• Title/Summary/Keyword: Directional Sign System

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Multi-resolution Lossless Image Compression for Progressive Transmission and Multiple Decoding Using an Enhanced Edge Adaptive Hierarchical Interpolation

  • Biadgie, Yenewondim;Kim, Min-sung;Sohn, Kyung-Ah
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.11 no.12
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    • pp.6017-6037
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    • 2017
  • In a multi-resolution image encoding system, the image is encoded into a single file as a layer of bit streams, and then it is transmitted layer by layer progressively to reduce the transmission time across a low bandwidth connection. This encoding scheme is also suitable for multiple decoders, each with different capabilities ranging from a handheld device to a PC. In our previous work, we proposed an edge adaptive hierarchical interpolation algorithm for multi-resolution image coding system. In this paper, we enhanced its compression efficiency by adding three major components. First, its prediction accuracy is improved using context adaptive error modeling as a feedback. Second, the conditional probability of prediction errors is sharpened by removing the sign redundancy among local prediction errors by applying sign flipping. Third, the conditional probability is sharpened further by reducing the number of distinct error symbols using error remapping function. Experimental results on benchmark data sets reveal that the enhanced algorithm achieves a better compression bit rate than our previous algorithm and other algorithms. It is shown that compression bit rate is much better for images that are rich in directional edges and textures. The enhanced algorithm also shows better rate-distortion performance and visual quality at the intermediate stages of progressive image transmission.

Likelihood-based Directional Optimization for Development of Random Pattern Authentication System (랜덤 패턴 인증 방식의 개발을 위한 우도 기반 방향입력 최적화)

  • Choi, Yeonjae;Lee, Hyun-Gyu;Lee, Sang-Chul
    • Journal of Korea Multimedia Society
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    • v.18 no.1
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    • pp.71-80
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    • 2015
  • Many researches have been studied to overcome the weak points in authentication schemes of mobile devices such as pattern-authentication that is vulnerable for smudge-attack. Since random-pattern-lock authenticates users by drawing figure of predefined-shape, it can be a method for robust security. However, the authentication performance of random-pattern-lock is influenced by input noise and individual characteristics sign pattern. We introduce an optimization method of user input direction to increase the authentication accuracy of random-pattern-lock. The method uses the likelihood of each direction given an data which is angles of line drawing by user. We adjusted recognition range for each direction and achieved the authentication rate of 95.60%.

A Study of Information Needs of Academic Library Users: With Special Emphasis on Reference Questions (대학도서관 이용자의 정보요구에 관한 연구: 참고질문을 중심으로)

  • 유재옥
    • Journal of the Korean Society for information Management
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    • v.18 no.2
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    • pp.125-142
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    • 2001
  • This study attempts to identify users' potential information needs in a medium-sized academic library by analyzing reference questions raised by library users for 64 days from October 10th to December 13th, 2000. A total of 474 reference questions were analysed of which only 10.1% were pure reference questions that dealt with the professional knowledge of reference librarians. 71.0% had nothing to do with professional knowledge, being directional(29.3%), mechanic(23.8%), and library policy related(l7.9%). Improvements in sign system and guiding materials in the library should be made to deliver reference services of good quality to academic library users.

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Segmentation and Recognition of Traffic Signs using Shape Information and Edge Image in Real Image (실영상에서 형태 정보와 에지 영상을 이용한 교통 표지판 영역 추출과 인식)

  • Kwak, Hyun-Wook;Oh,Jun-Taek;Kim, Wook-Hyun
    • The KIPS Transactions:PartB
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    • v.11B no.2
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    • pp.149-158
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    • 2004
  • This study proposes a method for segmentation and recognition of traffic signs using shape information and edge image in real image. It first segments traffic sign candidate regions by connected component algorithm from binary images, obtained by utilizing the RGB color ratio of each pixel in the image, and then extracts actual traffic signs based on their symmetries on X- and Y-axes. Histogram equalization is performed for unsegmented candidate regions caused by low contrast in the image. In the recognition stage, it utilizes shape information including projection profiles on X- and Y-axes, moment, and the number of crossings and distance which concentric circular patterns and 8-directional rays from region center intersects with edges of traffic signs. It finally performs recognition by measuring similarity with the templates in the database. It will be shown from several experimental results that the system is robust to environmental factors, such as light and weather condition.