• Title/Summary/Keyword: 이미지맵

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Extracting the color map and color chip for a patent and application (컬러 맵과 컬러 칩 추출의 특허 출원과 적용 사례)

  • Lee, Keum Hee
    • The Research Journal of the Costume Culture
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    • v.20 no.6
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    • pp.869-882
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    • 2012
  • The purpose of this study is to obtain the patent for extracting the color map and color chip from the color image source and to develop color image map for fashion design. For this study, fashion image maps were produced from 210 pictures with Adobe Photoshop CS2 program targeting 200 university students from 2004 to 2006. The procedures for extracting the color map and color chip included providing the color image, the filtering phase, the segmentation phase, the extraction phrase, and the arrangement phase. Based on the results of this study, patent application was made to KIPO(Korean Intellectual Property Office) for this invention. The following effects can be expected from the standpoint of design based on the case study. First, it is a straight forward procedure to extract a color chip and color map from a color image. Second, it can be applied to various art works based on the recombination of colors as representative colors can be extracted from the related color image that combines a variety of colors. Third, desired colors can be selected based on the taste cluster classification or sensibility axis of design by extracting the representative color from the color image.

GB-Index: An Indexing Method for High Dimensional Complex Similarity Queries with Relevance Feedback (GB-색인: 고차원 데이타의 복합 유사 질의 및 적합성 피드백을 위한 색인 기법)

  • Cha Guang-Ho
    • Journal of KIISE:Databases
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    • v.32 no.4
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    • pp.362-371
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    • 2005
  • Similarity indexing and searching are well known to be difficult in high-dimensional applications such as multimedia databases. Especially, they become more difficult when multiple features have to be indexed together. In this paper, we propose a novel indexing method called the GB-index that is designed to efficiently handle complex similarity queries as well as relevance feedback in high-dimensional image databases. In order to provide the flexibility in controlling multiple features and query objects, the GB-index treats each dimension independently The efficiency of the GB-index is realized by specialized bitmap indexing that represents all objects in a database as a set of bitmaps. Main contributions of the GB-index are three-fold: (1) It provides a novel way to index high-dimensional data; (2) It efficiently handles complex similarity queries; and (3) Disjunctive queries driven by relevance feedback are efficiently treated. Empirical results demonstrate that the GB-index achieves great speedups over the sequential scan and the VA-file.

A study of improve vectorising technique on the internet (인터넷에서의 개선된 벡터라이징 기법에 관한 연구)

  • 김용호;이윤배
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.6 no.2
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    • pp.271-281
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    • 2002
  • Currently, most web designers guarante high quality using bitmap graphics as fixed font size, but that has defects about file size and flexibility. Especially, to provide high quality of banner and advertise characters, after you should use a bitmap edit program, and then we should follow the method we add that program to HTML documents as bitmap data. In this study, as I show a couple of new tags in front of HTML documents, I show methods which can be presented diverse effects. When text information are stored, because we print out a screen with simple control points and outside information, it can be possible for us to express the same quality of Hangul characters like printed documents in a web browser. Regardless of the second class of platform, we can make it possible the character expression with exact character expressions and diverse effects.

Technology Convergence Map Creation and Country Profile Analysis in the Field of Artificial Intelligence (인공지능 분야의 기술융합맵 생성 및 국가 프로파일 분석)

  • Kim, Hyun-Woo;Noh, Kyung-Ran;Ahn, Sejung;Kwon, Oh-Jin
    • The Journal of the Korea institute of electronic communication sciences
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    • v.12 no.1
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    • pp.139-146
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    • 2017
  • The interest about Artificial Intelligence through the AlphaGo Match in Korea has been increasing rapidly. So far, very little has been done in Artificial Intelligence. The aim of this paper is to reveal technology convergence and to assess the country profile in the field of artificial intelligence(AI). Technology convergence map was created after extracting USPTO patent grants and Web of Science data and generating matrics in the field of AI. Several Indicators were obtained by extracting and calculating SCOPUS Data that KISTI has. According to USPTO patent grants, it shows that AI technology has a strong relationship with several sectors such as cost/price determination, image analysis, and surgery, etc. Also, AI has a active convergence with some fields of Electrical and Electronic Engineering, BioTechnologies, and Medicine etc. According to country profile analysis, Korea reaches a global average growth index. However, in terms of specialization index (SI) and average of relative citations (ARC), there is a large gap between Korea and research leading countries.

Expanded Object Localization Learning Data Generation Using CAM and Selective Search and Its Retraining to Improve WSOL Performance (CAM과 Selective Search를 이용한 확장된 객체 지역화 학습데이터 생성 및 이의 재학습을 통한 WSOL 성능 개선)

  • Go, Sooyeon;Choi, Yeongwoo
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.9
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    • pp.349-358
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    • 2021
  • Recently, a method of finding the attention area or localization area for an object of an image using CAM (Class Activation Map)[1] has been variously carried out as a study of WSOL (Weakly Supervised Object Localization). The attention area extraction from the object heat map using CAM has a disadvantage in that it cannot find the entire area of the object by focusing mainly on the part where the features are most concentrated in the object. To improve this, using CAM and Selective Search[6] together, we first expand the attention area in the heat map, and a Gaussian smoothing is applied to the extended area to generate retraining data. Finally we train the data to expand the attention area of the objects. The proposed method requires retraining only once, and the search time to find an localization area is greatly reduced since the selective search is not needed in this stage. Through the experiment, the attention area was expanded from the existing CAM heat maps, and in the calculation of IOU (Intersection of Union) with the ground truth for the bounding box of the expanded attention area, about 58% was improved compared to the existing CAM.

A Study on Attention Mechanism in DeepLabv3+ for Deep Learning-based Semantic Segmentation (딥러닝 기반의 Semantic Segmentation을 위한 DeepLabv3+에서 강조 기법에 관한 연구)

  • Shin, SeokYong;Lee, SangHun;Han, HyunHo
    • Journal of the Korea Convergence Society
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    • v.12 no.10
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    • pp.55-61
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    • 2021
  • In this paper, we proposed a DeepLabv3+ based encoder-decoder model utilizing an attention mechanism for precise semantic segmentation. The DeepLabv3+ is a semantic segmentation method based on deep learning and is mainly used in applications such as autonomous vehicles, and infrared image analysis. In the conventional DeepLabv3+, there is little use of the encoder's intermediate feature map in the decoder part, resulting in loss in restoration process. Such restoration loss causes a problem of reducing segmentation accuracy. Therefore, the proposed method firstly minimized the restoration loss by additionally using one intermediate feature map. Furthermore, we fused hierarchically from small feature map in order to effectively utilize this. Finally, we applied an attention mechanism to the decoder to maximize the decoder's ability to converge intermediate feature maps. We evaluated the proposed method on the Cityscapes dataset, which is commonly used for street scene image segmentation research. Experiment results showed that our proposed method improved segmentation results compared to the conventional DeepLabv3+. The proposed method can be used in applications that require high accuracy.

Perception and Appraisal of Urban Park Users Using Text Mining of Google Maps Review - Cases of Seoul Forest, Boramae Park, Olympic Park - (구글맵리뷰 텍스트마이닝을 활용한 공원 이용자의 인식 및 평가 - 서울숲, 보라매공원, 올림픽공원을 대상으로 -)

  • Lee, Ju-Kyung;Son, Yong-Hoon
    • Journal of the Korean Institute of Landscape Architecture
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    • v.49 no.4
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    • pp.15-29
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    • 2021
  • The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.

Multidimensional scaling analysis on the images of special purpose academies (다차원 척도법을 이용한 특수 목적대학에 대한 이미지 분석)

  • Bae, Hyun-Wung;Kwon, Ki-Ho;Moon, Mi-Nam;Moon, Ho-Seok
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.1
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    • pp.11-20
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    • 2010
  • The purpose of this study is to analyze the images of the Military Academy, Naval Academy, Air Force Academy, Police Academy, and Armed Forces Nursing Academy using multidimensional scaling method. For this research, we surveyed 363 applicants to special purpose academies including Military Academies and Police College. The study showed that the Military, Naval, and Air Force Academies had stronger image than the Police Academy in the area of physical strength, tradition, and fellowship between senior and junior. On the other hand, the Police Academy had better image in the area of social activity and applicant's academic achievement. The Military Academy had been evaluated the best school among the three Academies in the area of applicant's academic achievement, educational environment, faculty, tradition, and fellowship between senior and junior.

Weather Classification and Fog Detection using Hierarchical Image Tree Model and k-mean Segmentation in Single Outdoor Image (싱글 야외 영상에서 계층적 이미지 트리 모델과 k-평균 세분화를 이용한 날씨 분류와 안개 검출)

  • Park, Ki-Hong
    • Journal of Digital Contents Society
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    • v.18 no.8
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    • pp.1635-1640
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    • 2017
  • In this paper, a hierarchical image tree model for weather classification is defined in a single outdoor image, and a weather classification algorithm using image intensity and k-mean segmentation image is proposed. In the first level of the hierarchical image tree model, the indoor and outdoor images are distinguished. Whether the outdoor image is daytime, night, or sunrise/sunset image is judged using the intensity and the k-means segmentation image at the second level. In the last level, if it is classified as daytime image at the second level, it is finally estimated whether it is sunny or foggy image based on edge map and fog rate. Some experiments are conducted so as to verify the weather classification, and as a result, the proposed method shows that weather features are effectively detected in a given image.

An Improved PCF Technique for The Generation of Shadows (그림자생성을 위한 개선된 PCF 기법)

  • Yu, Young-Jung;Choi, Jin-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.11 no.8
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    • pp.1442-1449
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    • 2007
  • Shadows are important elements for realistic rendering of the 3D scene. We cannot recognize the distance of objects in the 3D scene without shadows. Two methods, image-based medthods and object-based methods, are largely used for the rendering of shadows. Object based methods can generate accurate shadow boundaries. However, it cannot be used to generate the realtime shadows because the time complexity defends on the complexity of the 3D scene. Image based methods which are techniques to generate shadows are widely used because of fast calculation time. However, this algorithm has aliasing problems. PCF is a method to solve the aliasing problem. Using PCF technique, antialiased shadow boundaries can be generated. However, PCF with large filter size requires more time to calculate antialiased shadow boundaries. This paper proposes an improved PCF technique which generates antialiased shadow boundaries similar to that of PCF. Compared with PCF, this technique can generate antialiased shadows in less time.