This PDF file contains the front matter associated with SPIE Proceedings Volume 8878, including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Corner detection has been shown to be very useful in many computer vision applications. Some valid approaches have been proposed, but few of them are accurate, efficient and suitable for complex applications (such as DSP). In this paper, a corner detector using invariant analysis is proposed. The new detector assumes an ideal corner of a gray level image should have a good corner structure which has an annulus mask. An invariant function was put forward, and the value of which for the ideal corner is a constant value. Then, we could verify the candidate corners by compare their invariant function value with the constant value. Experiments have shown that the new corner detector is accurate and efficient and could be used in some complex applications because of its simple calculation.

We introduced and compared ten denoisingfilters which are all proposed during last fifteen years. Especially, the state-of-art denoisingalgorithms, NLM and BM3D, have attracted much attention. Several expansions are proposed to improve the noise reduction based on these two algorithms. On the other hand, optimal dictionaries, sparse representations and appropriate shapes of the transform’s support are also considered for the image denoising. The comparison among variousfiltersis implemented by measuring the SNR of a phantom image and denoising effectiveness of a clinical image. The computational time is finally evaluated.

Recent studies show that wavelet-based image enhancement methods provide a high quality in enhanced images. However, images enhanced by most wavelet-based methods have less spatial resolution because the critical down- sampling is included in wavelet transform. In this paper, we propose a useful image enhancement scheme based on nonsubsampled contourlet transform. Because edges and texture are fundamental in image representation, enhancing them is an effective means of enhancing spatial resolution. Experimental results show that the proposed enhancement scheme is able to enhance the detail and increase the contrast of the enhanced image at the same time.

The distribution of image data points forms its geometrical structure. This structure characterizes the local variation, and provides valuable heuristics to the regularization of image restoration process. However, most of the existing approaches to sparse coding fail to consider this character of the image. In this paper, we address the deblurring problem of image restoration. We analyze the distribution of the input data points. Inspired by the theory of manifold learning algorithm, we build a k-NN graph to character the geometrical structure of the data, so the local manifold structure of the data can be explicitly taken into account. To enforce the invariance constraint, we introduce a patch-similarity based term into the cost function which penalizes the nonlocal invariance of the image. Experimental results have shown the effectiveness of the proposed scheme.

In this paper, proposed an image restoration method which base on the sparse constraint. Based on the principle of Compressed Sensing, the observed image is transformed into the wavelet domain, and then converted the image restoration problem to a convex set unrestricted optimization problem by limiting the number of non-zero elements of the wavelet domain, using the gradient projection method for solving the optimization problem to achieve the restoration of the input image. Experiments show that the method presented has the fast convergence and good robustness compared to the traditional total variation regularization restoration method.

In this paper, a modified adaptive nonlocal means (ANLM) filter is investigated for image denoising by introducing the image gradient into the classical nonlocal means filter. The proposed algorithm takes the orientation of matching neighborhood into consideration and can adaptively select the filtering parameter based on image gradient. Moreover, the symmetry or approximate symmetry of some filtered images is also considered. Therefore, comparing with the classical nonlocal means filter, the new method can exploit much more similar pixels. The proposed approach is applied to several real images corrupted by white Gaussian noise with different standard deviation. The comparative experimental results show that the improved ANLM filter obtains superior denoising performance.

The image preprocessing plays an important role in finger vein recognition system. However, previous preprocessing schemes remind weakness to be resolved for the high finger vein recongtion performance. In this paper, we propose a new finger vein preprocessing that includes finger region localization, alignment, finger vein ROI segmentation and enhancement. The experimental results show that the proposed scheme is capable of enhancing the quality of finger vein image effectively and reliably.

This paper proposes a novel idea for ionogram trace enhancement to obtain the “clean” ionogram with real ionospheric echo signals, which is very important for further ionogram interpretation and scaling manually or automatically. Two methods based on ionogram trace pixel connectedness are adopted: max filter and connected components labeling. The experiments show that both methods are feasible and effective, and parameter selection and time complexity of the two methods are analyzed.

Recent research has made significant progress in single image dehazing by using dark channel prior. We can directly estimate the thickness of the haze and recover a high quality haze-free image by using it. However, such method is inefficient when processing high resolution or high-bit-wide images because of its high computational complexity. Besides, the results are inaccurate when there are large white objects in the scene. A novel image prior is proposed in this paper to solve the above drawbacks. We develop a powerful and speed-up single image dehazing method by replacing the single dark channel with double dark channels with different scales to estimate the global atmospheric light and the transmission. Thus we can separate the method into two parts and ignore the soft matting that occupies 95% computation cost of the previous method. The experimental results show that our method is much faster than the original method and reduces the distortion caused by large white objects in the scene at the same time. Compared with previous method, our new single image dehazing method achieves the same, even better image quality with only around 1/23 computation time and saves lots of memory space.

With constraints to the performance of the IR detector, IR image usually has lower visual effect with low contrast and less detailed information. In this paper, a new dynamic range infrared image details enhancement algorithm is studied, using a bilateral filter to extract a base component and a detail component. Then these two components are compressed to fit the display dynamic range and then recombined to obtain the output-enhancement image. This algorithm has solved the problem of ripple phenomenon which exists in the traditional infrared image digital detail enhancement. Finally, the algorithm described in this paper is proved experimentally that can provide better DDE effect.

The paper presents an improved method for color image enhancement based on dynamic range compression and shadow compensation method proposed by Felix Albu et al. The improved method consists of not only the dynamic range compression and shadow compensation but also intensity contrast stretching implemented under the logarithmic image processing (LIP) model. The previous method enhance image while preserve image details and color information without generating visual artifacts. On the premise of remaining the advantages of the previous one, our improved method enhances the intensity of the whole image especially the low-light areas. The experimental results illustrate the effectiveness of our proposed method and the superiority over the previous one.

Torpedo fuze signal denoising is an important action to ensure reliable operation of fuze. Based on the good characteristics of wavelet packet transform (WPT) in signal denoising, the paper used wavelet packet transform to denoise the fuze signal under a complex background interference, and a simulation of the denoising results with Matlab is performed. Simulation result shows that the WPT denoising method can effectively eliminate background noise exist in torpedo fuze target signal with higher precision and less distortion, leading to advance the reliability of torpedo fuze operation.

De-noise speech signal if it is noisy. Construct a wavelet according to Daubechies’ method, and derive a wavelet packet from the constructed scaling and wavelet functions. Decompose the noisy speech signal by wavelet packet. Develop algorithms to detect beginning and ending point of speech. Construct polynomial function for local thresholding. Apply different strategies to de-noise and compress the decomposed terminal nodes coefficients. Reconstruct the wavelet packet tree. Re-build audio file using reconstructed data and compare the effectiveness of different strategies.

In many cases, the image acquisition devices have a limited dynamic range, which is lower than one encounter in the real world. The capture image from the camera can not reflect the high dynamic range. According to different exposure time of images in the same scene, it apply the Laplace sharpening the images and enhancing their details, and it use wavelet transform based on weighted rule of fusion to fuse those images. Consequently, the fused image retains effective information from those different exposure images. The resolution of the fusion image is better, and those images of the bright and dark area have been enhanced in the fusion image. Experiment show that the method for wavelet image fusion can fuse those multiple exposure images into a high dynamic range image.

As the demand for 3DTV keep increasing these years, the conversion from exist 2D videos to 3D ones becomes a new area of research. Depth map generation plays a key point in the process. Two most important clues of depth are geometry of the scene and motion vector. This paper presents an algorithm of depth map generation, which intends to get the depth map combines two aspects of information. Compared to the previous work, our method is improved in finding vanishing point, detect motion vectors, and depth map generation.

We propose an effective method, flexible log-polar image (FLPI) to represent quantum images sampled in log-polar coordinate system. Each pixel is represented by three qubit sequences and the whole image is stored into a normalized quantum superposition state. If needed, a flexible qubit sequence can be added to represent multiple images. Through elementary operations, both arbitrary rotation transformation and similarity evaluation can be realized. We also design an image registration algorithm to recognize the angular difference between two images if one is rotated from the other. It is proven that the proposed algorithm could get conspicuous improvement in performance.

Saliency is an important feature of human visual attention. Salient regions of an image immediately attract our attention. Therefore, attention to salient regions is an important attribute to measure image qualities. A novel image quality metric is proposed in this paper, in which salient regions are extracted and the use of FSIM (Feature SIMilarity) in these regions is analyzed for image quality assessment. Experimental results for a set of intuitive examples with different distortion types demonstrate that the improved FSIM can achieve a better performance than the original form.

After analyzing the weakness of traditional video codec methods in processing real time compression of high-speed camera images. This paper designs a video codec method based on online compressed sensing (CS). The coding part uses pseudo-random down-sampling of the two-dimensional fast Fourier transform (2D FFT) to process video frames. Meanwhile, combine approximate message passing (AMP) algorithms and three-dimensional dual-tree wavelet transform (3D DTWT) for offline decoding. Experimental results show that, this method could achieve high signal to noise ratio while simplified the coding process.

The spatial position of convergent point of dual-view stereo camera is a key parameter. To solve the problem that lack of simple and effective convergent point positioning method at present, we present two methods of convergent point positioning. The first method for convergent point positioning is by observing the difference between the corresponding points of principal points in left and right images. The second method is by computing the relative extrinsic parameters between right and left cameras. The experimental results show that the first method is convenient for the stereo camera which consists of adjustable left and right cameras; the second method is convenient for the stereo camera which consists of stable left and right cameras. Both of the methods are available for convergent point positioning.

Color is often used to simplify object exaction and identification for many color-based machine vision systems. However, image colors produced by color vision system strongly depend on lighting geometry, illumination color and the spectral response function of digital camera. Either small variation in the illumination or the changes of digital camera can dramatically make the image color changed. In this paper, color correction is performed for our color vision measurement system. The mapping coefficient matrix is obtained by polynomial regression model under artificial D65 illumination and LED array illumination. The detailed correction accuracies are compared between the two common used device-independent color space (sRGB color space and CIEL*a*b*color space). sRGB color space is recommended due to the higher accuracy and simple algorithm. The correction images illustrate the usefulness of our method for color correction

While the rapid development in optical and storage technique enable us to easily acquire large amount of microscopy images, it is still a great challenge for biological researchers to analyze these huge data and draw meaningful and statistically sound conclusions. In this paper, we take single particle point spread function fitting problem into consideration and implement fast algorithm on the CPU+GPU heterogeneous architecture. Our approach is tested on real-world dataset and achieve about 23 ~40x faster than the traditional fitting algorithm.

Terrestrial laser scanning creates a point cloud composed of thousands or millions of 3D points. Through pre-processing, generating TINs, mapping texture, a 3D model of a real object is obtained. When the object is too large, the object is separated into some parts. This paper mainly focuses on problem of gray uneven of two adjacent textures’ intersection. The new algorithm is presented in the paper, which is per-pixel linear interpolation along loop line buffer .The experiment data derives from point cloud of stone lion which is situated in front of west gate of Henan Polytechnic University. The model flow is composed of three parts. First, the large object is separated into two parts, and then each part is modeled, finally the whole 3D model of the stone lion is composed of two part models. When the two part models are combined, there is an obvious fissure line in the overlapping section of two adjacent textures for the two models. Some researchers decrease brightness value of all pixels for two adjacent textures by some algorithms. However, some algorithms are effect and the fissure line still exists. Gray uneven of two adjacent textures is dealt by the algorithm in the paper. The fissure line in overlapping section textures is eliminated. The gray transition in overlapping section become more smoothly.

The original dynamic range of scene is generally limited to the capture sensors and display devices, and showed with the low dynamic range.Therefore, it is difficult to display the details in both dark and bright areas simultaneously. This paperadopted flexible thresholds combined with luminance map to improve the quality of image captured with unideal light distribution, and based on simple computation. The implementation effectively adjusts image contrast to both lowlight and highlight details while avoiding common quality loss due to halo-artifacts, desaturation and greyish appearance.

The great advantage of Microsoft Kinect makes the depth acquisition real-time and inexpensive. But the depth maps directly obtained with the Microsoft Kinect device have absent regions and holes caused by optical factors. The noisy depth maps affect lots of complex tasks in computer vision. In order to improve the quality of the depth maps, this paper presents an efficient image inpainting strategy which is based on watershed segmentation and region merging framework of the corresponding color images. The primitive regions produced by watershed transform are merged into lager regions according to color similarity and edge among regions. Finally, mean filter operator to the adjacent pixels is used to fill up missing depth values and deblocking filter is applied for smoothing depth maps.

With the progress of the age, the popularization of the computer and the internet, the text, images, photographs and varieties of multimedia will be uploaded to groups of the network space or cloud storage space by users. Thus, the multimedia data and technology have to renew and transfer by user constantly. How to search the images economically is a significant issue. This paper will focus on 3D images for in-depth investigate. It will propose an efficient 3D searching method. The analytical object is used by three-dimensional trademark gallery of the Intellectual Property Office of the Ministry of Economic Affairs, R.O.C. One three-dimensional trademark image expresses by a set of 2D images. This paper uses Harris Corner detection and combines CPDH (contour points distribution histogram) method to extract the shape feature and uses color histogram to refine the color feature. And then, the two features help to retrieve the similar 3D images. Experiment verifies that the method we proposed is effective.

The pulse description word (PDW) encoder of digital channelized reconnaissance receiver was investigated. The encoding flow of digital channelized reconnaissance receiver was introduced. The measurement methods of the pulse width of wideband frequency modulated signal crossing channels and the direction of arrival (DOA) were discussed in detail. It has been applied to reconnaissance receiver in electronic support measure (ESM) system successfully.

The WiFi network is one of the most rapidly developing wireless communication networks, which makes wireless office and wireless life possible and greatly expands the application form and scope of the internet. At the same time, the WiFi network security has received wide attention, and this is also the key factor of WiFi network development. This paper makes a systematic introduction to the WiFi network and WiFi network security problems, and the WiFi network security technology are reviewed and compared. In order to solve the security problems in WiFi network, this paper presents a new WiFi network security model and the key exchange algorithm. Experiments are performed to test the performance of the model, the results show that the new security model can withstand external network attack and ensure stable and safe operation of WiFi network.

Reversible watermarking algorithms allow extraction of the hidden information to ensure authenticity and restore original medium. Prediction-error expansion (PEE) methods give high payload with little or no visible distortion. In these methods performance of the algorithm relies heavily on predictor`s response. In this study image pixels are predicted using 8 pixels within a 3×3 neighborhood of a pixel. Image pixels are divided into two sets and information bits in each set are embedded using PEE and histogram shifting (HS). Better prediction in conjunction with HS increases imperceptibility of the watermarked image. Experimental results indicate superior performance of the proposed scheme in comparison to recent methods published in the literature.

Through the in-depth study on the existing mobile e-commerce and WAP protocols, this paper presents a security solution of e-commerce system based on WPKI, and describes its implementation process and specific implementation details. This solution uniformly distributes the key used by the various participating entities , to fully ensure the confidentiality, authentication, fairness and integrity of mobile e-commerce payments, therefore has some pract ical value for improving the security of e-commerce system.

Existing privacy-preserving publishing models can not meet the requirement of periodical publishing for medical information whether these models are static or dynamic. This paper presents a (k,l)-anonymity model with keeping individual association and a principle based on (Epsilon)-invariance group for subsequent periodical publishing, and then, the PKIA and PSIGI algorithms are designed for them. The proposed methods can reserve more individual association with privacy-preserving and have better publishing quality. Experiments confirm our theoretical results and its practicability.

At present, XML language has been regarded as a standard of data exchange due to such attributes as being unrelevant to platform, auto-description of itself, easy extention, separation of content and form, and so on. Therefore, in the course of study of share of heterogeneous-data in coal enterprises, we design a heterogeneous-data query system based on XML to provide a unified data integration platform and share of information resource with high quality and speed, which will solve a problem with information islands produced in coal enterprises.

Aiming at characteristics of rigorous identity authentication for both two trading parties in network transactions and based on analysis and study on Hyper Elliptic Curve (HEC) and ElGamal based on discrete logarithm problem, these two approaches were combined organically to design a digital signature system based on HEC-ElGamal. It was applied in the identity authentication to provide bipolar signature verification. The security performance of this approach was also analyzed. This system of digital signature inherits security technology of two systems, so that it can reach high security index. It provides guarantee for integrity check and identity authentication.

Evaluations of both academic face recognition algorithms and commercial systems have shown that the recognition performance degrades significantly due to the variation of illumination. Previous methods for illumination robust face recognition usually involve computationally expensive 3D model transformations or optimization base reconstruction using multiple gallery face images, making them infeasible in practical large scale face identification applications. In this paper, we propose an alternative face identification framework, in which one image per person is used for enrollment as is commonly practiced in real life applications. Several probe images captured under different illumination conditions are synthesized to imitate the illumination condition of the enrolled gallery face image. We assume Lambertian reflectance of human faces and use the harmonic representations of lighting. We demonstrate satisfactory performance on the Yale B database, both visually and quantitatively. The proposed method is of very low complexity when linear facial feature are used, and is therefore scalable for large scale applications.

Consistency by means of image fractal dimension of the surface fractal dimension is designed and implemented based on fractal theory of image quality assessment method. Classic SSIM algorithm based on research and analysis of the factors affecting the image quality, the quality factor of the fractal, built for the blurred image quality evaluation method. Experiments show that the method of subjective and objective evaluation of the relevance, scientific evaluation of fuzzy image quality.

We present an improved approach to another previous work on 3D hand tracking that also uses the Microsoft Kinect sensor. The previous implementation tracks the position, orientation, and full articulation from marker-less visual observations provided by Kinect. As an optimization problem, the objective of hand tracking is to minimize the difference between a hand gesture depth image obtained from Kinect and a hypothesized 3-D hand model. The previous method of relied heavily on the best current frame result, skin detection data, and depth data, often resulting in a "losttrack state" with unrecoverable error, especially when the hand moved faster than the per-frame processing speed. To recover from the lost track state, we use the skeleton joint data from Kinect to determine hand position, instead of relying on skin data. This joint data is also used to limit the search range of our Particle Swarm Optimization (PSO), allowing for a more efficient search. Consequently, the fewer generations required to obtain a result enables us to achieve higher frame-rate processing. The computationally intensive step in matching the observed hand depth with the hypothesized hand pose is accelerated using a GPGPU processor. The proposed method also improves reliability by adding a recovery mechanism for quick hand movements, eliminating the need for manual hand position initialization by a user. Our method does not depend on skin color detection and, therefore, avoids errors common in incorrect or extra skin detection. Thus, a user need not hide arm skin by wearing long-sleeve clothing, for example.

In our daily lives we often assess our surroundings to classify the situations we encounter. We do so based on the observations we make of our surroundings and information we obtain from other sources, using our knowledge and abilities. While this process is natural to us, if we want to give a similar task to a computer system then we have to take various steps in order to enable our computers to partially emulate the human capacity for observation, learning and making final decisions based on knowledge. As information complexity increases, there is an increasing demand for systems which can recognize and classify the objects presented to them. Recently there has been an increase in interest in application of computer image analysis in various research areas. One of these applications is food quality assessment, which aims to replace traditional instrumental methods. A computer visual system was developed to assess carrot quality, based on a single variety. Characteristic qualities of the variety were chosen to describe a suitable root. In the course of the study, digital photographs of carrot roots were taken, which were used as input data for the assessment performed by a dedicated computer program created as a part of the study.

The spatiogram features have been widely used in computer vision. In this paper, in order to improve the performance of image retrieval method based on spatiogram features, we propose a new method to measure the spatiogram similarity in the framework of extended Gaussian Lie group model. In our method, the spatiogram features are extracted in the HSV space. The similarity between images described by spatiogram features depends on the distances between Gaussian probability density functions which can be calculated using Lie group theory. Based on the framework of the extended Gaussian matrix Lie group, the contribution of the covariance matrix and the mean vector is adjusted automatically, which ensures both the covariance matrix and the mean vector will not be ignored when calculating the image similarity in the process of retrieval. We test our algorithm on the WANG Image Database. Experiments show that the proposed method has a better performance than the method based on the traditional Gaussian Lie group.

Classing object is an important step for the high-level visions processing tasks, such as security managing, and abnormality event analysis etc. In this paper, we address these challenges in real-world unconstrained environments where the background is complex and dynamic. In the algorithm proposed, we extract the features in a color space technique is also developed to monitor the abnormal surface of water based on Mutation Particle Swarm Optimization (MPSO). Our know, MPSO is one of the important evolutionary algorithms that it not only makes use of a mutation operator to update particles/individuals which was originally designed for Genetic Algorithm (GA), but also a weighted update rule can produce the new swarm for MPSO. Experimental results show that our algorithm works efficiently and robustly.

In the literature of neurophysiology and computer vision, global and local features have both been demonstrated to be complementary for robust face recognition and verification. In this paper, we propose an approach for face verification by fusing global and local discriminative features. In this method, global features are extracted from whole face images by Fourier transform and local features are extracted from ten different component patches by a new image representation method named Histogram of Local Phase Quantization Ordinal Measures (HOLPQOM). Experimental results on the Labeled Face in Wild (LFW) benchmark show the robustness of the proposed local descriptor, compared with other often-used descriptors.

In order to improve the accuracy of global motion estimation (GME), a new method for GME combining with motion segmentation is proposed in this paper. The proposed method removes motion vector (MV) outliers and implements initial motion segmentation by analyzing properties of motion vectors. Using the filtered MV field, global motion parameters were estimated, and then the difference frame was generated by global motion compensation（GMC ）. According to the movement difference between the background and the foreground regions and movement consistency in the same region, the absolute sum of difference frame in every block was calculated, and thus adaptively generating the threshold value to detect motion regions. MVs in the motion regions were rejected as outliers for GME, and iterative computations between GME and motion segmentation were performed successively. Experimental results demonstrate that the proposed approach can effectively extract motion regions, thus enhancing the accuracy of GME.

How to implement the efficient query is the key problem deployed on P2P networks. This paper analyses the shortage of several query algorithm, and presents a new algorithm DDI, which means distributed searching with double indices. It discusses the popularity of documents and the linking status of the networks, and calculates the availability of the nodes in whole network, determines the route of the query process. It compares the items of time using, the quantity of requests and update information by the emulate experiments.

Along with the rapid development of computer network technology, peer-to-peer (referred to as P2P) network research has gradually become mature, and it is widely used in different fields, some large P2P computing project has entered the implementation stage. At present, many more popular software systems such as Gnutella, Freenet, Napster are deployed based on P2P technology. How to achieve effective information query has become one of the key problems of P2P research.

Researchers put forward many knowledge representation methods according to specific problems for different domain. These methods according to their respective domain problem has different characteristic, each has its own strong points, suitable for different questions. This paper is research on a knowledge representation method based on extenics. Through the analysis of organizational knowledge system, the paper puts forward the related theory of knowledge set, and with the extenics theory gives the extension representation and the correlation analysis of knowledge set.

This paper presents an economical extensible model and a new load balancing strategy based on network load capacity, so that the integrity of evidence in high-speed network forensics can be guaranteed. This algorithm gets the capture ability of evidence capture by dynamic feedback and forecast mechanism. Taking one session as a distribution unit, it distributes the network, it distributes the network packets to the host with the maximal-load capacity. The result shows the stretchability of this system can satisfy the current huge network-flow.

The paper researched the wetland vegetation dynamics in the Yellow River Delta by the remote sensing and geographic information system technology. The study results showed that the land area of the closed regions with sea dykes was basically stable since 1995, and which of the open regions showed the diminishing trend from 1984 to 2006. The change trend of the area of the saline vegetation had increased from 1984 to 2006 and the change rate was 7.18. The change analysis of dynamic degree of wetland vegetation landscape revealed that the succession and conversion within the wetland vegetation landscapes was also one main process. The conversion rate of farmland was more than 12% in the two periods of 1984-1995 and 1995-2006, the human farmland reclamation activities in the coastal region had been on the rise.

Taking advantage of the fusion technology of things this paper constructed a combination of hardware and software application, hardware’s major function was to collect the bus behavior data of system needed, including basic data of driver and fare bag stored in moving passive RFID tag, and the information of running status of bus on each stage perceived by all kinds of sensors in the parking area. The information which was handled by the middleware was sent to data center. The program solved the problem on the monitoring of the behavior of the bus in the parking area, meanwhile, achieved the data sharing, so as to tackle the defects of the traditional bus parking area management system’s non-automated data collection, non-real-time data presenting and poor data sharing.

In contrast with single HLA federation framework, hierarchical federation framework can improve the performance of large-scale simulation system in a certain degree by distributing load on several RTI. However, in hierarchical federation framework, RTI is still the center of message exchange of federation, and it is still the bottleneck of performance of federation, the data explosion in a large-scale HLA federation may cause overload on RTI, It may suffer HLA federation performance reduction or even fatal error. Towards this problem, this paper proposes a load balancing method for hierarchical federation simulation system based on queuing theory, which is comprised of three main module: queue length predicting, load controlling policy, and controller. The method promotes the usage of resources of federate nodes, and improves the performance of HLA simulation system with balancing load on RTIG and federates. Finally, the experiment results are presented to demonstrate the efficient control of the method.

In the present paper, we propose a novel method for face recognition against contiguous occlusion. The general idea is to eliminate the impact of occlusions on the linear regression-based classification (LRC) method. Inspired by the level set methods that can provide smooth and closed contours as segmentation results which fit for the assumption of spatially continuity about occlusion, we show how to use the spatial continuity of pixels to segment the occluded regions. By incorporating the idea of level set based image segmentation into the LRC, the proposed approach is capable of reliably determining the occluded regions and removing them from LRC framework. Extensive experiments on publicly available databases (Extended Yale B and AR) show the efficacy of the proposed approach against different types of occlusion.

Mobile reading is the trend of current publishing industry. Intelligent Recommendation system is useful and profitable for mobile reading platforms. Currently, intelligent recommendation systems mainly focus on news recommendation or production recommendation in e-commerce. In this paper, we designed and implemented an intelligent recommendation system based on slope one algorithm. Results show that our algorithm can help the users to find their interested books and thus greatly improve the income of mobile reading platform.

Agricultural products typically exhibit high variance in quality characteristics. To assure customer satisfaction and control manufacturing productivity, quality classification is necessary to screen off defective items and to grade the products. This article presents an application of image processing techniques on squid grading and defect discrimination. A preliminary study indicated that surface color was an efficient determinant to justify quality of splendid squids. In this study, a computer vision system (CVS) was developed to examine the characteristics of splendid squids. Using image processing techniques, squids could be classified into three different quality grades as in accordance with an industry standard. The developed system first sifted through squid images to reject ones with black marks. Qualified squids were graded on a proportion of white, pink, and red regions appearing on their bodies by using fuzzy logic. The system was evaluated on 100 images of squids at different quality levels. It was found that accuracy obtained by the proposed technique was 95% compared with sensory evaluation of an expert.

To improve the operation level of copper converter, the approach to optimal decision making modeling for coppermatte converting process based on data mining is studied: in view of the characteristics of the process data, such as containing noise, small sample size and so on, a new robust improved ANN (artificial neural network) modeling method is proposed; taking into account the application purpose of decision making model, three new evaluation indexes named support, confidence and relative confidence are proposed; using real production data and the methods mentioned above, optimal decision making model for blowing time of S1 period (the 1st slag producing period) are developed. Simulation results show that this model can significantly improve the converting quality of S1 period, increase the optimal probability from about 70% to about 85%.

Association rule mining is an essential knowledge discovery method that can find associations in database. Previous studies on association rule mining focus on finding quantitative association rules from certain data, or finding Boolean association rules from uncertain data. Unfortunately, due to instrument errors, imprecise of sensor monitoring systems and so on, real-world data tend to be quantitative data with inherent uncertainty. In our paper, we study the discovery of association rules from probabilistic database with quantitative attributes. Once we convert quantitative attributes into fuzzy sets, we get a probabilistic database with fuzzy sets in the database. This is theoretical challenging, since we need to give appropriate interest measures to define support and confidence degree of fuzzy events with probability. We propose a Shannon-like Entropy to measure the information of such event. After that, an algorithm is proposed to find fuzzy association rules from probabilistic database. Finally, an illustrated example is given to demonstrate the procedure of the algorithm.

A continuous time model is proposed in this paper, dealing with the decentralized decision-making of multi-products promotion, under the condition of moral hazard. The main result is that when the revenue function and the movement law satisfy certain conditions, the optimal efforts level of the division in continuous time is the same with that in the static case, even though both are distorted compared with the optimal efforts level under complete information.

With the development of remote sensing technology, getting the image data more and more, how to apply and manage the mass image data safely and efficiently has become an urgent problem to be solved. According to the methods and characteristics of the mass remote sensing image data management and application, this paper puts forward to a new method that takes Oracle Call Interface and Oracle InterMedia to store the image data, and then takes this component to realize the system function modules. Finally, it successfully takes the VC and Oracle InterMedia component to realize the image data storage and management.

This paper studies on the image registration of the medical images. Wavelet transform is adopted to decompose the medical images because the resolution of the medical image is high and the computational amount of the registration is large. Firstly, the low frequency sub-images are matched. Then source images are matched. The image registration was fulfilled by the ant colony optimization algorithm to search the extremum of the mutual information. The experiment result demonstrates the proposed approach can not only reduce calculation amount, but also skip from the local extremum during optimization process, and search the optimization value.

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

ASP.NET technology was used to construct the B/S mode image query system. The theory and technology of database design, color feature extraction from image, index and retrieval in the construction of the image repository were researched. The campus LAN and WAN environment were used to test the system. From the test results, the needs of user queries about related resources were achieved by system architecture design.