• Journal of Korea Multimedia Society
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• v.8 no.1
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• pp.34-45
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• 2005

In this paper, a novel implementation of a person verification system based on depth-weighted Hausdorff distance (DWHD) using the surface curvature of the face is proposed. The definition of Hausdorff distance is a measure of the correspondence of two point sets. The approach works by finding the nose tip that has a protrusion shape on the face. In feature recognition of 3D face image, one has to take into consideration the orientated frontal posture to normalize after extracting face area from original image. The binary images are extracted by using the threshold values for the curvature value of surface for the person which has differential depth and surface characteristic information. The proposed DWHD measure for comparing two pixel sets were used, because it is simple and robust. In the experimental results, the minimum curvature which has low pixel distribution achieves recognition rate of 98% among the proposed methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.4
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• pp.826-832
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• 2016

In this paper, we propose a tongue diagnosis system for determining the presence of specific taste crack area as a first step in the digital tongue diagnosis system that anyone can use easily without special equipment and expensive digital tongue diagnosis equipment. Training DB was developed by the Haar-like feature, Adaboost learning on the basis of 261 pictures which was collected in Oriental medicine. Tongue candidate regions were detected from the input image by the learning results and calculated the average value of the HUE component to separate only the tongue area in the detected candidate regions. A tongue area is separated through the Connected Component Labeling from the contour of tongue detected. The palate regions were divided by the relative width and height of the tongue regions separated. Image on the taste area is converted to gray image and binarized with each of the average brightness values. A crack in the presence or absence was determined via Connected Component Labeling with binary images.

• Journal of the Korea Society of Computer and Information
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• v.10 no.5 s.37
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• pp.333-342
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• 2005

This paper proposes a method, which recognizes student's identification card by using image processing and recognition technology and can manage student information on the web. The presented scheme sets up an average brightness as a threshold, based on the brightest Pixel and the least bright one for the source image of the ID card. It is converting to binary image, applies a horizontal histogram, and extracts student number through its location. And, it removes the noise of the student number region by the mode smoothing with 3$\times$3 mask. After removing noise from the student number region, each number is extracted using vertical histogram and normalized. Using the enhanced ART1 algorithm recognized the extracted student number region. In this study, we propose the enhanced ART1 algorithm different from the conventional ART1 algorithm by the dynamical establishment of the vigilance parameter. which shows a tolerance limit of unbalance between voluntary and stored patterns for clustering. The Experiment results showed that the recognition rate of the proposed ART1 algorithm was improved much more than that of the conventional ART1 algorithm. So, we develop an educational matters administration system by using the proposed recognition method of the student's identification card.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.34-43
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• 2010

Texture information plays an important role in object recognition and classification. To perform an accurate classification, the texture feature used in the classification must be highly discriminative. This paper presents a novel texture descriptor for texture-based image retrieval and its application in Computer-Aided Diagnosis (CAD) system for Emphysema classification. The texture descriptor is based on the combination of local surrounding neighborhood difference and centralized neighborhood difference and is named as Combined Neighborhood Difference (CND). The local differences of surrounding neighborhood difference and centralized neighborhood difference between pixels are compared and converted into binary codewords. Then binomial factor is assigned to the codewords in order to convert them into high discriminative unique values. The distribution of these unique values is computed and used as the texture feature vectors. The texture classification accuracies using Outex and Brodatz dataset show that CND achieves an average of 92.5%, whereas LBP, LND and Gabor filter achieve 89.3%, 90.7% and 83.6%, respectively. The implementations of CND in the computer-aided diagnosis of Emphysema is also presented in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.444-450
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• 1999

This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.35D no.10
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• pp.107-115
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• 1998

A multiplier bit-pair recoding technique derived from Booth algorithm is used as an effective method that can carry out a fast binary multiplication regardless of a sign of both multiplicand and multiplier. In this paper, we propose an implementation of an optical high-speed multiplier which consists of a symbolic substitution adder and an optical multiplication algorithm, which transforms and enhances the multiplier bit-pair recoding algorithm to be fit for optical characteristics. Specially, a symbolic substitution addition rules are coded with a dual-rail logic, and so the complement of the logic of the symbolic substitution adder is easily obtained with a shift operation because it is always present. We also construct the symbolic substitution system which makes superposition image by superimposing two shifted images in a serial connection and recognizes a reference image by feeding this superimposed image to a mask. Thus, the optical multiplier, which is compared with a typical system, is implemented to the smaller system by reducing the number of optical passive elements and the size of this system.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.33-43
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• 2007

In this proper, we proposes a robust watermarking against the lossy compression in the Hadamard domain. The Hadamard matrix consists of only 1 or -1 and can be computed veru fast. The Hadamrd transform has the inverse transform therefore it is able to be applied into the watermarking technology. In embedding process, we select 10 coefficients from intermediate frequency domain and create two watermark patterns. In extraction process, we use the watermark patterns and compare them to detect the watermark information. When we use the standard image ($512{\times}512$) and binary watermark image ($64{\times}64$), the results of these examines are PSNR for $38{\sim}42dB$ and BER for $3.9{\sim}12.5%$. The JPEG QF between 30 and100, naked human eyes can detect to watermark image easily. The experimental results show that performance of Hadamard domain is better than those of DCT, FFT, and DWT.

• Journal of Korea Multimedia Society
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• v.23 no.4
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• pp.539-548
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• 2020

Deep learning technology is currently being used and applied in many different fields. Convolution neural network (CNN) is a method of artificial neural networks in deep learning, which is commonly used for analyzing different types of images through classification. In the conventional classification of histopathology images of prostate carcinomas, the rating of cancer is classified by human subjective observation. However, this approach has produced to some misdiagnosing of cancer grading. To solve this problem, CNN based classification method is proposed in this paper, to train the histological images and classify the prostate cancer grading into two classes of the benign and malignant. The CNN architecture used in this paper is based on the VGG models, which is specialized for image classification. However, color normalization was performed based on the contrast enhancement technique, and the normalized images were used for CNN training, to compare the classification results of both original and normalized images. In all cases, accuracy was over 90%, accuracy of the original was 96%, accuracy of other cases was higher, and loss was the lowest with 9%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.5
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• pp.390-399
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• 1988

A conventional BTC has the merit of real time processing and simple computation, but has the problem that its compression rate is low. In this paper, a modified BTC using the Quad Tree which is frequently used in binary image is proposed. The method results in the low compression rate by decreasing the total number of subblocks by mean of making the size of a subblock large in the small variation area of graty level and the size af a subblock small in the large variation area of gary level. For the effective transmission of bit plane, the Huffman run-lengh code for the large size of a subblock and the lookup table for tha small size of a subblock are used. The proposed BTC method show the result of coding 256 level image at the average data rate of about 0.8 bit/pixel.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.1-8
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• 2011

Automatic vehicle license plate recognition technology is widely used in gate control and parking control of vehicles, and police enforcement of illegal vehicles. However inherent geometric information of the license plate can be transformed in the vehicle images due to the slant and the sunlight or lighting environment. In this paper, a distortion invariant vehicle license plate extraction and recognition algorithm is proposed. First, a binary image reserving clean character strokes can be achieved by using a DoG filter. A plate area can be extracted by using the location of consecutive digit numbers that reserves distortion invariant characteristic. License plate is recognized by using neural networks after geometric distortion correction and image enhancement. The simulation results of the proposed algorithm show that the accuracy is 98.4% and the average speed is 0.05 seconds in the recognition of 6,200 vehicle images that are obtained by using commercial LPR system.