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检索条件"任意字段=Conference on Visual Communications and Image Processing 2005"

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SEGMENT-BASED VIDEO CODING USING AN AFFINE MOTION MODEL

SEGMENT-BASED VIDEO CODING USING AN AFFINE MOTION MODEL

引用

conference on visual communications and image processing 94

作者： JOZAWA, H NIPPON TELEGRAPH & TEL PUBL CORP HUMAN INTERFACE LABS YOKOSUKAKANAGAWA 23803 JAPAN

ISBN: (纸本)081941638X

This paper describes a segment-based motion compensation (MC) method where object motion is described using an affine motion model. In the proposed MC method, the shape of the objects is obtained using 5D K-mean clustering (employing three color components and two coordinates). Motion estimation and compensation employing the affine motion model are performed on each arbitrarily shaped object in the current frame. By using the affine motion model, rotation and scaling of objects can be compensated for in addition to translational motion. The residual between the current frame and the predicted one is DCT-coded and multiplexed with the motion and shape parameters. Simulation results show that the interframe prediction error is significantly reduced compared to the conventional block-based method. The SNR of the coded sequences obtained using the proposed method (modified MPEG1 with segment-based affine MC) is about 0.8 - 2 dB higher than that using the conventional one (original MPEG1 with block-based MC).

关键词： VIDEO CODING INTERFRAME PREDICTION MOTION ESTIMATION MOTION COMPENSATION AFFINE MOTION MODEL image SEGMENTATION K-MEAN CLUSTERING

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VECTOR FIELD visualIZATION BY PROJECTION PURSUIT - ANALYSIS OF PROJECTION INDEXES

VECTOR FIELD VISUALIZATION BY PROJECTION PURSUIT - ANALYSIS ...

引用

conference on visual communications and image processing 94

作者： HARIKUMAR, G BRESLER, Y UNIV ILLINOIS COORDINATED SCI LABDEPT ELECT & COMP ENGNURBANAIL 61801

ISBN: (纸本)081941638X

We address the problem of the display of vector valued images, also known as vector fields or multiparameter images, in which a vector of data, rather than a scalar, is associated with each pixel of a pixel grid. Each component of the vector field defines a gray-scale image on the pixel grid. Vector fields usually arise when more than one physical property (henceforth an attribute) is measured for the object being imaged. Mapping the measured values of any one attribute to gray-scale on a pixel grid defines an image. The collection of these images then defines a vector field, with a vector of attributes, corresponding to each pixel. The object usually consists of several disjoint regions, each made up of a region type, which we shall call a class. A set of attributes separates the classes (and therefore also the regions) if, for any two different classes, there exists an attribute in the set that has distinct values for the two. We refer to a pixel grid where the pixels are labeled as belonging to different regions as an Underlying image (UI) . Vector fields arise naturally in any application that involves the display of multiparameter data, or multisensor fusion, as surveyed in [1], the classical example being multispectral satellite images. Once we have a vector field of an object consisting of measurements of a set of attributes that separate all of the classes in the object, we have the potential to distinguish between all of its regions. Regardless of the ultimate goal of the specific imaging application, most applications share a common intermediate goal, which is the one we address: to present a specialist human observer with an image that will maximize his chances to classify correctly the image pixels into different regions, that is, segment the image. Unfortunately, the human observer is inefficient at assimilating and intergrating multidimensional data, and traditional methods like the parallel display of the component images are often misleading, as demo

关键词： image segmentation visualization image fusion Quality measurement Sodium visual analytics Feature extraction image quality Monte Carlo methods Niobium

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BOUNDARY-CONTROL POINT BASED MOTION REPRESENTATION SCHEME WITH ITS APPLICATION IN VIDEO CODING

BOUNDARY-CONTROL POINT BASED MOTION REPRESENTATION SCHEME WI...

引用

conference on visual communications and image processing 94

作者： LI, J LIN, XG WU, YS TSING HUA UNIV DEPT ELECTR ENGNDIV IMAGE PROCBEIJING 100084PEOPLES R CHINA

ISBN: (纸本)081941638X

In this paper, a boundary-control point (BCP) based motion representation scheme is proposed. According to the scheme, the dense motion field is described by the object boundary and the motion vectors located at predefined grids--the control points. Our scheme differs from the conventional block based motion representation scheme in the point that the motion field has more degree of freedom. It can represent complex motion, e.g., translation, rotation, zooming and deformation. And the motion field is generally continuous, with discontinuity only at the object boundary, so the distortion after motion compensation is mainly geometrical deformation, which is relatively insensitive to human eye compared with the block effect caused by the conventional block based scheme. A pixel threshold criterion is also proposed to evaluate the BCP based motion compensated prediction (MCP) image and to determine whether the MCP error needs to be transmitted. Finally, a BCP based video encoder is constructed. With nearly the same decoded signal-to-noise ratio about 20 - 55% bit rate saving can be achieved compared with MPEG-I, while the subjective quality of the BCP based scheme is better than that of MPEG-I. The new scheme is also quire unlike the model-based scheme for it needs no complex scenery analysis. Some promising experimental results are shown.

关键词： VIDEO CODING BOUNDARY-CONTROL POINT PIXEL THRESHOLD CRITERION GEOMETRICAL DISTORTION MAXIMUM A-POSTERIOR

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image SEQUENCE RECOGNITION

IMAGE SEQUENCE RECOGNITION

引用

conference on visual communications and image processing 94

作者： TASCINI, G ZINGARETTI, P UNIV ANCONA INST INFORMATI-60100 ANCONAITALY

ISBN: (纸本)081941638X

image sequence recognition is an interesting problem involved in various situations of the Computer Vision field and in particular in mobile robot vision. Typical for this purpose is the motion estimation from a series of frames. Many techniques of motion estimation are described in literature 10, 13, 22 The approaches are normally divided in two categories: pixel based methods and feature based methods. In both the motion is estimated in two steps: 1) 2D motion analysis (feature based) or estimation (pixel based), 2) 3D motion estimation. The pixel based, or flow based, method uses local changes in light intensity to compute optical flow at each image point and then derives 3D motion parameters . The feature based method, on which it falls our choice, firstly extracts the features (as corners, point of curvature, lines, etc.). They are used as features: sharp changes in curvature 15, global properties of moving objects 18, lines and curves 16, centroids 6 Secondly it establishes the correspondences of these features between two successive frames (correspondence problem), and finally it computes motion parameters and object structure from correspondences (structure from motion problem). The motion correspondence is the most difficult problem. Occlusion masks the features and noise creates difficulties. Given n frames taken at different time instants and m points in each frame, the motion correspondence maps a point in one frame to another point in the next frame such that no two points map on the same point. The combinatorial explosiveness of the problem has to be constrained; in Rangarajan and Sah 19 it is proposed the proximal uniformity constraint: given a location of a point in a frame, its location in the next frame lies in the proximity of its previous location. Even tough the problem has not yet been solved, many solutions are proposed for 3D motion estimation, assuming that the correspondences has been established 12, 13, 24 Regularization theory has also be

关键词： Motion estimation Data modeling Detection and tracking algorithms 3D modeling Chemical elements Sensors 3D image processing image segmentation Cameras image acquisition

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MODEL-BASED image-CODING

MODEL-BASED IMAGE-CODING

引用

conference on visual communications and image processing 94

作者： AIZAWA, K UNIV TOKYO DEPT ELECT ENGNBUNKYO KUTOKYO 113JAPAN

ISBN: (纸本)081941638X

Historically. progress in image coding tecliniqnes has been made by incorporating resnits from other fields SUCh as information theory. Most of the existing coding methods such as predictive coding. transform coding, and vector quantization belong to information-theory-based methods in which image signals are considered as ranidoni signals aiid compressed by exploiting their stochastic properties. Apart from these iiiformation-tlieoretic coding iniethods, research on new approaches such as modelbased coding (which is related to 1)0th image analysis and computer graphics) has recently intensified. An essential difference between conventional coding methods and these new approaches is the image model they assume. Contrary to conventional coding methods which efficiently represent 2-D waveforms of image signals model-based co ding represent iniage signals using structural image models which take into accomit the 3-D properties of the scene in some sense. There have been two major approaches to model-based image coding. One approach uses general niodels such as planar patches and sniooth surfaces to represent the 3-D properties of the oI)jects [11, 19, 28. 31, 33. 36, 39. 40, 43]. The other approach utilizes detailed 1)arameterized 3-D object models such as a parameterized facial model [2. 6, 13. 15. 17, 22. 24. 2G. 32, 35. 41. 47, 48. 49. 51. 54]. TIne former approach has been discussed in the context of advanced motion compensation. The latter approach is clearly different from the conventional approaches. since it uses an explicit 3—D model and encodes images with coniputer—vision— Oiieiite(il tecliniques an(I 1eJ)1()(luces iniages with colnj)uter—grapllics—oriented techniques . Both approaches will potentially achieves very low l)it rate video compression. Iii the following sectionis. unage co(ling schemes are described froni the point of view of their associated image IIli)delS. Amoiig our works related to these paradignins in the Univ. of Tokyo [2. 3, 4. 8, 9, 10,

关键词： 3D modeling Motion models image compression Model-based design 3D image processing Data modeling image analysis Motion estimation Solid modeling Systems modeling

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A distortion measure for image artifacts based on human visual sensitivity

A distortion measure for image artifacts based on human visu...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： S.A. Karunasekera N.G. Kingsbury Signal Processing and Communications Laboratory Department of Engineering University of Cambridge Cambridge UK

A visual model which gives a distortion measure for coding artifacts in images is presented. Given the original and the reproduced image as inputs, the model output is a numerical value, which quantifies the visibility of error in a region of the reproduced image. The model aims to predict accurately the visibility of the common types of coding artifacts blocking, ringing and blurring. visual sensitivity to these artifacts is measured using psyche-visual experiments. The sensitivity measures thus obtained are used to derive the visual model which predicts the visibility of error. The final model is tested on real images, and the results show that the error visibility predicted by the model correlates well with the subjective ranking.< >

关键词： Distortion measurement Humans Predictive models image edge detection image coding Particle measurements Psychology Testing visual perception Frequency

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Masking and quantization laws in a visual subband coding scheme

Masking and quantization laws in a visual subband coding sch...

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IEEE International conference on image processing

作者： A. Saadane H. Senane D. Barba Ireste Lati Sei LaChantrerie Nantes France

To design psychovisual quantizers, experiments based on the visibility of the quantization noise have been conducted by Saadane, Senane and Barba (see visual communications and image processing, Oct. 1994). The results obtained show that for a given visual decomposition scheme and within a subband, the decision thresholds and reconstruction levels follow a linear law with an interval quantization varying with frequency and orientation. In this paper, and in order to examine the masking effects on the perception of quantization noise, further experiments have been conducted by keeping the same methodology and the same decomposition scheme as Saadane et. al. Two important results have been obtained. First the masking didn't affect the linear behavior observed when no masking was considered. Secondly, with the contrast definition used, and even a slight increase in the quantization interval has been observed for the radial subbands, the masking was essentially due to the angular subbands.< >

关键词： Quantization Frequency Degradation Psychology Gratings Equations image reconstruction Humans visual system image coding

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A block transform coder for arbitrarily shaped image segments

A block transform coder for arbitrarily shaped image segment...

引用

IEEE International conference on image processing

作者： H.H. Chen M. Reha Civanlar B.G. Haskell Visual Communications Research Department AT and T Bell Laboratories Inc. Holmdel NJ USA

This paper describes a method for coding arbitrarily shaped image segments. The method uses an iterative technique based on the theory of successive projection onto convex sets to determine the best transform coefficients. It uses block transforms with frequency domain region-zeroing and space domain region-enforcing operations for effective coding of image segments of arbitrary shape. A major strength of this method is that it can be implemented in real-time using existing codec hardware at an insignificant additional cost.< >

关键词： image segmentation image coding Shape Karhunen-Loeve transforms Transform coding Codecs Hardware image representation Discrete transforms visual communication

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Tree based motion compensated video coding

Tree based motion compensated video coding

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IEEE International conference on image processing

作者： R. Leonardi H. Chen Visual Communications Research Department Visual Communications Research Department AT and T Bell Laboratories Holmdel NJ USA Signals Communications Laboratory Department of Electronics for Automation University of Brescia Brescia Italy

We present a novel technique to encode video sequences, that performs a region-based decomposition of each frame on the basis of motion information. Using the segmentation map, any region in a frame to be encoded will be predicted from a single reference frame, using motion compensated prediction. The use of a single reference frame avoids feedback of the prediction error information in the prediction of successive frames. Coding is simply obtained by describing the segmentation map and the associated motion information. Error information will not be provided for low bit-rate applications. The segmentation map is described using a quadtree structure. Within such a tree structure, we show how motion information can be predicted either spatially or temporally, so as to minimize redundancy of information. The motion and segmentation information are estimated on the basis of a two stage process using the frame to be encoded and the reference frame: (1) a hierarchical top-down decomposition; (2) a bottom-up merging strategy. The proposed posed method is used to encode to encode QCIF video sequences with a reasonable duality at a 10 frame/s rate using roughly 20 kbit/s.< >

关键词： Video coding Video sequences Bandwidth Video compression Context Encoding image sequences Automation Postal services visual communication

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Self-organizing segmentor and feature extractor

Self-organizing segmentor and feature extractor

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IEEE International conference on image processing

作者： R.D. Dony S. Haykin Communications Research Laboratory McMaster University Hamilton ONT Canada

Proposes a novel approach to segmentation using a combination of Hebbian learning and competitive learning in a self-organizing manner. The network is modular, with each module corresponding to a different class of the input data. A module consists of a weight vector that is calculated during an initial training period. The appropriate class for a given input vector is determined by a maximum entropy classifier. The resulting network consistently extracts perceptually relevant features from image data. As well, the class representations are analogous to the arrangement of directionally sensitive columns in the visual cortex.< >

关键词： Feature extraction Entropy Equations image coding Tellurium Vectors Transform coding image segmentation Distortion Upper bound

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