• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1007-1010
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• 2002

In this paper, we present a new blocking artifact reduction algorithm using interpolation and signal adaptive filter (SAF) based on the edge map. Generally, block-based coding, such as JPEG and MPEG, is the most popular image compression method. However, for high compression it produces noticeable blocking and ringing artifacts in the decoded image. In proposed method, all the block is classified into low and high frequency blocks in block classification procedure. And edge map is obtained by using Sobel operator on decoded image. And according to the block property we applied blocking artifacts reduction algorithm. Namely, four neighbor low frequency block is participated in interpolation based on edge map. And ringing artifacts is removed by applying a signal adaptive filter around the edge using edge map in high frequency block. The computer simulation results confirmed a better performance by the proposed method in both the subjective and objective image qualities.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.142-147
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• 2009

The recent H.264/AVC video coding standard provides a higher coding efficiency than previous standards. H.264/AVC achieves a bit rate saving of more than 50 % with many new technologies, but it is computationally complex. Most of fast mode decision algorithms have focused on Baseline profile of H.264/AVC. In this paper, a fast block mode decision scheme for P- slices in High profile is proposed to reduce the computational complexity for H.264/AVC because the High profile is useful for broadcasting and storage applications. To reduce the block mode decision complexity in P- pictures of High profile, we use the SAD value after $16{\times}16$ block motion estimation. This SAD value is used for the classification feature to divide all block modes into some proper candidate block modes. The proposed algorithm shows average speed-up factors of 47.42 ${\sim}$ 67.04% for IPPP sequences.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• Journal of the Korean Society of Systems Engineering
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• v.17 no.2
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• pp.91-97
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• 2021

User interface for cancer classification system is a software application with clinician's friendly tools and functions to diagnose cancer from pathology images. Pathology evolved from manual diagnosis to computer-aided diagnosis with the help of Artificial Intelligence tools and algorithms. In this paper, we explained each block of the project life cycle for the implementation of automated breast cancer classification software using AI and machine learning algorithms to classify normal and invasive breast histology images. The system was designed to help the pathologists in an automatic and efficient diagnosis of breast cancer. To design the classification model, Hematoxylin and Eosin (H&E) stained breast histology images were obtained from the ICIAR Breast Cancer challenge. These images are stain normalized to minimize the error that can occur during model training due to pathological stains. The normalized dataset was fed into the ResNet-34 for the classification of normal and invasive breast cancer images. ResNet-34 gave 94% accuracy, 93% F Score, 95% of model Recall, and 91% precision.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.677-680
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• 1998

In this paper, we proposed a lwo bit rate subband coding with adaptive vector quantization using the correlation between motion vector and block energy in subband. In this method, the difference between the input signal and the motion compensated interframe prediction signal is decomposed into several narrow bands using quadrature mirror filter (QMF) structure. The subband signals are then quantized by adaptive vector quantizers. In the codebook generating process, each classified region closer to the block value in the same region after the classification of region by the magnitude of motion vector and the variance values of subband block. Because codebook is genrated considering energy distribution of each region classified by motion vector and variance of subband block, this technique gives a very good visual quality at low bit rate coding.

• Korean Journal of Child Studies
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• v.36 no.2
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• pp.75-94
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• 2015

The main purpose of this study was to examine the various aspects of logico-mathematical thinking and its development by observing a block building activity undertaken by infants and toddlers. The subjects comprised 73 young children from between the ages of 12- to 41-months-old. The interviewee was individually asked to build "something tall", making use of 20 blocks. The results of this study were, first, a regular increase by age is seen in congruence, the vertical use of flat blocks, and innovative ways of using triangular blocks. Second, many types of logico-mathematical thinking processes, such as classification, seriation, spatial relationship and temporal relationship, were shown during the block building activities on the part of the 12- to 41-months-olds who took part in this study.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.1
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• pp.74-80
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• 2010

The purpose of reverse engineering design using 3D measurement data is an accurate reconstruction of real body. In oder to accomplish this object, it is important that creating exact extracting edges should be studying out first of all. This study used edge-based method to find out edge point from the measuring point data. The characteristics are analysed using the mean curvature block method on the fitting NURBS curve and defined edges through block removal condition. The results showed that only using the NURBS curve of maximum curvature analysis to define correct edge of real geometry is limited, but this segmentation approach provides simplified necessary condition for edge classification, and an effectiveness to classify a straight line, curves and fillets etc.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.517-518
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• 2023

최근 뇌-컴퓨터 인터페이스 분야에서는 뇌파 신호를 이용한 운동 심상 분류 연구가 활발히 이루어지고 있다. 뇌파는 개인별 차이가 큰 생체 신호로, 사용자에 독립적인 경우 추론이 어려워지는 문제가 있어 운동 심상 분류에서는 주로 피험자 종속적인 연구가 행해져 왔다. 본 논문에서는 컨볼루션 신경망 기반의 뇌파 분류 모델인 EEGNet 에 새로운 방식으로 개선한 Squeeze-and-Excitation block 을 적용해 피험자에 대해 독립적인 운동 심상 분류 성능을 향상시키는 방법을 제안하며, 제안한 Squeeze-and-Excitation block 을 적용한 모델이 기존 모델보다 높은 분류 성능을 보여주는 것을 실험적으로 확인하였다.

• 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.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.292-294
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• 2003

본 논문에서는 독립성분분석(Independent Component Analysis； 이하 ICA)에서의 제안된 그레이 블록 거리 알고리즘(Grey Block Algorithm, 이하 GBD)을 이용한 영상 분류 방법을 제안한다. 이 제안된 방법은 기존의 GBD 알고리듬을 이용한 경우보다 k가 감소할 때 그 편차는 적어 좋은 영상 분류 특징을 보임을 모의 실험을 통하여 확인할 수 있었다.