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- 저자 : 심재연 ( Jae-youen Shim ) (검색결과 4 건)
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U-Learning 을 위한 스마트펜 인터페이스 시스템 디자인 및 개발
심재연 ( Jae-youen Shim ),김성환 ( Seong-whan Kim ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.2
In this paper, we present a design and implementation of U-learning system using pen based augmented reality approach. Student has been given a smart pen and a smart study book, which is similar to the printed material already serviced. However, we print the study book using CMY inks, and embed perceptually invisible dot patterns using K ink. Smart pen includes (1) IR LED for illumination, IR pass filter for extracting the dot patterns, and (3) camera for image captures. From the image sequences, we perform topology analysis which determines the topological distance between dot pixels, and perform error correction decoding using four position symbols and five CRC symbols. When a student touches a smart study books with our smart pen, we show him/her multimedia (visual/audio) information which is exactly related with the selected region. Our scheme can embed 16 bit information, which is more than 200% larger than previous scheme, which supports 7 bits or 8 bits information.
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심재연 ( Jae-youen Shim ),김성환 ( Seong-whan Kim ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.2
With the advent of cell phone digital cameras, and sophisticated photo editing software, digital images can be easily manipulated and altered. Although good forgeries may leave no visual clues of having been tampered with, they may, nevertheless, alter the underlying statistics of an image. Most digital camera equipped in cell phones employ a single image sensor in conjunction with a color filter array (CFA), and then interpolates the missing color samples to obtain a three channel color image. This interpolation introduces specific correlations which are likely to be destroyed when tampering with an image. We quantify the specific correlations introduced by CFA interpolation, and describe how these correlations, or lack thereof, can be automatically detected in any portion of an image. We show the efficacy of this approach in revealing traces of digital tampering in lossless and lossy compressed color images interpolated with several different CFA algorithms in test cell phones.
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영상기기의 프로파일 분석 기반 패턴추적에 의한 비디오 프레임의 위변조탐지
심재연 ( Jae-youen Shim ),천인혁 ( In-hyuk Chon ),김성환 ( Seong-whan Kim ) 한국정보처리학회 2011 한국정보처리학회 학술대회논문집 Vol.18 No.1
본 논문은 HD (High-definition) video, SD (Standard-definition) video, low quality video, handset video, 4 가지 Imaging Device 에 대한 프로파일 분석을 통해 비디오 프레임 상에 나타나는 위 변조를 검사하는 방법을 제안한다. High-definition video, Standard-definition video, low quality video, handset video 에 대한 분석을 하고 각 영상의 특이 점을 파악 하여 분류한 클래스에 대한 프로파일검사를 통해 EM Algorithm 을 이용하여 영상의 위 변조를 검사 하고 영상의 신뢰성을 높인다.
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스마트 학습지: 미세 격자 패턴 인식 기반의 지능형 학습 도우미 시스템의 설계와 구현
심재연 ( Jae-youen Shim ),김성환 ( Seong-whan Kim ) 한국정보처리학회 2011 한국정보처리학회 학술대회논문집 Vol.18 No.1
In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infra-red) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.
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