This paper presents and integrated view of personalized information spaces. The topics that are described cover different dimensions of describing, customizing, reusing and presenting multimedia content. In order to have personalized multimedia content the systems should provide the ability to access it when and where it is necessary, in a way that is appropriate for each specific user. The paper describes several multimedia information processing and visualization systems that explore these concepts. More specifically, the applications and systems include annotation tools for describing and enhancing multimedia content, personalization and customization techniques, and tools for anytime, anywhere access to multimedia information. The examples of information access are an augmented reality system and applications for mobile devices, representing two of the dominant and convergent trends in ubiquitous computing.

With the recent advances in computer technology, medical images and multimedia information have a major impact to our modem life. This Keynote presentation will give a brief introduction of the current research on medical images and multimedia data management and processing, conducted at the Center for Multimedia Signal Processing (CMSP) and the Biomedical and Multimedia Information Technology (BMIT) Group, as well as in other major research laboratories.

In non-parametric pattern recognition, the probability density function is approximated by means of many parameters, each one for a density value in a small hyper-rectangular volume of the space. The hyper-rectangles are determined by appropriately quantizing the range of each variable. Optimal quantization determines a compact and efficient representation of the probability density of data by optimizing a global quantizer performance measure. The measure used here is a weighted combination of average log likelihood, entropy and correct classification probability. In multi-dimensions, we study a grid based quantization technique. Smoothing is an important aspect of optimal quantization because it affects the generalization ability of the quantized density estimates. We use a fast generalized k nearest neighbor smoothing algorithm. We illustrate the effectiveness of optimal quantization on a set of not very well separated Gaussian mixture models, as compared to the expectation maximization (EM) algorithm. Optimal quantization produces better results than the EM algorithm. The reason is that the convergence of the EM algorithm to the true parameters for not well separated mixture models can be extremely slow.

Despite research activity during the past decade, the problem of how to carry out 3D scene reconstruction from a video sequence is not fully solved. Many techniques exist, useful in different situations. However, for a general scene, methods can still be quite costly in terms of time. The present paper discusses methods whereby the existence of planar or almost planar sections of the scene may be exploited throughout the reconstruction process. We consider the homography induced by a plane to aid in point tracking, projective reconstruction, self calibration and model building. Two recent algorithms for projective reconstruction in the presence of planes are reexamined and presented in a new form.

An algorithm complexity is a very crucial issue in the algorithm design, especially if large data sets are to be processed. Data search is very often used in many algorithms and hash function use gives us a possibility to speed up the process significantly. Nevertheless, it is very difficult do design a good hash function especially for geometric applications. This paper describes a new hash function, its behaviour and use for non-trivial problems. Some problems can be solved effectively using the principle of duality and the hash data structure. Also some problems that cannot be solved in Euclidean space can be solved if dual representation is used and some examples are presented, too.

We are participating in the international competition to develop robots that can play football (or soccer as it is known in the US and Canada). The competition consists of several leagues each of which examines a different technology but shares the common goal of advancing the skills of autonomous robots, robots that function without a central hierarchical command structure. The Dutch team, Clockwork Orange, involves several universities and the contribution of our group at the TU Deift is in the domain of robot vision and motion. In this paper we will describe the background to the project, the characteristics of the robots in our league, our approach to various vision tasks, their implementation in computer architectures, and the results of our efforts.

Image compression based on regions of interest (ROl) means to compress interesting regions in an image with high quality, and to compress uninteresting regions with relatively low quality. Based on this idea, a multi-threshold fractal image-coding algorithm based on regions of interest is proposed in this paper. It uses different error threshold for different regions, and puts both near-lossless coding in the ROl and lossy coding in the UROI (uninteresting regions) under the same fractal frame. Quadtree partition algorithm is employed to compresses the regions of interest near-losslessly and the other regions roughly. By using this algorithm, good decoding image quality of the regions of interest can be obtained while maintaining high compression ratio. Image coding time is also shortened greatly. Simulation results for some medical images have shown the effectiveness of the proposed method. As compared to other known method, the proposed method is very attractive both in computation and in storage.

Previous error control methods have obvious defects more or less for the videoconference applications. A simple and efficient error control mechanism based on adaptive intra-frame refreshment, in this paper, is proposed to conceal the transmission errors ofreal time video. This scheme maintains a backward channel to feedback the error information found by the decoder so that the coder is able to adjust the interval of intra-frame coding dynamically to conceal all sorts of real time video transmission errors. Simulation results present that this method can efficiently eliminate the temporal and spatial error propagation. Compared to conventional schemes, our algorithm need not modify the coder and decoder (Codec) and no more CPU time is consumed as well. As a result, this algorithm is especially fit for the videoconference or visual telephone applications.

In this paper, a novel robust image coder with scalable resolution is presented, called Robust ZeroBlock Wavelet (RZBW), which is suitable for image transport over a noisy channel. In the coder, the zeroblock-based coding algorithm is used, which proved to be an efficient technique for exploiting the clustering of energy found in image transforms. The coder provides both excellent compression performance and graceful degradation over noisy channel. The coder compresses the wavelet coefficients from low frequency to high frequency, so the resolutions ofthe reconsiructed image are scalable.

MPEG-4 is applied to varieties of current and future multimedia applications. The standard not only supports existed frame-based coding standards such as MPEG-i, MPEG-2 or H.263, but also provides content-based representation and flexible toolbox, which lead to MPEG-4 more complicated. This paper first briefly presents the implementation method of the video decoding of MPEG-4 Core Visual Profile, which is a subset of MPEG-4 standard. The Core Visual Profile is quite suitable to streaming video, which will possibly become the hot spot for the development ofMPEG-4. Then, the paper proposes a design scheme for the basic hardware structure of the decoding system based on TMS32OC6X DSP, and simply analyzes the decoding processing of the system.

A novel stereoscopic coding-decoding framework was proposed in this paper. The reference image stream of a stereoscopic sequence is independently coded by a conventional MPEG-type scheme at higher quality; only a few reference frames in target stream are coded and transmitted. The rest frames are 'skipped' and are reconstructed at the decoder using a Combine Stereoscopic Frame Estimation and Interpolation without search (CSFEI) technique proposed in this paper. In order to estimate the unfilled regions due to occlusion, a global solution based on a probability model is also proposed. Simulation results show that the CSFEI scheme can achieve a PSNR gain (about 2-3 dB) as compared to the block-based matching algorithm.

In this paper, we pay attention to the application of image compression and reconstruction with Newton-Thiele's rational interpolation theorem . We present a new algorithm which is suited for fast computation with less distortion . Many examples are given, and the result shows that this algorithm is more convenient in computation and more efficient in implementing reconstruction.

In this paper, the properties of the gray level mixing function in two-dimensional chaotic map image encryption are analyzed. We also discuss the necessity of introducing parameters and the methods how to present diffusion mechanism and interfuse a pseudo-random number sequence in the gray level mixing function. We proposed a new kind of gray level mixing function which not only have diffusion mechanism but also have random variable . It is shown in experiment that this method has good performance.

A small deconvolution kernel for image restoration has been sought based upon minimization of a target function, leading to a new restoration technique requiring considerably less computation compared to many other approaches. Regularization is achieved by introducing a multiplier that is in proportion to the average energy ofthe additive noise contained in the degraded image. The average noise energy may be estimated from the observed degraded image. Experimental results are given to show performance of the proposed method.

The disadvantage of the generalized learning vector quantization (GLVQ) and fuzzy generalization learning vector quantization (FGLVQ) algorithms is discussed. A revised GLVQ (RGLVQ) algorithm is proposed. Because the iterative coefficients of the proposed algorithms are properly bounded, the performance of our algorithms is invariant under uniform scaling of the entire data set unlike Pal's GLVQ, and the initial learning rate is not sensitive to the number of prototypes as Karayiannis's FGLVQ. The proposed algorithms are tested and evaluated using the iRIS data set. The efficiency of the proposed algorithms is also illustrated by their use in codebook design required for image compression based on vector quantization. The training time of RGLVQ algorithm is reduced by 20% as compared with Karayiannis's FGLVQ but the performance is similar.

For remote sensing imagery, every sensor system has unique system response, namely, point spread function (PSF), or modulation transfer function (MTF), which can be considered as sampling kernel given a prior. Sampling process like this doesn't satisfy Shonnon-Whittaker Representation Theorem's requirements, in such case, exact reconstruction is impossible. We have to look for optimal reconstruction in the sense of mean square, i.e. L 2 -norm. In this paper, we are mainly concerned with the applications of optimal reconstruction theory in remote sensing image processing, our aim is to develop a new resampling method for image magnification.

In fractal image compression, an image is coded as a set of contractive transformations, and is guaranteed to generate an approximation to the original image when iteration applied to any initial image. In this paper, according to Jacquin' 5 PIFS algorithm, and by analyzing traditional fractal mapping parameters, a kind of convolution-based fast fractal image coding scheme (CBFC) is advanced. To speed up the encoding and improve the compression ratio, it is combined with quad-tree partitioning neighbor searching algorithm. To improve the real-time performance of the algorithm, it is performed on TMS320C6201. Experiments results of algorithms based on CBFC, and CBFC using quad-tree partitioning structure on DSP are given in this paper as comparisons. The results show that fractal image real-time coding can be realized with the considerable reconstructive image and coding time.

The implementation of an image-based virtual navigation system is presented. An adaptive nonuniform sampling method of 4D light field is implemented based on slit images. Using the quadtree structure, this approach reduces the storage cost and accelerates the sampling process. The cause of holes is discussed and a hole-filling algorithm is designed. To avoid frame discontinuity, issues and approaches of memory management, track prediction and collision detection are discussed. Finally, an example navigation system of Science and Technology University of Macao shows the efficiency of the algorithms discussed.

In this paper, we propose an image compression method using the wavelet transform and context-based arithmetic coding exploiting bit plane conelation. The proposed method decomposes an image into several subband images using the discrete wavelet transform; the transform coefficients in each subband are classified into two classes; each subband image is then quantized; a "shuffling" process is applied to quantized wavelet coefficients; and finally arithmetic coding using the optimum context is carried out for bit plane coding of each subband. The performance improvement of the proposed method turns out to be mainly due to the "shuffling" process and the optimum context for arithmetic coding. Experimental results show that the proposed coder is similar to or superior to well-known existing coders, e.g. EZW and SPIHT, for images with low conelation.

Nowadays with the proliferation of readily available image content, image hiding has become a topic of very importance. In many applications, it is desirable to embed identifying images for authentication. This paper presents a novel digital image-hiding scheme based on blending technology in the frequency domain. The digital image blending is first introduced, and then the concept of digital image blending is generalized to the DCT domain. The properties of DCT blending are also studied. With these properties of the DCT blending, a novel digital image-hiding scheme is presented. The experimental results show that the scheme can be expediently realized and it is robust in a certain extent. The scheme also can be used in digital watermarking.

This paper presents a fast algorithm for fractal image compression. The algorithm uses quadtree partitioning to partition an image into image blocks of different sizes. Each of the image blocks is normalized to have zero mean and unity variance, and represented by a feature vector of dimension 1 6. The feature vectors, which can provide an accurate representation of the image blocks, are composed of the means and/or variances of each of the rows and columns. The k-d tree structure is used to partition the feature vectors of the domain blocks. This arrangement allows the search of the best matched domain block for a range block efficiently and accurately. An efficient encoding approach for low complexity range blocks is also proposed, which encodes the mean of a range block without searching the domain blocks. Moreover, during the range-domain matching process, a simple but very efficient search by using the property of zero contrast value is introduced, which can further improve the encoding time and compression ratio, especially in high compression ratio. This can lead to an improvement in encoding time and an increase in compression ratio, while maintaining comparable image quality. Experimental results show that the run-time required by our proposed algorithm is over 200 times faster than that of a full search.

Because of rapidness and easy to update, raster map is important in GIS and digital mapping. In many circumstances, the projection that users demand is not according with the original projection of the raster map, thus the projection transformation of raster map is necessary, Raster map projection transformation, which is essentially image transformation, involves transformation among four coordinate systems: original image coordinate system, original projection coordinate system, new projection coordinate system, new image coordinate system. In this paper, compared with the vast computation and the slow speed of traditional transform method, a rapid algorithm of raster map projection transformation based on dual transformation is researched. In this algorithm, first of all, transform rectangle control grid strictly according to the formula of projection transformation; and then, aiming at each rectangle, using the four corners to construct dual linear transformation polynomial between rectangle image and corresponding quadrilateral image in original raster map; finally, the direct transformation between image coordinates of two maps is realized. The experiment has proved that this algorithm not only satisfies precision demand, but also greatly improves transformation speed.

To effectively counteract the deceptions in multimedia ownership verification, the reversibility in the widely used and researched adaptive watermarking is investigated from a new point of view. The reversibility, together with its resulting attacks, is introduced and extended to adaptive systems. The mostly typical existing counteraction is revised and its potential is pointed out. Then, the intrinsic irreversibility of some adaptive technologies is disclosed and evaluated. Under these technologies, attackers have great difficulty dividing a released version into their claimed original data and scaled watermarks, and in the meantime making the latter be the adaptive results based on the former. Their reversed solutions are violently perturbed and perceptually unacceptable. The condition number of the coefficient matrix of the reverse equations can be employed to assess the degree of the perturbation. The experiments that use the proper adaptive filter to enlarge the condition number force the attackers to solve the difficult problems in algebra and signal processing. Instead of using specialized security processing, such as hashing and randomization, they exploit the irreversible nature of chosen technologies so that a more feasible irreversible watermarking scheme is achieved.

Network Keys Exchange Facility (NKEF) is a kind of negotiatory protocol. With it, network user can correspond with each other in ID authentication mode, encryption styles and secure connect time. It's one of research hotspots of network security problem. In this paper, Shamir protocol based scheme for Secret Transmission of digital image is proposed. According to the exchangeable character of encrypting operator, we give the scheme, which can overcome the traditional method's disadvantages including insecurity and that the amount of key is too large. This method takes full advantage ofShamir protocol' s skillful idea.

The basic concept of fractal and the mathematics of fractal compression are introduced in this paper, The basic principle and implementing method ofthe tradition fractal image compression are expound, A new method of sequence image fractal coding based on the visual character is proposed. In this coding algorithm, we use different fractal image coding method for different coding block after fully considering the statistic character, based on which we define the visual character of the coding block, then code the parameters of the iteration function by variably length coding. Consequently we obtain the higher compression ratio and the better decoding subjective quality.

VQ technology has been proved to be an important technology and are extensively used in the low-bit-rate image compression. The VQ quantizer consists of two procedures: an encoder and a decoder. The encoder assigns each input vector X to an index i, which points to the closest codeword Yi in the codebook. The decoder uses the index i to look up the codeword 1', in the codebook. With good designed coodbook, the VQ quantizer can obtain very low bit rate compressed image while the decoded images have high SNR. The VQ also leads to blocking effect which is not comfortable to naked eye. To smooth the decoded image, some image filters for color image are studied here. Linear filter, such as scalar/vector mean filter, can not fulfill the target. The nonlinear filter, such as scalar median filter, vector median filter and vector direction filter can do better work. The neural network image filter proposed in this paper has a forward structure. This filter is optimized with advanced GA method. Then this filter is applied to color image filter and have a better performance. The parallel structure makes it easy to implement on the DSP device. Both VQ encoder/decoder and NN image filter are implemented on the fastest DSP, TI TMS320C6416.

One of the most significant features of diagnostic echocardiographic images is to reduce speckle noise and make better image quality. In this paper we proposed a simple and effective filter design for image denoising and contrast enhancement based on multiscale wavelet denoising method. Wavelet threshold algorithms replace wavelet coefficients with small magnitude by zero and keep or shrink the other coefficients. This is basically a local procedure, since wavelet coefficients characterize the local regularity of a function. After we estimate distribution of noise within echocardiographic image, then apply to fitness Wavelet threshold algorithm. A common way of the estimating the speckle noise level in coherent imaging is to calculate the mean-to-standard-deviation ratio of the pixel intensity, often termed the Equivalent Number of Looks(ENL), over a uniform image area. Unfortunately, we found this measure not very robust mainly because of the difficulty to identify a uniform area in a real image. For this reason, we will only use here the S/MSE ratio and which corresponds to the standard SNR in case of additivie noise. We have simulated some echocardiographic images by specialized hardware for real-time applicati on;processing of a 512*512 images takes about 1 mm. Our experiments show that the optimal threshold level depends on the spectral content of the image. High spectral content tends to over-estimate the noise standard deviation estimation performed at the fmest level ofthe DWT. As a result, a lower threshold parameter is required to get the optimal S/MSE. The standard WCS theory predicts a threshold that depends on the number of signal samples only.

This paper introduces a two-phase algorithm to extract a center-adjusted, one-voxel-thick line representation of cerebral vascular trees from volume angiograms coded in gray-scale intensity. The first stage extracts and arranges the vessel system in the form of a directed graph whose nodes correspond to the cross sections of the vessels and whose node connectivity encodes their adjacency. The manual input reduces to the selection of two thresholds and the designation of a single initial point. In a second stage, each node is replaced by a centered voxel. The locations of the extracted centerlines are insensitive to noise and to the thresholds used. The overall computational cost is linear, of the order of the size of the input image. An example is provided which demonstrates the result of the algorithm applied to actual data. While being developed to reconstruct a line representation of a vessel network, the proposed algorithm can also be used to estimate quantitative features in any 2-D and/or 3-D intensity images. This technique is sufficiently fast to process large 3-D images at interactive rates using commodity computers.

Three kinds of image reconstruct algorithms for Electrical Resistance Tomography (ERT) has been researched, and a new ERT reconstruct algorithm-Regularized general inverse(RGI) ERT reconstruct algorithm is proposed, which is based on linearity ERT forward problem, and makes use of general inverse to confirm the minimum norm error solution of ERT inverse problem. Meanwhile, adopting regularized method to stabilized the numerical value. The observation operator is set up by multiple linear regression method. Three restriction conditions is brought to bear the optimum stabilization solution. The simulation result shows that reconstructed image can reflect the truth medium distribution in the field truly including different complex distributions. After filtering the images by unite bound for the same medium distribution, the average of CSIE image reconstructed by linear back project algorithm, sensitivity coefficient algorithm and regularized general inverse algorithm is 12%, 9% and 6% respectively. The result shows that the image quality reconstructed by regularized general inverse algorithm is improved in evidence than that of the other two algorithms. The calculate amount of regularized general inverse algorithm is same as one step sensitivity coefficient algorithm, the speed of reconstruction is fast.

In this paper, we present a novel digital watermark system frameworkfor 3D mesh model with all mesh attributes, including position coordinates, normal, color and texture coordinates etc. Within this framework, 3D mesh model attributes are considered as geometry signals defined on mesh surfaces. A planar parameterization algorithm, which is first proposed by us, is used to map 3D mesh models to 2D parametric meshes. Geometric signals are then transformed into 2D signals. Then a wavelet-based watermark casting scheme is proposed to embed the watermark into some wavelet coefficients. Experimental results show that the embedded watermark is robust against various geometry signal processing.

Thresholding is difficult for images with poor contrast or illumination, intensive noise and non-planar background. An active surface based adaptive thresholding algorithm is proposed in this paper. Derived from the idea of active contour models, an active surface model is used to estimate the background surface ofthe image. Subtraction ofthis active surface from the original image surface is to remove the influence of uneven background and poor illumination, and convert the problem to a global threshold one. Thus a proper choice of the global threshold will obtain a desirable binary result.

By replacing the complex-valued Gabor basis functions of the complex-valued discrete Gabor transforms (CDGTs) with real-valued Gabor basis functions, we propose fast algorithms for 1 -D and 2-D real-valued discrete Gabor transforms (RDGTs) in this paper. The RDGT algorithms provide a simpler method than the CDGT algorithms to calculate the transform (or Gabor) coefficients of a signal or an image from finite summations and to reconstruct the original signal or image exactly from the computed transform coefficients. The similarity between the RDGTs and the discrete Hartley transforms (DHTs) enables the RDGTs to utilize the fast DHT algorithms for fast computation. Moreover, the RDGTs have a simple relationship with the CDGTs such that the CDGT coefficients can be directly computed from the RDGT coefficients.

Codeword index assignment (CIA) is a key issue to vector quantization (VQ). The application of tabu search algorithm has made some achievement in solving the codeword index assignment. Combined with the notion of tabu search the energy allocation scheme has been also successfully used to overcome channel error sensitivity . In this paper, a new algorithm called modified tabu energy allocation algorithm (MTEAA) is applied to index transmission of codeword for the purpose ofminimizing the extra distortion due to bit errors. Simulated annealing (SA) technique and a new parameter are introduced in the iteration of the tabu energy allocation approach (TEAA) to improve the convergence performance. Experimental results demonstrate that the proposed algorithm is superior to TEAA and the conventional energy allocation algorithm (CEAA) by evaluating the performance of channel distortion.

The automatic sorting of bank bill has become very important to speed up the office automation. Some methods have been reported to classify the denomination and directions of bank bill. But there were no reports about defile detection. In this paper, we designed a fast and effective algorithm to detect defiles on bank bill. We make use of mathematical morphology image processing to do shift and rotation correction. In order to reduce CPU time, we adopt pyramidal strategy to do template matching. In addition to these, we designed a check-up means to eliminate dummy defiles. The test results prove that this algorithm is robust. It's not sensitive to random noise and geometry distortion. In a word, this algorithm is quite effective.

Based on evolutionary computation, a new classification approach, OCEC, for radar target recognition using high-resolution range profiles is proposed. OCEC has nothing with dimension ofthe input space and has advantages of small amount of computation and stable performance. Simulations are presented to classify microwave anechoic chamber data for three different scaled airplane models. The results show OCEC has higher performance than other algorithms for recognition ofhigh-resolution radar target range profiles.

A simple and effective error detection and error concealment based on the H.263 video decoder has been described in this paper. The H.263 syntax structure and semantics, VLC code word and continuity property of video signal at the video decoder are employed to process the error detection. The spatial and temporal correction is made full of use for error concealment. It has been found that this method can greatly improve the quality of the reconstruction images without decreasing the coding efficiency.

This paper proposes an optimization model for extracting edges in gray-scale images. The model sufficiently utilizes the gray-level information in a pair of orthogonal directions at each considered pixel. The model has three major features in its novelty: (1) Emphasizing the globality of traditional local features; (2) Being a generalized case of the classical snake models; and (3) Offering a theoretical interpretation to the setting of the parameters for the method based on the Simulation of Particle Motion in a Vector image Field (SPMVIF). Our Edge Detection from Edge Knots (EDEK) model can be divided into two stages: the generation ofedge knots on or near edges and a propagation process for producing complete edges from these edge knots. One advantage ofour approach is that the propagation process does not depend on any control parameters. The EDEK model is suitable for live plant image processing, which is demonstrated by a number of simulation results on the edge detection of live plant images. Our model is simple in computing and robust, and can perform very well even in situations where high curvature exists.

This paper studies restoration of images blurred caused by uniform linear motion and its spatial domain processing. Horizontal motion is taken as the canon case, 1 -dimensional traveling wave equation is adopted as the mathematical model for image blurring, and Hough transformation is applied to detection of the motion direction. As for oblique motions, they are transferred to horizontal ones for easy solution. Experimental results show that the deblurred images resulting from this spatial domain processing are of better quality than that from traditional frequency domain processing where the ring effects will more or less appear and the computing cost is high.

Landsat TM image is the most popular and universal RS information source, and got wide uses in different fields such as resource investigating, environment monitoring, urban planning, disaster preventing and as on. Although TM image has got wide applications, its use in mining area is still in experiment and beginning stage because mining area is a kind of special and complex geographic region. One of the most important issues is to study the information charaéteristics and determine the most effective band combinations oriented to given region and task. In this paper, Xuzhou mining area, located in Northern Jiangsu Province, is taken as the studying area, and Terrestrial Surface Evolution (TSE) as the studying task. According to the specific condition of studying area, the information characteristics of each band of TM image and relations between different bands are analyzed by selecting different sampling area, and relative rules are given. After that, band combination is discussed and the information content is used as the judging rule. Because more bands will require more computer resource and is low speed and cost consuming, three-band combination is used widely. It is found that in all three-band combination schemes, the combination of Band 3, Band 4 and Band 5 is the most effective. Finally, Genetic Algorithm (GA) is used to the band selection in multi-band RS image, and it proved that GA is an effective method to determine the optimal band combination, especially for multi-spectrum and super-spectrum RS information source, and GA is also a good optimized algorithm in Geoscience.

The local accumulative histogram gives us the statistic characteristics about the image more flexibly and precisely than the whole histogram of the image. We use the intrinsic structure of the edge region alternated with steep rising and stable rising to detect the edge information, which is the key feature in the local accumulative histogram. Then we refine the edge image to one-pixel wide edge image by the threshold obtained from the local accumulative histogram. The experimental results show that not only the algorithm can give us a good edge detecting result, but also it has the ability of antinoise. And some definitions of the one-pixel wide edge are given out in accordance with the people's intuition more directly and simply than other definitions before.

In this paper, we propose a multi-scale image segmentation algorithm based on mean shift, a simple iterative procedure that shifts each data point to the average of data points in its neighborhood, which has been proven to be a mode-seeking process on a surface constructed with a "shadow" kernel. In the presented algorithm, not only the color features, but also the space relationship of each pixel are considered in multiple scales, thus getting a more reasonable clustering sequence, furthermore, center candidates are validated by contour map. Experimental examples are illustrated and compared to show that the approach is effective not only in segmentation, but also in denoising.

Quality assurance is the key for increasing competition in the market place. This paper presents a new machine vision based approach for the detection of defects using real Gabor functions. A bank of real Gabor functions, followed by a nonlinear function, is used to sample texture features at different scales. These texture features are compared with those from defect-free (reference) image, and a set of feature difference arrays are created. These are used to generate a combined image output using image fusion. This combined image output is used to obtain a binary image of defects using calibration. This paper also details a new method for automated selection of the center frequency of Gabor function using spectral analysis. Experimental results have confirmed the usefulness of the proposed approach for the automated inspection of textile webs.

There are different techniques available for solving of the restoration problem including Fourier domain techniques, regularization methods, recursive and iterative filters to name a few. But without knowing at least approximate parameters of the blur, these methods often show poor results. If incorrect blur model is chosen then the image will be rather distorted much more than restored. The original solution of the blur and blur parameters identification problem is presented in this paper. A neural network based on multi-valued neurons is used for the blur and blur parameters identification. It is shown that it is possible to identify the type of the distorting operator by using simple single-layered neural network. Four types of blur operators are considered: defocus, rectangular, motion, and Gaussian ones. The parameters of the corresponding operator are identified by using a similar neural network. After identification of the blur type and its parameters the image can be restored using different methods. Some fundamentals of image restoration techniques are also considered.

In this paper, we present a multi-scale internal energy model for the balloons, which are closed snakes driven by pressure forces. When used to extract fuzzy contours from noisy images, a balloon sometimes goes through gaps on the contour, thus not able to reach equilibrium. We argue that a balloon should be discretized with a scale comparable to sizes of gaps for the internal energy to be effective in performing contour completion. However, using too large a scale will also prevent a balloon from catching necessary details. To solve this problem, we propose a multi-scale internal energy model that has the ability to maintain smoothness at various scales, thus able to perform contour completion for gaps with various sizes without missing any details.

One of the most popular level set algorithms is the so-called fast marching method. In this paper, a medical image segmentation method is proposed based on the combination of fast marching method and watershed transformation. First, the original image is smoothed using nonlinear diffusion filter, then the smoothed image is over-segmented by the watershed algorithm. Last, the image is segmented automatically using the modified fast marching method. Due to introducing over-segmentation, the arrival time the seeded point to the boundary of region should be calculated. For other pixels inside the region of the seeded point, the arrival time is not calculated because of the region homogeneity. So the algorithm's speed improves greatly. Moreover, the speed function is defmed again based on the statistical similarity degree of the nearby regions. Experiments show that the algorithm can fast and accurately obtain segmentation results ofmedical images.

Based on radar remote sensing and sediment facies analyzing, this paper studies the features of environmental evolution of North-eastern Ejin Banner since 60 Ka BP(Before Present). The conclusions are listed as follows: (1) The evolution of Gaxunnur Lake, Sugunur Lake, Tiane Lake is dominated by faults and regional climate; (2) By analyzing deposit of old Juyanze Lake, it is a large outlet lake about 5OKa BP. Just about 5OKa BP, there was a rapid decline of temperature in the Northwestern of China. The events caused those lakes shrinkage. (3) By fault activity arising uplift in the north of old Juyan Lake and depression in the south, the lake's water flowed out from north to south at around 35Ka BP, which is reversed to the former flow direction. So, old Juyanze fluvial fan was formed. At the same time, Juyan Lake separated from Sugunur lake and Wentugunr old channel was abandoned. (4) Near 2000 years , Ruoshui River is a wandering river. Due to intense influence of human activities, the oasis ecosystem is rapidly degenerated in recently 15 years.

In the close digital photogrammetric three-dimension coordinates measurement, the circular target is often taken as imaging feature and mounted on the measured object or the probe for 3D coordinates detection. The accuracy with which circular targets are located determines the effectiveness of measurement. Subpixel level accuracy is one of the methods that can improve the accuracy of target location. Many methods which based on subpixel edge or centroid detection have been developed and analyzed for target location, but little research focused on circular optical target location. In this research, a new algorithm named bilinear interpolation centroid algorithm was developed for circular optical target subpixel location. In this technique, the accuracy of the squared gray weighted centroid algorithm can be improved by increasing the available pixels which obtained by bilinear interpolation. The intensity profile of the imaging points and the signal to noise ratio, which affect the subpixel location accuracy, are optimized by automatic exposure control. The experiments have shown that the accuracy of this algorithm is better than the traditional centroid algorithm, and the absolute error of less than 0.0 1 pixels is obtained on the image of a rigid reference bar.

s+P transform proposed by Amir Said[7] is a multiresolution representation method. It can map integers to integers, so it has been used in lossless compression of images with better performance than that of JPEG standard based on linear prediction. To further improve compression performance, this paper presents an efficient lossless compression algorithm of images based on DPCM and S+P transform. Firstly, an error image is obtained from an original image with linear prediction; Secondly, the error image is transformed with S+P transform; Finally, the transformed coefficients will be compressed effectively with entropy coding. We list our software simulation results, which are compared with those of other known algorithms7111011121 for multiresolution-based lossless compression. The comparison shows that our new method is effective and efficient, and produces the best results.

The application of image morphing to computer animation and computer graphics is experiencing broad growth. It has proven to be a powerful visual effects tool in film and television, depicting the fluid transformation of one digital image into another. In this paper, the authors present a new method for image morphing based on field morphing and mesh warping. Our method makes use of the feature specification method of field morphing which is simple and expressive and the warp generation approach of mesh warping which is straightforward and fast. Some measures are taken to make the proposed method work; experimental results show that the proposed method facilitates the input of features and calculates quickly with a steady metamorphosis effect.

We present a joint segmentation scheme for the stained renal tubular image in this paper. The scheme, which combines several distinct image processing methods and uses a special criteria to select appropriate segmented areas based on regional properties, makes the best use of different segmentation methods. Experimental results are given and some further improvements are discussed at the end of this paper.

In this paper a framework of a wireless spread spectrum video system has been proposed. Because the energy supply in the wireless systems is limited, the power consumption of the systems must be reduced. Consequently the design principles for low-power wireless video systems are introduced at first. Wireless video systems have many requirements on encoding/decoding. And MPEG-4 standard is just suitable for the systems by reason of its high compression efficiency and outstanding performance. Thus the video information can be transmitted at a very low bit rate over the wireless channels. Given the advantages of the spread spectrum system, we also introduced spread spectrum into the wireless video systems and gave a framework of wireless spread spectrum video system.

This paper presents a private and lossless digital image watermarking system. First, to ensure the security of the watermark, the watermark image is scrambled before embedding. And then, Human Visual System (HVS) is employed to further enhance the transparence and the robustness of the watermarking system. Thirdly, the watermark image is compressed as watermarking information and is embedded in the DCT domain of the original image. Finally, We save the decimal fractions of the image-signed as a data file as a key during embedding and the data file can avoid the decimal fractions data loss caused by saving the image-signed. The experimental results show that it can embed the image as the watermark with strong robustness.

in the paper a theoretical analysis ofoptimal cubic filter for image scaling is investigated. Starting from the analysis of the cubic filter in the frequency domain, and then by minimizing the magnitude of the frequency response, |H(?)|, over the interval 0.5 ??? 1.0, the best choice of the coefficients for the cubic filter can be obtained in terms of aliasing suppression and clear text display. By incorporating the previous result obtained by Keys with the new result obtained here, the optimal coefficients for the cubic filter can be obtained. Since the optimal cubic filter is obtained by suppressing the frequency response beyond the Nyquist frequency, it can be expected that the aliasing can be suppressed by using the proposed optimal cubic filter. The proposed optimal filter has performed a text enlargement example, and simulation results justify our theoretical analysis. it shows that the proposed method has very good performance in image scaling, especially in performing text enhancement.

The purpose ofthis note is to present a novel techniquefor boundary determination that incorporates both a shape prior and a local intensity profile into a geometric active contour model. The basic idea of this model is to minimize an energyfunctional, which incorporates the information ofthe image gradient, the shape ofinterest, and the intensity profile across the shape into the search for the boundary of an object. The level set form of the proposed model is also provided. Experimental results on ultrasound images are presented, which indicate that this model provides close agreement with expert traced borders. These results indicate the effectiveness ofthe incorporation ofthe local intensity profile in the model.

Five robot vision algorithms for object segmentation are explained. This algorithms are compared in an experiment. The aim of the experiment was to check which of this five algorithms are useful for our robotics project in the field of indoor room exploration. In this project a robot shall autonomously generate a map from our office which shall be the foundation to fulfill tasks like an office messenger. Two evaluation criterions were used to get hints with regard to the eligibility if inhomogeneous illumination must be handled. Although the number of researchers, which are working on the field of video based exploration with autonomous robots, is increasing, it don't exist sufficient works at this juncture which give hints how an appropriate evaluation ofrobot vision programs in autonomous robot systems can happen. This work wants to give contribution to this gap.

This paper discusses about ergodic matrices and related application about scrambling and encryption of digital images. First, we use ergodic matrices to realize the position permutation algorithms. In particular several novel methods of scrambling are proposed. Subsequently we analyze the isomorphism relationship between ergodic matrices and permutation group. By defining permutation symbol operation system of ergodic matrices, we construct a union form to express all existed permutation algorithms. Finally the images are encrypted using them. Preliminary results are satisfactory.