• 한국통신학회논문지
• /
• 제24권8B호
• /
• pp.1480-1489
• /
• 1999

본 논문에서는 블록 기반 DCT 부호화 방식의 단점인 블록화 현상을 제거하기 위하여 MSDS 방법을 개선한 후 처리 기법을 제안한다. MSDS방법의 문제점인 예측된 DCT 계수값의 범위를 제한하기 위하여 입력 영상의 블록 경계 화소차 분포를 규정할 수 있는 OSLD(Overlapped Sub-Laplacian Distribution)를 정의한다. 블록화 현상은 블록간의 기울기를 이용하여 불연속 정도를 측정함으로써 정량화 되고, 정량화 된 값을 최소화하도록 양자화 오류값을 예측한다. OSLD를 이용하여 각 블록들을 네 가지 형태로 분류하고 이를 에지 부류와 평탄 부류로 구분한다. 에지 부류로 판별된 블록에서는 예측된 양자화 오류의 범위가 해당되는 양자화 간격보다 크면 이 간격으로 예측된 양자화 오류를 보정한다. 본 방법을 사용하여 실험 영상에서 블록화 현상을 제거할 때 기존의 MSDS 방법에서 요구하였던 입력 영상에 따라 실험적으로 문턱값을 설정하였던 문제점을 해결하고, PSNR 값을 영상에 따라 0.1∼0.3 dB 정도 향상시키면서 시각적으로 화질을 향상시킬 수 있다.

• 대한원격탐사학회지
• /
• 제19권2호
• /
• pp.159-169
• /
• 2003

본 논문에서는 고해상도의 항공 영상으로부터 건물의 3차원 정보를 자동으로 생성하는 방법을 제안하였다. 먼저 에지 보존 필터를 사용하여 영상에 포함된 잡음을 제거한 후에 watershed 기법을 이용하여 에지의 위치를 보존하고 영상 분할을 수행한다. 분할된 영역의 경계선에 위치한 화소의 곡률을 계산하여 control point를 검출하고 control point 사이의 선소를 추출한다. 추출된 선소들의 방향과 길이를 고려하여 선소의 연결을 수행하고 최종적으로 화소의 그레디언트 크기를 이용하여 선소의 위치를 조정한다. 공면의 그룹화와 다각형 조각을 형성하는 과정은 각 영역에 대해 공선 기하학과 비행 정보를 이용하여 정합된 3차원 선소들을 선택하여 이루어진다. 항공 영상에 제안한 방법을 적용하여 건물 지붕을 정확하게 검출할 수 있음을 보였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2003년도 ICCAS
• /
• pp.1315-1318
• /
• 2003

In this paper, a neural network based approach using a self-organizing feature map is proposed for the segmentation of X ray images. A number of algorithms based on such approaches as histogram analysis, region growing, edge detection and pixel classification have been proposed for segmentation of general images. However, few approaches have been applied to X ray image segmentation because of blur of the X ray image and vagueness of its edge, which are inherent properties of X ray images. To this end, we develop a new model based on the neural network to detect objects in a given X ray image. The new model utilizes Mumford-Shah functional incorporating with a modified adaptive SOFM. Although Mumford-Shah model is an active contour model not based on the gradient of the image for finding edges in image, it has some limitation to accurately represent object images. To avoid this criticism, we utilize an adaptive self organizing feature map developed earlier by the authors.[1] It's learning rule is derived from Mumford-Shah energy function and the boundary of blurred and vague X ray image. The evolution of the neural network is shown to well segment and represent. To demonstrate the performance of the proposed method, segmentation of an industrial part is solved and the experimental results are discussed in detail.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
• /
• pp.275-279
• /
• 2002

Change Detection is a useful technology that can be applied to various fields, taking temporal change information with the comparison and analysis among multi-temporal satellite images. Especially, Change Detection that utilizes high-resolution satellite imagery can be implemented to extract useful change information for many purposes, such as the environmental inspection, the circumstantial analysis of disaster damage, the inspection of illegal building, and the military use, which cannot be achieved by low- or middle-resolution satellite imagery. However, because of the special characteristics that result from high-resolution satellite imagery, it cannot use a pixel-based method that is used for low-resolution satellite imagery. Therefore, it must be used a feature-based algorithm based on the geographical and morphological feature. This paper presents the system that builds the change map by digitizing the boundary of the changed object. In this system, we can make the change map using manual or semi-automatic digitizing through the user interface implemented with a floating window that enables to detect the sign of the change, such as the construction or dismantlement, more efficiently.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
• /
• pp.575-578
• /
• 2005

Topographical images, in case of aerial or satellite images, are usually similar in colors and textures, and complex in shapes. Thus we have to use shape features of images for efficiently retrieving a query image from topographical image databases. In this paper, we propose a shape feature extraction method which is suitable for topographical images. This method, which improves the existing projection in the Cartesian coordinates, performs the projection operation in the polar coordinates. This method extracts three attributes, namely the number of region pixels, the boundary pixel length of the region from the centroid, the number of alternations between region and background, along each angular direction of the polar coordinates. It extracts the features of complex shape objects which may have holes and disconnected regions. An advantage of our method is that it is invariant to rotation/scale/translation of images. Finally we show the advantages of our method through experiments by comparing it with CSS which is one of the most successful methods in the area of shape feature extraction

• 한국농공학회논문집
• /
• 제47권7호
• /
• pp.3-9
• /
• 2005

A computer system for crop information was developed using Visual Basic and ArcGIS VBA. The system is operated on ArcGIS 8.3 with Microsoft Access MDB. Landsat +ETM, KOMPSAT-1 EOC, ASTER VNIR and IKONOS panchromatic (Pan) and multi-spectral (MIS) images were included in the system to extract agricultural land use items identifiable at various spatial resolutions of images. Agriculture related data inventories using crop cover information such as texture and average pixel value of each band based on crop cultivation calendar were designed and implemented. Three IKONOS images were loaded in the system to show crop cover characteristics such as rice, pear, grape, red pepper, garlic, and surface water cover of reservoir with field surveys. GIS layers such as DEM (Digital Elevation Model), stream, road, soil, land use and administration boundary were prepared to understand the related characteristics and identify the location easily.

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2007년도 춘계학술대회 논문집
• /
• pp.113-119
• /
• 2007

This article deals mainly with two objectives viz, 1) the potentiality of very high-resolution(VHR) multi-spectral and pan chromatic QuickBird satellite data in resources mapping over moderate resolution satellite data (IRS LISS III) and 2) the advantages of using object oriented classification method of eCognition software in land use and land cover analysis over the ISODATA classification method. These VHR data offers widely acceptable metric characteristics for cartographic updating and increase our ability to map land use in geometric detail and improve accuracy of local scale investigations. This study has been carried out in the Sukkalampatti mini-watershed, which is situated in the Eastern Ghats of Tamil Nadu, India. The eCognition object oriented classification method succeeded in most cases to achieve a high percentage of right land cover class assignment and it showed better results than the ISODATA pixel based one, as far as the discrimination of land cover classes and boundary depiction is concerned.

• 환경영향평가
• /
• 제8권3호
• /
• pp.77-90
• /
• 1999

The degree of green naturality (DGN) has played a key role for maintaining the environmental quality from inappropriate developments, although the quality and effectiveness of the mapping of DGN has been under debate. In this study, spatial distribution of degree of green naturality was initially estimated from forest stand maps that were produced from the aerial photo interpretation and extensive field survey. Once the boundary of initial classes of DGN were defined, it were overlaid with normalized difference vegetation index (NDVI) data that were derived from the recently obtained Landsat Thematic Mapper data. NDVI was calculated for each pixel from the radiometrically corrected satellite image. There were no significant differences in mean values of vegetation index among the initial DGN classes. However, the satellite derived vegetation index was very effective to delineate the developed and damaged forest lands and to adjust the initial value of DGN according to the distribution of NDVI within each class.

• 한국정보처리학회논문지
• /
• 제7권5호
• /
• pp.1587-1598
• /
• 2000

For the object-based video compression at very low bit rate, vieo segmentation is an essential part. In this paper, we propose a temporal video segmentation algorithms for motion pictures which is based on blocks. The algorithm is composed of three steps: (1) the change-detection, (2) the block merging, and (3) the block segmentation. The first step separates the change-detected region from background. Here, a new method for removing the uncovered region without motion estimation is presented. The second step, which is further divided into three substeps, estimates motions for the change-detected region and merges blocks with similar motions. The merging conditions for each substep as criteria are also given. The final step, the block segmentation, segments the boundary block that is excluded from the second step on a pixel basis. After describing our algorithm in detail, several experimental results along the processing order are shown step by step. The results demonstrate that the proposed algorithm removes the uncovered region effectively and produced objects that are segmented well.

• 디지털산업정보학회논문지
• /
• 제11권4호
• /
• pp.89-97
• /
• 2015

In this paper, a method of color image segmentation based on DBSCAN(Density Based Spatial Clustering of Applications with Noise) using compactness of superpixels and texture information is presented. The DBSCAN algorithm can generate clusters in large data sets by looking at the local density of data samples, using only two input parameters which called minimum number of data and distance of neighborhood data. Superpixel algorithms group pixels into perceptually meaningful atomic regions, which can be used to replace the rigid structure of the pixel grid. Each superpixel is consist of pixels with similar features such as luminance, color, textures etc. Superpixels are more efficient than pixels in case of large scale image processing. In this paper, superpixels are generated by SLIC(simple linear iterative clustering) as known popular. Superpixel characteristics are described by compactness, uniformity, boundary precision and recall. The compactness is important features to depict superpixel characteristics. Each superpixel is represented by Lab color spaces, compactness and texture information. DBSCAN clustering method applied to these feature spaces to segment a color image. To evaluate the performance of the proposed method, computer simulation is carried out to several outdoor images. The experimental results show that the proposed algorithm can provide good segmentation results on various images.