• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1121-1130
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• 2004

This paper proposes a method for line and word segmentation for machine-printed text blocks. To separate a text region into the unit of lines, it analyses the horizontal projection profile and performs a recursive projection profile cut method. In the word segmentation, between-word gaps are identified by a hierarchical clustering method after finding gaps in the text line by using a connected component analysis. In addition, a special symbol detection technique is applied to find two types of special symbols tying between words using their morphologic features. An experiment with 84 text regions from English and Korean documents shows that the proposed method achieves 99.92% accuracy of word segmentation, while a commercial OCR software named Armi 6.0 Pro$^{TM}$ has 97.58% accuracy.y.

• The Journal of the Acoustical Society of Korea
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• v.24 no.3
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• pp.160-165
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• 2005

We have developed SH (shear horizontal) surface acoustic wave (SAW) sensors for detection of the immobilization and hybridization of DNA (deoxyribonucleic acid) on the gold coated delay line of transverse SAW devices. The experiments of DNA immobilization and hybridization were performed with 15-mer oligonucleotides (probe and complementary target DNA). The sensor consists of twin SAW delay line oscillators operating at 100 MHz fabricated on $36^{\circ}$ rotated Y-cut $LiTaO_3$ piezoelectric single crystals. The relative change in the frequency of the two oscillators was monitored to detect the hybridization between target DNA and immobilized probe DNA in pH 7.4 PBS (phosphate buffered saline) solution. The measurement results showed a good response of the sensor to the mass loading effects of the DNA immobilization and hybridization with the sensitivity up to $1.55{\cal}ng/{\cal}ml/Hz$.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.44-51
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• 2009

Text understand in natural images has become an active research field in the past few decades. In this paper, we present an automatic recognition system in Korean signboards with a complex background. The proposed algorithm includes detection, binarization and extraction of text for the recognition of shop names. First, we utilize an elaborate detection algorithm to detect possible text region based on edge histogram of vertical and horizontal direction. And detected text region is segmented by clustering method. Second, the text is divided into individual characters based on connected components whose center of mass lie below the center line, which are recognized by using a minimum distance classifier. A shape-based statistical feature is adopted, which is adequate for Korean character recognition. The system has been implemented in a mobile phone and is demonstrated to show acceptable performance.

• The Journal of the Acoustical Society of Korea
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• v.20 no.6
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• pp.75-81
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• 2001

In this paper we analyzed the localization error of near-field detection algorithm in the sea. The near-field detection algorithms using triangulation and wavefront curvature basically assume a signal in two dimension of bearing and range. But the assumption causes localization error because there is three dimension of bearing, range, and depth in the sea. Even through three dimensional effect is considered, the localization error is occurred if multipath propagation in the sea is ignored. To analyze the localization error in the sea, we simulate the near-field localization using acoustic propagation model and focused beamforming considering wavefront curvature. The simulation results indicate that localization error always occurs in the sea and the error varied with sound velocity profile, water depth, bottom slope, source range, etc.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.4
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• pp.237-243
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• 2007

In this paper, we propose the Real-time Dead-Pixel Detection and Compensation System for mobile camera and its hardware architecture. The CMOS image sensors as image input devices are becoming popular due to the demand for miniaturized, low-power and cost-effective imaging systems. However a conventional Dead-Pixel Detection Algorithm is disable to detect neighboring dead pixels and it degrades image quality by wrong detection and compensation. To detect dead pixels the proposed system is classifying dead pixels into Hot pixel and Cold pixel. Also, the proposed algorithm is processing line-detector and $5{\times}5$ window-detector consecutively. The line-detector and window-detector can search dead pixels by using one-dimensional(only horizontal) method in low frequency area and two-dimensional(vertical and diagonal) method in high frequency area, respectively. The experimental result shows that it can detect 99% of dead pixels. It was designed in Verilog hardware description language and total gate count is 23K using TSMC 0.25um ASIC library.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.8
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• pp.395-400
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• 2020

Coronavirus disease 2019 (COVID-19) was identified in December 2019 in China and has spread globally, resulting in an ongoing pandemic. Because COVID-19 is spread mainly from person to person, every person is required to wear a facemask in public. On the other hand, many people are still not wearing facemasks despite official advice. This paper proposes a method to predict whether a human subject is wearing a facemask or not. In the proposed method, two eye regions are detected, and the mask region (i.e., face regions below two eyes) is predicted and extracted based on the two eye locations. For more accurate extraction of the mask region, the facial region was aligned by rotating it such that the line connecting the two eye centers was horizontal. The mask region extracted from the aligned face was fed into a convolutional neural network (CNN), producing the classification result (with or without a mask). The experimental result on 186 test images showed that the proposed method achieves a very high accuracy of 98.4%.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.509-516
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• 2002

This paper proposes an efficient algorithm which recognizes the mixed document image consisting of the images, texts, tables, and straight lines. This system is composed of three steps. The first step is the detection of rotation angle for complementing skewed images, the second is detection of erasing an unnecessary background region and last is the classification of each component included in document images. This algorithm performs preprocessing of detecting rotation angles and correcting documents based on the detected rotation angles in order to minimize the error rate by skewness of the documentation. We detected the rotation angie using only horizontal and vertical components in document images and minimized calculation time by erasing unnecessary background region in the detecting process of component of document. In the next step, we classify various components such as image, text, table and line area included in document images. we applied this method to various document images in order to evaluate the performance of document recognition system and show the successful experimental results.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.71-83
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• 1996

This paper describes facial component detection and skew correction algorithm for face recognition. We use a priori knowledge and models about isolated regions to detect eye location from the face image captured in natural office environments. The relations between human face components are represented by several rules. We adopt an edge detection algorithm using sobel mask and 8-connected labelling algorith using array pointers. A labeled image has many isolated components. initially, the eye size rules are used. Eye size rules are not affected much by irregular input image conditions. Eye size rules size, and limited in the ratio between gorizontal and vertical sizes. By the eye size rule, 2 ~ 16 candidate eye components can be detected. Next, candidate eye parirs are verified by the information of location and shape, and one eye pair location is decided using face models about eye and eyebrow. Once we extract eye regions, we connect the center points of the two eyes and calculate the angle between them. Then we rotate the face to compensate for the angle so that the two eyes on a horizontal line. We tested 120 input images form 40 people, and achieved 91.7% success rate using eye size rules and face model. The main reasons of the 8.3% failure are due to components adjacent to eyes such as eyebrows. To detect facial components from the failed images, we are developing a mouth region processing module.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.632-641
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• 2004

In this paper, a new intermediate view reconstruction method employing a stereo image rectification algorithm by which an uncalibrated input stereo image can be transformed into the calibrated one is suggested and its performance is analyzed. In the proposed method, feature point are extracted from the stereo image pair though detection of the corners and similarities between each pixel of the stereo image. And then, using these detected feature points, the moving vectors between stereo image and the epipolar line is extracted. Finally, the input stereo image is rectified by matching the extracted epipolar line between the stereo image in the horizontal direction and intermediate views are reconstructed by using these rectified stereo images. From some experiments on synthesis of the intermediate views by using three kinds of stereo image; a CCETT's stereo image of 'Man' and two stereo images of 'Face' ＆ 'Car' captured by real camera, it is analyzed that PSNRs of the intermediate views reconstructed from the calibrated image by using the proposed rectification algorithm are improved by 2.5㏈ for 'Man', 4.26㏈ for 'Pace' and 3.85㏈ for 'Car' than !hose of the uncalibrated ones. This good experimental result suggests a possibility of practical application of the unposed stereo image rectification algorithm-based intermediate view reconstruction view to the uncalibrated stereo images.

• 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.