• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.1707-1710
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• 2003

In this paper, we propose a method of decision on blurring for business card images using block classification. In the proposed method, an input image is partitioned into 8${\times}$8 blocks and each block is classified into character block or background block using a block energy calculated in DCT domain. Whether the input image is blurring or non-blurring is determined using a ratio of low frequency energy and high frequency energy in DCT domain. Experimental results show that the proposed block classification classifies block well and the proposed decision on blurring decides well for various business card images.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.66-69
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• 2000

In this paper, we proposed a postprocessing algorithm for quantization effects reduction in block coded images using the block classification and adaptive filtering. The proposed method consists of classification, adaptive inter-block filtering, and intra-block filtering. First, each block is classified into one of seven classes based on the characteristics of 8${\times}$8 DCT coefficients. Then each block boundary is filtered by adaptive inter-block filters according to the block classification. Finally for blocks which are classified into edge block, intra-block filtering is peformed. Experimental results show that the proposed method gives better results than the conventional methods from both a subjective and an objective viewpoint.

• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• 한국멀티미디어학회논문지
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• 제8권10호
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• pp.1347-1359
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• 2005

본 논문에서는 공간 명암도 의존 행렬을 이용하여 문서영상의 다양한 블록들을 상세하게 분류해 낼 수 있는 방법을 제안하였다. 제안한 블록분류 방법에서는 먼저 명암도 문서영상을 이진화하여 평활화 기법을 적용함으로써 명암도 영상의 질감특징을 이용하여 분할하는 것보다 신속하게 블록을 분할하고 동시에 그 위치정보도 구할 수 있도록 하였다. 분할된 각 블록들의 공간 명암도 의존 행렬로부터 문서블록들의 7가지 질감특징을 구하고, 이를 정규화한 다음 역전파 신경회로망를 이용하여 문서블록들을 분류하였다. 문서블록들을 큰 문자, 중간 문자, 작은 문자, 표, 그래픽 및 사진 등 여섯 가지 유형으로 상세 분류하였다. 또한 명암도 문서영상의 2차 통계 질감특징을 얻기 위해 공간 명암도 의존 행렬을 구할 때, 기존의 사진과 같은 일반 영상분할에서와는 달리, 문서블록 고유의 특징이 잘 반영되도록 하였다. 즉, 분할된 각 블록을 하나의 마스크로 정하여 수평 한 방향의 공간 명암도 의존 행렬을 구함으로써 고속의 질감특징추출과 상세 블록분류가 가능하도록 하였다.

• 대한건축학회연합논문집
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• 제20권6호
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• pp.89-96
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• 2018

The purpose of this study is to categorize Commercial area by identifying characteristics of blocks and coding them in order to segment use zoning in Commercial area. The study was conducted as follows. Data from building register, cadastral map, statistics annual report are utilized to identify the physical environment of the block. four types used as code under the physical environment classification code which are classification code of physical environment, detail usage, volume ratio, and height type are set, and combine the classification codes sorted by the four types of code. Through the physical environment classification codes, there are currently 37 different block characteristics of the Old downtown Commercial area. Diversity is not reflected. There are only Central commercial area of regulations in Old downtown commercial areas that are uniformly managed. For the renewal, management and development that can occur in the near future, it is necessary to segment of use district in the commercial area. Consider the current situation and future development direction for the management of sustainable commercial areas. Management is required using physical environment classification codes. It is meaningful that it can be maintained, managed and developed in accordance with the characteristics of each block.

• ETRI Journal
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• 제32권3호
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• pp.380-389
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• 2010

This paper presents a new edge-protection algorithm and its very large scale integration (VLSI) architecture for block artifact reduction. Unlike previous approaches using block classification, our algorithm utilizes pixel classification to categorize each pixel into one of two classes, namely smooth region and edge region, which are described by the edge-protection maps. Based on these maps, a two-step adaptive filter which includes offset filtering and edge-preserving filtering is used to remove block artifacts. A pipelined VLSI architecture of the proposed deblocking algorithm for HD video processing is also presented in this paper. A memory-reduced architecture for a block buffer is used to optimize memory usage. The architecture of the proposed deblocking filter is verified on FPGA Cyclone II and implemented using the ANAM 0.25 ${\mu}m$ CMOS cell library. Our experimental results show that our proposed algorithm effectively reduces block artifacts while preserving the details. The PSNR performance of our algorithm using pixel classification is better than that of previous algorithms using block classification.

• 제어로봇시스템학회논문지
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• 제18권11호
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• pp.1005-1010
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• 2012

We propose a novel approach to classify fingerprints using the extracted adaptive core block for improving classification performance of incomplete fingerprints in this paper. We compute representative directions from fingerprint images by the block unit and learn horizontal and vertical Markov models by deciding the center position of a fingerprint image based on the expert knowledge. The center block of a test image is the block has the highest probability after comparing the Markov model with $11{\times}11$ blocks. The proposed approach can effectively classify incomplete fingerprints using the optimal center block.

• 대한전자공학회논문지SP
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• 제38권4호
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• pp.442-452
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• 2001

본 논문에서는 블록 분류와 적응적 필터링을 이용하여 블록 기반 부호화에서의 양자화 잡음을 제거하는 후처리 방법을 제안하였다. 제안한 방법에서는 블록 분류, 적응적인 블록 간 필터링, 및 블록 내 필터링의 단계로 이루어진다. 먼저, 각 블록을 8x8 DCT 계수 분포에 따라 7개의 클래스로 분류하고, 인접한 두 클래스 정보에 따라 적응적인 블록 간 필터링을 수행한다. 그리고 에지 블록으로 분류된 블록에 대하여 에지맵을 이용한 블록 내 필터링을 수행한다. 실험결과로부터 제안한 방법이 기존의 방법에 비하여 객관적 화질 측면에서는 유사하지만, 주관적 화질 측면에서 보다 우수함을 확인하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2108-2111
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• 2003

This paper introduces a new local feature extraction method and image matching method for the localization and classification of targets. Proposed method is based on the block-by-block projection associated with directional pattern of blocks. Each pattern has its own eigen-vertors called as CEBs(Classified Eigen-Blocks). Also proposed block-based image matching method is robust to translation and occlusion. Performance of proposed feature extraction and matching method is verified by the face localization and FLIR-vehicle-image classification test.

• 대한전자공학회논문지SP
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• 제39권4호
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• pp.399-407
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• 2002

본 논문에서는 블록 기반으로 부호화된 영상에 대하여 블록 분류 (block classification)와 다층 퍼셉트론 (multi-layer perceptron, MLP) 모델을 이용한 적응적 블록화 현상 제거 알고리듬을 제안하였다. 제안한 방법에서는 각 블록을 DCT 계수의 분포 특성에 따라 네 개의 클래스로 분류한 다음, 인접한 두 블록의 클래스 정보에 따라 수평 및 수직 블록 경계 영역에 대하여 적응적으로 신경망 필터를 적용한다. 즉, 평탄한 영역, 수평 방향 에지 영역, 수직 방향 에지 영역, 및 복잡한 영역에 대하여 각각 서로 다른 신경망 필터를 수평 및 수직 방향으로 적용하여 블록화 현상을 제거한다. 모의 실험 결과를 통하여 제안한 방법이 객관적 화질 및 주관적 화질 측면에서 기존의 방법보다 그 성능이 우수함을 확인하였다.