided background region, the blocks which can be represented in each background region are set as a reference template. After dividing an image into several same size of blocks, we apply multi template matching to the divided search blocks and histogram template to divide the image into object region and background region. Proposed algorithm is adapted to various images in which objects exist in the background of sea and sky. We verified that proposed algorithm performed properly without given informmed prby prior learning.ropso, regardless of the slope of sea-sky line and the locmed p of object based on sea-sky line, we verified that the objects region was segmented effectively from the input image.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.19-25
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• 2016

This paper is to recognize and play tab chords from guitar musical sheets. The musical chord area of an input image is segmented by changing the image in saturation and applying the Grabcut algorithm. Based on a template matching, our approach detects tab starting sections on a segmented musical area. The virtual block method is introduced to search blanks over chord lines and extract tab fret segments, which doesn't cause the computation loss to remove tab lines. In the experimental tests, the prototype based classification outperforms Bayesian method and the nearest neighbor rule with the whole set of training data and its performance is similar to that of the support vector machine. The experimental result shows that the prediction rate is about 99.0% and the number of selected prototypes is below 3.0%.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.