• Title, Summary, Keyword: pixel-based image similarity

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Dynamic Scene Segmentation Algorithm Using a Cross Mask and Edge Information (Cross Mask와 에지 정보를 사용한 동영상 분할)

  • 강정숙;박래홍;이상욱
    • Journal of the Korean Institute of Telematics and Electronics
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    • v.26 no.8
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    • pp.1247-1256
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    • 1989
  • In this paper, we propose the dynamic scene segmentation algorithm using a cross mask and edge information. This method, a combination of the conventioanl feature-based and pixel-based approaches, uses edges as features and determines moving pixels, with a cross mask centered on each edge pixel, by computing similarity measure between two consecutive image frames. With simple calcualtion the proposed method works well for image consisting of complex background or several moving objects. Also this method works satisfactorily in case of rotaitional motion.

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Iris Recognition Based on a Shift-Invariant Wavelet Transform

  • Cho, Seongwon;Kim, Jaemin
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.4 no.3
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    • pp.322-326
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    • 2004
  • This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

An advanced reversible data hiding algorithm based on the similarity between neighboring pixels

  • Jung, Soo-Mok
    • Journal of the Korea Society of Computer and Information
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    • v.21 no.2
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    • pp.33-42
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    • 2016
  • In this paper, an advanced reversible data hiding algorithm which takes the advantage of the spatial locality in image was proposed. Natural image has a spatial locality. The pixel value of a natural image is similar to the values of neighboring pixels. So, using the neighboring pixel values, it is possible to precisely predict the pixel value. Frequency increases significantly at the peak point of the difference histogram using the predicted values. Therefore, it is possible to increase the amount of data to be embedded. By using the proposed algorithm, visually high quality stego-image can be generated, the original cover image and the embedded data can be extracted from the stego-image without distortion. The embedding data into the cover image of the proposed algorithm is much lager than that of the previous algorithm. The performance of the proposed algorithm was verified by experiment. The proposed algorithm is very useful for the reversible data hiding.

An EM Algorithm-Based Approach for Imputation of Pixel Values in Color Image (색조영상에서 랜덤결측화소값 대체를 위한 EM 알고리즘 기반 기법)

  • Kim, Seung-Gu
    • The Korean Journal of Applied Statistics
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    • v.23 no.2
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    • pp.305-315
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    • 2010
  • In this paper, a frequentistic approach to impute the values of R, G, B-components in random missing pixels of color image is provided. Under assumption that the given image is a realization of Gaussian Markov random field, its model is designed such that each neighbor pixel values for a given pixel follows (independently) the normal distribution with covariance matrix scaled by an evaluates of the similarity between two pixel values, so that the imputation is not to be affected by the neighbors with different color. An approximate EM-based algorithm maximizing the underlying likelihood is implemented to estimate the parameters and to impute the missing pixel values. Some experiments are presented to show its effectiveness through performance comparison with a popular interpolation method.

Reversible Data Embedding Algorithm based on Pixel Value Prediction Scheme using Local Similarity in Image (지역적 유사성을 이용한 픽셀 값 예측 기법에 기초한 가역 데이터 은닉 알고리즘)

  • Jung, Soo-Mok
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.6
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    • pp.617-625
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    • 2017
  • In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

Content-Based Video Retrieval Algorithms using Spatio-Temporal Information about Moving Objects (객체의 시공간적 움직임 정보를 이용한 내용 기반 비디오 검색 알고리즘)

  • Jeong, Jong-Myeon;Moon, Young-Shik
    • Journal of KIISE:Software and Applications
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    • v.29 no.9
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    • pp.631-644
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    • 2002
  • In this paper efficient algorithms for content-based video retrieval using motion information are proposed, including temporal scale-invariant retrieval and temporal scale-absolute retrieval. In temporal scale-invariant video retrieval, the distance transformation is performed on each trail image in database. Then, from a given que교 trail the pixel values along the query trail are added in each distance image to compute the average distance between the trails of query image and database image, since the intensity of each pixel in distance image represents the distance from that pixel to the nearest edge pixel. For temporal scale-absolute retrieval, a new coding scheme referred to as Motion Retrieval Code is proposed. This code is designed to represent object motions in the human visual sense so that the retrieval performance can be improved. The proposed coding scheme can also achieve a fast matching, since the similarity between two motion vectors can be computed by simple bit operations. The efficiencies of the proposed methods are shown by experimental results.

Super-Pixels Generation based on Fuzzy Similarity (퍼지 유사성 기반 슈퍼-픽셀 생성)

  • Kim, Yong-Gil;Moon, Kyung-Il
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.2
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    • pp.147-157
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    • 2017
  • In recent years, Super-pixels have become very popular for use in computer vision applications. Super-pixel algorithm transforms pixels into perceptually feasible regions to reduce stiff features of grid pixel. In particular, super-pixels are useful to depth estimation, skeleton works, body labeling, and feature localization, etc. But, it is not easy to generate a good super-pixel partition for doing these tasks. Especially, super-pixels do not satisfy more meaningful features in view of the gestalt aspects such as non-sum, continuation, closure, perceptual constancy. In this paper, we suggest an advanced algorithm which combines simple linear iterative clustering with fuzzy clustering concepts. Simple linear iterative clustering technique has high adherence to image boundaries, speed, memory efficient than conventional methods. But, it does not suggest good compact and regular property to the super-pixel shapes in context of gestalt aspects. Fuzzy similarity measures provide a reasonable graph in view of bounded size and few neighbors. Thus, more compact and regular pixels are obtained, and can extract locally relevant features. Simulation shows that fuzzy similarity based super-pixel building represents natural features as the manner in which humans decompose images.

Efficient Hardware Architecture for Fast Image Similarity Calculation (고속 영상 유사도 분석을 위한 효율적 하드웨어 구조)

  • Kwon, Soon;Lee, Chung-Hee;Lee, Jong-Hun;Moon, Byung-In;Lee, Yong-Hwan
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.48 no.4
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    • pp.6-13
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    • 2011
  • Due to its robustness to illumination change, normalized cross-correlation based similarity measurement is widely used in many machine vision applications. However, its inefficient computation structure is not adequate for real-time embedded vision system. In this paper, we present an efficient hardware architecture based on a normalized cross correlation (NCC) for fast image similarity measure. The proposed architecture simplifies window-sum process of the NCC using the integral-image. Relieving the overhead to constructing integral image, we make it possible to process integral image construction at the same time that pixel sequences are inputted. Also the proposed segmented integral image method can reduce the buffer size for storing integral image data.

Real-Time Hierarchical Techniques for Rendering of Translucent Materials and Screen-Space Interpolation (반투명 재질의 렌더링과 화면 보간을 위한 실시간 계층화 알고리즘)

  • Ki, Hyun-Woo;Oh, Kyoung-Su
    • Journal of Korea Game Society
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    • v.7 no.1
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    • pp.31-42
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    • 2007
  • In the natural world, most materials such as skin, marble and cloth are translucent. Their appearance is smooth and soft compared with metals or mirrors. In this paper, we propose a new GPU based hierarchical rendering technique for translucent materials, based on the dipole diffusion approximation, at interactive rates. Information of incident light, position, normal, and irradiance, on the surfaces are stored into 2D textures by rendering from a primary light view. Huge numbers of pixel photons are clustered into quad-tree image pyramids. Each pixel, we select clusters (sets of photons), and then we approximate multiple subsurface scattering term with the clusters. We also introduce a novel hierarchical screen-space interpolation technique by exploiting spatial coherence with early-z culling on the GPU. We also build image pyramids of the screen using mipmap and pixel shader. Each pixel of the pyramids is stores position, normal and spatial similarity of children pixels. If a pixel's the similarity is high, we render the pixel and interpolate the pixel to multiple pixels. Result images show that our method can interactively render deformable translucent objects by approximating hundreds of thousand photons with only hundreds clusters without any preprocessing. We use an image-space approach for entire process on the GPU, thus our method is less dependent to scene complexity.

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Orientation-based Adaptive Prediction for Effective Lossless Image Compression (효과적인 무손실 영상압축을 위한 방향성 기반 적응적 예측 방법)

  • Kim, Jongho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.10
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    • pp.2409-2416
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    • 2015
  • This paper presents an orientation-based adaptive prediction method for effective lossless image compression. For a robust prediction, the proposed method estimates the directional information and the property near the current pixel in a support region-based fashion, not a pixel-based one which is sensitive to a small variation. We improve the prediction performance effectively by selection of the prediction pixel adaptively according to the similarity between support regions of the current pixel and the neighboring pixels. Comprehensive experiments demonstrate that the proposed scheme achieves excellent prediction performance measured in entropy of the prediction error compared to a number of conventional prediction methods such as MED, GAP, and EDP. Moreover the complexity of the proposed algorithm measured by average execution time is low compared to MED which is the simplest prediction method.