• Journal of Korea Multimedia Society
• /
• v.18 no.10
• /
• pp.1197-1204
• /
• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• Spatial Information Research
• /
• v.13 no.4 s.35
• /
• pp.393-400
• /
• 2005

Due to the development of the network and Internet, it is easy to copy and spread digital data. These data has the advantage of being able to be copy without loss. However, this has generated a problem over copyright. The problem occurred in GIS, too. Although GIS data acquisition is the major cost there is insufficient effort made to protect copyright. For this reason watermarking could be a good method to guarantee owner's copyright. This paper will explain watermarking, and show an overview of watermarking studies connecting image and vector data.

• Current Optics and Photonics
• /
• v.4 no.3
• /
• pp.210-220
• /
• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• Annual Conference of KIPS
• /
• 2000.10b
• /
• pp.887-890
• /
• 2000

본 연구에서는 내용기반 영상 데이터 검색을 위하여 변환 영역에서 위치 정보와 주파수 정보를 가지는 웨이블릿 성질을 이용하여 영상을 압축한 후에 저주파 성분에 의한 객체들의 특징을 추출하는 방안으로 Vector Quantization 을 이용한 class 별 영상 검색을 제시한다 내용기반 영상 검색의 주요특징들은 색상, 질감, 그리고 영상의 공간적인 특징을 고려한 특징 값 둥이 사용된다. 먼저 검색의 효율성을 높이기 위해 영상을 구성하는 특징 치 중에서 가장 빈도가 많은 class 부터 영상의 유사도를 검색한 후에 다음으로 영상을 구성하는 빈도가 큰 순서대로 DB 내에 저장되어 있는 영상과 비교를 하게 된다. DB내 영상 검색은 빈도수가 우선인 5개의 class를 기준으로 유사도를 측정해서 검색을 이룬다. 이러한 영상의 특징들을 어떻게 결합하고 특징 추출을 하느냐에 따라 검색의 효율성에 영향을 준다. 따라서 본 연구에서는 영상의 위치 정보와 주파수 정보를 가지는 웨이블릿 변환 후 얻어지는 저대역 부밴드에서의 공간적인 특성을 고려한 특징 값을 이용하여 Vector Quantization 알고리즘에 의해 정지영상의 객체 대표 특징들을 마르게 검색하고자 한다. 본 연구에서는 Haar Wavelet과 Vector Quantization 에서 색상과 질감의 가중치를 적용한 후 DB 에 저장된 영상과 유사도를 검색하는 방법을 취하고자 한다.

• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.517-517
• /
• 2002

Recently in the field of telecommunication, there is much interest in geo-surface characteristics of urban areas. Geophysical properties of urban features are now incorporated with accurate positional information to model the telecommunication environment. In this study, three-dimensional buildings are geometrically reconstructed from existing vector maps and aerial images. Accurate digital vector maps are easily available in Korea. However existing maps, which had been produced for GIS applications, do not have height information which is critical to three dimensional building reconstruction. Image matching techniques were applied to aerial image stereopairs to automatically extract the height information of buildings. Planimetric coordinates from vector maps were used as initial guides in the process. Future studies will be undertaken to link geophysical properties to the three-dimensional spatial objects reconstructed from this study thus bringing the telecommunication environment model closer to reality.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.12
• /
• pp.6190-6213
• /
• 2019

For small-scale vector data, restrictions on watermark scheme capacity and robustness limit the use of copyright protection. A watermarking scheme based on robust geometric features and capacity maximization strategy that simultaneously improves capacity and robustness is presented in this paper. The distance ratio and angle of adjacent vertices are chosen as the watermark domain due to their resistance to vertex and geometric attacks. Regarding watermark embedding and extraction, a capacity-improved strategy based on quantization index modulation, which divides more intervals to carry sufficient watermark bits, is proposed. By considering the error tolerance of the vector map and the numerical accuracy, the optimization of the capacity-improved strategy is studied to maximize the embedded watermark bits for each vertex. The experimental results demonstrated that the map distortion caused by watermarks is small and much lower than the map tolerance. Additionally, the proposed scheme can embed a copyright image of 1024 bits into vector data of 150 vertices, which reaches capacity at approximately 14 bits/vertex, and shows prominent robustness against vertex and geometric attacks for small-scale vector data.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.24 no.2
• /
• pp.153-160
• /
• 2000

A Particle Image Velocimetry(PIV) algorithm is developed to analyze whole flow field both qualitatively and quantitatively. The practical use of PIV requires the use of fast, reliable, computer-based methods for tracking numerous particles suspended in a flow field. The TSS, NTSS, FFT-Hybrid, which are developed in the area of image compression and coding, are introduced to develop fast vector search algorithm. The numerical solution of the lid-driven cavity flow by the ADI algorithm with the Wachspress Formula is introduced to produce synthetic data for the validation of the tracking algorithms. The algorithms are applied to image data of real flow experiments. The comparisons in CPU time and mean error show, with a small loss of accuracy, CPU time for tracking is reduced considerably.

• Proceedings of the IEEK Conference
• /
• 2000.09a
• /
• pp.375-378
• /
• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.19 no.5
• /
• pp.699-712
• /
• 1995

The purpose of this study was 1) to identify the constructing factors of jeans brand image 2) to analyze the domestic jeans market using perceptual maps of three benefit segments based on stdy(I). The questionnaire consisted of brand preference, attribute of brand image and wearer image was selected from the previous studies or developed for this study. The subjects were 350 male and female university students who have purchased at least one of the nine jeans wear brand selected for the study. For statistical analysis, reliability test, factor analysis, MANOVA, and multiple regression were used. The results of this study were as follows: 1. Symbolism, quality, and economy were found out as constricting factors of brand image in the attribute dimensions, while innovative and active image were found out in the wearer image dimensions. 2. 9 Perceptual maps of attribute dimensions and 3 perceptual maps of wearer image dimensions were constructed and each ideal vector was drawn.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.934-938
• /
• 2000

This paper presents a laser cutting of 2D image. 2D image in pixel graphic format is converted into vector graphic image by image processing. Bitmap graphics are made easily, but can not being used in application works for geometry transition. The Sobel's Edge detection method is used to find boundary points on 2D image. The points are fitted into curves with sampling and filtering. Sampling can provide efficient computation and filtering reconstuct features in image. The NC code is generated on MURBS curve of the points. Also, the offset of contour and cutting conditions are considered.