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The impact of coated paper printability for image quality on the high-fidelity digital printing was studied in this paper. Compared with the traditional printing technology, high-fidelity digital printing technology is more likely to expand the color gamut, maintain the original tone linear and produce vivid image. The performance of coated paper to a large extent determines the image quality. In order to get high-quality printed matters, the mechanism of the coated paper printability influencing on the performance on high-fidelity digital printing is need to be researched in-depth. The printability of coated paper was studied from several aspects in this paper, including the structural properties, roughness, optical properties, absorption etc, the relationships between these properties and printing color, dot reproduction performance were also studied. Based on the previous theoretical and practical studies, the printability evaluation system of coated paper on high-fidelity digital printing was established initially. On digital printing, accurate color management technology also affects the printing quality as the performance of coated paper, so the advanced color management technology was also used in the current study. The results showed that the pore structure and the absorbent of coated paper were the most important factors for affecting the printing quality, and the roughness and optical properties of coated paper also had a certain impact.

The color reproduction is based on visual perception. The psychophysics experiments present the light source; illuminants and view background affect the results of color matching. Paper is a typical substrate for printed color matching and effects printed color greatly. Now almost all of print papers contain optical brightening agents, which changes significantly the color appearance of printed color and makes color shifting of printed color matching. This paper used CAT02 chromatic adaptation models to transform the measurements of printed color in paper with and without OBA, compared the effect of printed color matching between before and after transforming with display, present the method using chromatic adaptation transforms to improve the accurate of printed color matching.

Much progress has been made in the field of digital imaging technology with the widespread use of CMOS and CCD devices and the high computing power of IC chips. However, there still are many technological problems to overcome to achieve lightweight and affordable high density 3D lossless imaging for both industrial and scientific applications. This paper will focus on the latest development of freeform optical technology and powerful image analysis techniques to develop a compound eye camera system so as to extract the 3D relative position coordinates of the sub-object in the field of view, and to compensate the shadow effect in the images caused by the non-uniform illumination to obtain high quality lossless images. Such a novel compound eye system will find applications in delivering 3D image of digital camera, increasing capability of scientific profiling measuring instrument, and lowering cost and enhancing resolution of wide-field, video-rate, gigapixel cameras such as the AWARE designed by Duke University.The main objective of this paper is to design a novel compound eye system from high definition 3D lossless digital imaging technology to achieve the realistic three-dimensional positional information of the object in both near and far fields. In the imaging process, the ultimate imaging quality is affected and limited by the resolution of the detector, the field of view, wavefront aberration, resolution of the optical imaging system and the ambient light.The novel compound eye imaging system includes two modules: one is the optical imaging module with compound eye optical elements with other secondary optical lenses, while the other one is the image analysis module. By combining the above optical hardware with the image analysis software, the relative position information of the sub-objects in the picture and the lighting condition of the environment can be deduced accurately.

It is important to obtain the 3d coordinate in the field of measuring. How accurate, fast, convenient to obtain the 3d coordinate affects the accuracy and reliability of measurement directly. Through studying the basic theories of machine vision this paper focus on the study of a stereo vision measurement model based on the intersecting axis. In view of the parameters in the model of binocular stereo vision, this paper uses the Zhang Zhengyou calibration method to calibrate the system of Stereo vision. In order to test the accuracy of the system, this paper measures the distance of two standard circular. Finally, the machining experiment validates the proposed method.

This paper analysis the characteristics of High Spatial Resolution Remote Sensing Image (HSRRSI), consider the drawback of pixel-wise Classification, an Object-based method was taken to classify the image. The paper firstly preprocess and coarsely segment the image, secondly optimize the segmented results for deeply processing, then extracting the image feature from the original image and segmented results, finally classify the image and assess the results.

The paper analysis mean filter, median filter and morphology filter. The paper studied order morphology theory and its filter character. Based on this, the paper proposed multi-scale and multi-structure filter algorithm based on order morphology. The result of simulation shows that the algorithm can not only keep the edge and detail of medical image but also can enhance the contrast of recovery image.

Stereo matching methods are widely used in computer vision and stereo reconstruction, from the perspective of improving the matching accuracy, this paper focuses on the global optimization algorithm. An improved Belief Propagation method is proposed in this paper, by involving more pixels into information transmission, our method improves the accuracy ofstereo matching. The experimental results verify the efficiencyand reliability of our method.

In this paper ,a color printing defect automatic online detection method based on digital image processing technique is proposed. The main idea of this method is comparison of defect product and template and it makes up of following key models. Firstly, multi-scale segmentation is applied to composed image which is overlaid by detecting product and template image. Secondly, an automatic region similarity analysis calculation is taken to segmentation obtained in multi-scale segmentation. The color difference between detecting product and template can be calculated accurately. Thirdly, defect detection results can be obtained according to threshold segmentation. Finally, the characteristics and advantages are approved by experimental analysis and discussion. Algorithm parameters are adjusted and modified to improve the stability and effectiveness. Experimental results approve that color printing defect automatic detection method in this paper has the characteristics of effectiveness and applicability. And experimental results indicate that this method has the advantage of judging the defect types automatically.

Synthetic Aperture Radar (SAR) has become one of the important means for the ocean remote sensing detection of oil spills. The existing SAR image segmentation method has the issues of edge blur, poor contrast, non-uniform intensity image, so the segmentation effect is not ideal. This paper presents a variational level set SAR image of oil spill detection method based on fuzzy clustering. First of all, apply the threshold method on initial segmentation of the original SAR image to transform the initial segmented image as fuzzy clustering. Secondly, introduce the clustering results into the initial level set function to achieve the initial contour. Finally, add fuzzy clustering model in the level set energy function to complete the level set evolution process and get the final segmented image. This paper uses the threshold segmentation results to achieve the initialization of the variational level set function profile. In theory, it could improve the level set method for efficiency, and reduce the wrong segmentation phenomenon. The experimental results show that the SAR image segmentation method of oil spill has good segmentation qualities and is suitable for the edge complex image segmentation.

Geographic Information Service is open service based on the network environment. DEM should be protected because of its significance. This paper gives three methods for DEM information protecting, they are DEM encrypting, DEM information hiding and DEM Information disguising, and researches their theories and techniques. Then, this paper analyses their differences and relation. This paper could provide theory support for DEM information security research in Geographic Information Service.

With the improvement of computer hardware and software performance, digital multimedia technology has been applied to various fields. The visual effect of ceramic art image is affected mainly by the brightness, texture and color, in which the image defects will affect the visual effect of the ceramic product. This paper designs the digital processing system of a new ceramic art image processing, the system can repair defects in ceramic art image. In order to verify the validity and reliability of digital processing system, the lead-free perovskite structure BZT based ceramics is prepared in this paper, and the use of electron microscope and digital camera take the ceramic products processing image with artistic defects, and then using VB programming digital image processing technology carries out color re coating for the ceramic, the ceramic products electron microscopy images after restoration will be obtained by the image boundary restructuring, which provides a new computer method for the processing art of the ceramic products.

With the development of computer graphics, real-time rendering-based VF: technology has been applied in more and more fields. LOD is the key technology in large-scale terrain rendering. In this paper, the basic concept of LOD is introduced briefly and some algorithms of LOD in use are mentioned and analyzed; secondly as one of algorithms of LOD, View-Dependent Progressive Mesh algorithm is studied and improved, the result of implementing the large-scale terrain’s LOD by using VDPM is presented. There are key technologies in LOD Large-scale terrain real-time rendering are researched. Relative technologies are presented such as: LOD of the terrain, visibility culling, and cracks eliminate, view-dependent refine, LOD error, technologies of texture etc. Using LOD technology, VR system can greatly reduce the; number of polygons produced in real-time rendering procedure. Finally, we do experimental design work based on the methods and techniques presented by this paper.

This paper aims at the difficulty that lack of observation model and high-dimensional sampling in video tooning, proposes a method based on key frame matching and dual-directional Markov chain Monte Carlo sampling of video motion redirection. At first, after extracting the key frame of a given video, By affine transformation and linear superposition, the subject initializes the video’s space-time parameters and forms the observation model; Secondly, in each space-time, based on the bi-directional Markov property of each frame, This paper proposed a dual-directional Markov chain Monte Carlo sampling particle filter structure and takes full advantage of the relationship of the front and back frame of the parameters to estimate motion redirection parameters. At the same time, for high-dimensional sampling problem, the subject according to the directional parameters’ correlation implements classification of skeleton parameters-morphological parameters-physical parameters, proposes a hierarchical genetic strategy to optimize the output parameters and improves the efficiency of the algorithm. The research of this paper will produce an efficient and prominent animation expressive video motion redirection method and play an important role on video animation of the development.

Infrared communication technology is a short-range wireless communications technology widely adopted in the world today range .This paper detailedly introduces the basic principles of infrared protocol. Infrared communications applications of this paper is to achieve system applications of infrared communication between two development boxes with an infrared transceiver modules.The program consists of PC program and lower computer program two parts,also are called infrared communication basic procedures and infrared communication console program.Running PC program on a PC,running lower computer program on the development box.Based on Borch-company's S3C2410-S ARM9, experimental development box implements a infrared communications infrastructure lower computer program based on serial meeting SIR standards.By calling the Qt graphics library,using the programming language C++,it achieves a visual infrared communication console PC program under linux system.

This paper mainly studies modeling and recognition of 3D English words’ images. With the development of secondary modeling, segmentation and recognition theories and the application of evolution computation in 3D modeling and recognition, this paper analyzes the issues of parameter fitting in the 3D model, multi-object scene segmentation and parts recognition aiming at the 3D data features in the English words. The 3D model is used as the primitives part to model and segment the scenes and the group parallel evolution and the relationship matching theories are introduced into the 3D modeling and recognition to deeply identify the rare English words’ images. The paper searches for a practical and efficient three-dimensional modeling and identification scheme.

This paper presents fuzzy color histogram feature-based image retrieval method and texture spectrum fuzzy histogram feature analyzes the image database indexing techniques and the introduction of the experimental system for an improved method of fuzzy indexes. Algorithm reflects the underlying characteristics of high-level concepts and integration, relevance feedback and machine learning mechanism combining ideas. In this paper, the algorithm full processing power of computer systems, has a certain reference value and practical significance.

This paper presents a 3D gesture recognition technology based on machine vision as the center. Based on a large number of experiments, this paper sums up and introduces the existing gesture recognition technology, the key research contents of gesture recognition, as well as the history of development of gesture recognition technology. Then, the paper does research in the main technology of gesture recognition .The experimental results show that the method can realize 3D gesture recognition in video sequences with real-time and stability, even more; it can get better recognition result.

In this paper, frame differential method will be used to detect moving targets in a static background video file, and pre-contour can be obtained by binarizing the detected targets . However,, the result are not what was expected, so the expansion and corrosion of mathematical morphology are used to extract the final contour of moving targets. in the progress of dealing with massive data , mathematical morphology is not good enough to achieve the need of the real-time in video surveillance. Considering the dilation and erosion is a kind of parallel processing operations, in order to improve the speed of mathematical morphology operations, this paper offers detailed implementation process of the dilation algorithm for parallel computing on GPU. Experimental results showed that GPU parallel processing on mathematical morphology algorithm faster than the CPU serial processing.

The objective of this paper is to build a system for pedestrian detection in an outdoor environment. The contribution of this paper is the detection method that integrates the silhouette edge information with patterns of motion in each frame of the image sequence. The test results show that the high-precision, good adaptability, and real-time performance of the proposed method.

This paper first introduced the geometric correction method for remote sensing image used in the software, put forward the method precision visualization comprehensively and systematically describes the quality of remote sensing image rectification. This paper illustrates an instance method and its application through a IKONOS image rectification,and some useful conclusions are obtained. Keywords: Remote sensing image; image rectification; accuracy; spatial interpolation; error

In this paper, we use the embedded theory and wavelet algorithm to improve image coding, and get a new optimization algorithm of track and field image, and establish the wavelet reconstruction mathematical model of image optimization. In order to verify the effectiveness and reliability of the wavelet reconstruction algorithm, this paper uses computer embedded PLC control system and 317-2 PN/DP special CPU to establish the optimization system of track and field path, and debug a program by using Step7 software, which realizes the calculation function of embedded system. Finally, this paper does wavelet reconstruction on real time image, we obtain the reconstruction wavelet optimal path, and output it in the form of digital image, and use the drawing function to realize re-draw function of image, finally we get the route optimization figure of track and field teaching. It provides a new method for computer teaching process.

Due to the development of image segmentation and reconstruction technology, it provides a larger space for development of the image enhancement technology. Under the promoting of the image processing calculation, the capture and recognition function of digital X-ray photography technology are stronger, and the image processing precision is higher. Based on the variation principle, this paper uses the function approximation to improve the X-ray photography image processing technology, and obtains the new boundary value reconstruction condition of X-ray photography. In order to verify the effectiveness and reliability of the mathematical model and algorithm of the boundary value reconstruction, this paper uses MATLAB software and C language to debug the algorithm, and realizes the digital color rendering for the images at the terminal of track and field, obtains the image reconstruction algorithm under different boundary values. It provides a new computer method for the research on image enhancement technology.

This paper presents an algorithm for hiding information’s in raw video steams using art of steganography using discrete wavelet transform. While mostly applied to still images in the past, it has become very popular for video streams recently. When steganographic methods are applied to digital video streams, the selection of target pixels, which are used to store the secret data, is especially crucial for an effective and successful-embedding process; if pixels are not selected carefully, undesired spatial and temporal perception problems occur in the stegno-video. Typically, an irrecoverable steganography algorithm is the algorithm that makes it hard for malicious third parties to discover how it works and how to recover the secret data out of the carrier file. In this paper, a new embedding algorithm is proposed to hide the secret data in moving videos. The 2D-DCT of the video is taken and the secret message is embedded. The performance measures are evaluated for the quality of the video after the data hiding and show good results.

Compressed sensing is referred to as the CS technology; it can realize image compression and reconstruction process in low sample rate. It has great potential to reduce the sampling rate and improve the quality of image processing. In this paper, we introduce the structure prior model into the compressed sensing and image processing, and make the image reconstruction of high dimensional optimization process simplified into a series of low dimensional optimization process, which improves the processing speed and image quality. In order to verify the effectiveness and reliability of the proposed algorithm, this paper uses combined control form of C language and MATLAB software to design the programming of structure prior model, and use the Simulink environment to debug the program. Through the calculation we get the image block and the reconstruction result. It provides the technical reference for the research on image compressed sensing technology.

A biometric identification system may be a pc application for mechanically distinctive or confirmative of an individual from a digital image or a video frame from a video supply. One in all the ways that to try and do this can be by examination designated face expression from the image and a facial information. This paper planned dynamic face recognition from near-infrared images by exploitation sparse representation classifier. Most of the prevailing datasets for facial expressions are captured in a very visible light spectrum. However, the visible light (VIS) will modify with time and placement, causing important variations in look and texture. This new framework was designed to attain strength to pose variation and occlusion and to resolve uncontrolled environmental illumination for reliable biometric identification. This paper gift a unique analysis on a dynamic facial features recognition, exploitation near-infrared (NIR) datasets and LBP(Local binary patterns) feature descriptors. It shows sensible and strong results against illumination variations by exploitation infrared imaging system.

Disparity estimation is a basic analytic method for images and video and is highly used in fields like stereo image coding, video retrieval and stereo image segmentation. This paper proposes a new disparity estimation method. In this paper, the superiority of the multi-block matching is analyzed. The new method based on the idea of block matching constructs a multi-block matching algorithm, thereby improving matching accuracy especially in the disparity discontinuous regions. A adaptive window method based on edge detect is proposed to select the best window.The experimental results based on the Middlebury stereo datasets demonstrate that our method can achieve a better performance.

The Mean-Shift algorithm has very good tracking effect when the background is in a simple; but for a complex environment, tracking effect is not very ideal. Therefore, a new gray feature modeling method is proposed in this paper. Firstly, target in the tracking window is uniformly divided into even pieces. Then the pixel gray value of each block is calculated with subtraction of certain rules. Finally, the gray value of gray difference and the whole object value fusion are fused and established the object model. The object model that established not only contains the whole gray value information, but also contains the gray value differences between regions, has a more accurate description of the target, and then distinguish target from background better. The experiment results show that: the target model using the method in this paper to track based on the Mean-Shift algorithm, has good adaptability when the target is partially occluded and has better robustness for complex background.

The traditional virtral target aiming training uses indoor large screen projection more. Recently, the system also uses the virtual reality scene as background. According to the situation, using real-time video frame as the virtual target aiming training background for the authenticity of the virtual target aiming training scene is presented in this paper. This method fully shows the maturity of modern video acquisition technology and the innovation of virtual reality scene. This paper prensents using DirectShow tools for video frame acquisition and taking the advantages of OpenFight API for the model built by military simulation modeling software MultiGen Creator development based on OpenGL development platform and completes the building of virtual target aiming training scene based on real-time three-dimensional video scene and the consistency of virtual target and scene.

Image segmentation technique was used widely for computer vision and image processing. A robust technique of image segmentation plays a crucial role in identification problem. In this paper, a nonparametric and unsupervised method of automatic threshold for segmenting image was proposed, i.e. the optimal threshold is approximated by global average gray and local average gray, and this method was compared with other methods by using standard image. The experimental results show that our method proposed in this paper is robust. In addition, an image database of road traffic marking (www.ananth.in/RoadMarkingdetection.html) is provided to do this experiment for testing our method, the results show that our method is excellent.

The paper mainly discusses the analysis method for the psychological impact of computer vision noising technology. Actually, people's psychological acceptance and corresponding memory capacity of computer vision images with lots of noise are relatively poor. The de-noising process to computer vision images can improve the clarity, thus generating passive psychological impact. Therefore, the paper proposes a spatial domain filtering algorithm-based de-noising method for computer vision. It establishes wavelet packet decomposition tree for computer vision images and de-noises accordance with the decomposing results. The experiment results show that the proposed de-noising method has passive psychological influence and improves the memory capacity of computer vision images.

UAV Video is rapidly emerging as a widely used source of imagery for many applications in recent years. This paper presents our research on the mosaic of UAV video for the purpose of harbor surveillance. First, one new framework on estimating video frame transformation with Optical flow is presented in this paper. For this new framework, fewer number of Gaussian pyramid is created for implementing for the multiresolution approach and thus, more details for optical flow computation is well kept; Second, we make a discussion on using Fourier-Mellin Transformation in image frequency domain to estimate initial motion parameter of adjacent video frames and with those initial motion parameters, small displacements for optical flow computation can be achieved; The experimental results demonstrated that the mosaic image generated from aerial video shows satisfied visual quality and its surveillance application for fast response to time-critical event, e.g., flood, is descried.

This paper researches on the detection and tracking technology of ship intrusion and remaining based on the special surveillance video scenes of sea. A ship detection and tracking method that combines the adaptive background subtraction with centroid algorithm is proposed. Compared with the method of edge orientation histogram, the method proposed in this paper shows a lower computational complexity and a better real-time performance. The experiment demonstrate that this method has a high degree of accuracy. And it makes a realization of detecting and tracking of ship intrusion and remaining within the surveillance sea.

In order to quickly and effectively segment the video scene, a multi-modality video scene segmentation algorithm with shot force competition is proposed in this paper. This method is take full account of temporal associated co-occurrence of multimodal media data, to calculate the similarity between video shot by merging the video low-level features, then go to the video scene segmentation based on the judgment method of shot competition. The authors experiments show that the video scene can be efficiently separated by the method proposed in the paper.

Remote sensing image fusion is an important branch in the field of image fusion. Remote sensing image fusion mainly researches how to use different aerial remote sensor to obtain relevant image information. In view of the present research status in this field and the full analysis of remote sensing image fusion method and wavelet transform, the paper aims at proposing the image fusion technology of combining discrete multiwavelet transform and HIS method, then the paper analyses the experimental results with the help of MATLAB. It is proved that the analysis method of wavelet has more advantages than traditional methods: Wavelet transform fusion method can keep maximized resolution and multispectral image.

Rapid and automatic extraction of pollutants is of great importance for mobile camera in real-world scenarios. In the moving process of robot, the camera is treated as dynamic background, and the pollutants on the camera are treated as static target. So as to realize the detection of static target in dynamic scene, this paper presents an automatic extraction algorithm for pollutants based on frame difference accumulation. Firstly, this algorithm can determine whether pollutants exist or not by the overall trend of gray histogram of the cumulative frame difference images. And then if existing pollutants, Otsu algorithm was adopted to adaptively calculate threshold to convert the gray image into binary image. In this paper, analysis of the important parameters of the algorithm - cumulative frame number and frame difference threshold was implemented. Moreover, feasibility of the algorithm was verified by experiments on the automatic extraction of pollutants for gun-type camera.

The quality of side information (SI) is one of the critical factors which play an important part in the compression performance of Distributed Video Coding (DVC).In this paper, we analyze the algorithm of Motion Compensation Interpolation (MCI) and propose an improving algorithm against its limitation. The algorithm proposed firstly makes each pixel has more than one motion vectors, then picks the best vector out and makes it plays the most important role in motion compensation with weighted average. Through the simulation of different frame sequences, the PSNR of the side information generated by the algorithm proposed in this paper is 0.3-0.7dB higher than that in the original algorithm.

With the development of computer and internet, how to recognize face images rapidly has become the research focus in recent years. Therefore, face recognition technology develops rapidly, and is widely applied to national counterterrorism, information security and access control. But the researchers find that face recognition only can achieve satisfactory effect under the constraint conditions. The paper proposes face recognition algorithm based on multi-resolution analysis and classification. The algorithm firstly uses wavelet transform for multi-scale wavelet decomposition on training sample, and uses the correlation coefficient between wavelet coefficients with the maximum variance as the classification distance to classify the samples and determine the central image of the images. Face recognition algorithm based on multi-resolution analysis and classification extracts some wavelet coefficients of the images, which not only achieves the purpose of dimension reduction and reduces calculated amount, but also effectively improves the speed of face recognition. The simulation experiment proves that the algorithm proposed in the paper is effective.

This paper presents a calibration procedure for a Kinect RGB-D sensor and its application to robot simultaneous localization and mapping(SLAM). The calibration procedure consists of two stages: in the first stage, the RGB image is aligned with the depth image by using the bilinear interpolation. The distorted RGB image is further corrected in the second stage. The calibrated RGB-D sensor is used as the sensing device for robot navigation in unknown environment. In SLAM tasks, the speeded-up robust features (SURF) are detected from the RGB image and used as landmarks in the environment map. The depth image could provide the stereo information of each landmark. Meanwhile, the robot estimates its own state and landmark locations by mean of the Extended Kalman filter (EKF). The EKF SLAM has been carried out in the paper and the experimental results showed that the Kinect sensors could provide the mobile robot reliable measurement information when navigating in unknown environment.

With the development of integration of three networks which combines telecommunication network, computer network and cable television network [1-2], decision support system for stereo vision is proposed. Combined with the user experience which is given a model of the influence factor in this paper of Stereo vision, the data mining model will be more comprehensive and more convincing and the decision support system for stereo vision will be more plump and more practical. This paper designs the model of the influence factor for Stereo vision user experience which makes the data mining model for Stereo vision exhibits an excellent function.

Through the analysis of present research and application situation of binocular stereo vision system, the paper indicates an opinion that the structural parameters of system are crucial to the measurement accuracy of system. This paper focuses on several parameters to discuss the effect on the measurement accuracy, such as the distance of baseline, angles between optical axis and baseline, projection angles and so on. According to measuring principle and mathematical model of binocular stereo vision system, through deriving formulas and drawing curves by means of MATLAB, the impact that different structural parameter has on measurement accuracy is analyzed. Finally, the correctness of theoretical analysis is proved by experiment and the research result is summarized.

With the development of the computer information science and technology, image Mosaic technology has been applied to various fields, which including image compression and amplification, synthesis of images and virtual scene of image design . The algorithm of image mosaic technology research is increasing. This paper analyzes the present situation and application of image mosaic technology and proposes the genetic algorithm of image mosaic technology based on computer information theory. It also constructs the image mosaic technology flow chart. This paper simulates image magnification and synthesis technology in order to prove feasibility and reliability of algorithm by the image Mosaic technology cases. This paper puts forward opinions to the deficiency of mosaic technology and provides a theoretical reference for the application and development of image mosaic technology.

A fast remote sensing scene matching method, taking airports, oil depots, harbors and so on as research objects, is proposed in this article which is based on the SR saliency detection and frequency segmentation. Saliency detection is used to determine the candidate region where the target may exist to reduce the searching range effectively. And then, frequency segmentation is used to eliminate the frequency component except the frequency of the target to reduce the redundant information, thereby saving the computation of SIFT feature extraction and matching. A variety of experiments under different interference factors are carried out in this paper. Experimental results show that the fast matching algorithm proposed in this paper can not only maintain the validity of SIFT features under the condition of rotation, scale, illumination and viewpoint changes, but also shorten the matching time largely and improve the matching efficiency, laying the foundation for further practical application.

This paper presents some techniques for constructing panoramic image stitching from sequences of images captured by cameras from different angle in garment defects detecting area. The image stitching representation associates a transformation matrix with each input image. In this paper, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. An improved SIFT(Scale Invariant Feature Transform) algorithm was used to locate the feature points in the scanned images targeting at the problem of automatically stitching of textile images which were scanned in divided pieces. RANSAC (random sample consensus) method is proposed to to estimate image transformation parameters and to find a solution that has the best consensus with the data. Techniques for estimating and rening camera focal lengths are also presented. In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimally registered image stitching. A local alignment technique is also developed which warps each image based on the results of pairwise local image registrations to compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions.

In realistic image processing, it is a problem of image foreground extraction. For a large number of color image processing, an important requirement is the automation of the extraction process. In this paper, by automatically setting foreground seed, we improve the image existing segmentation algorithm; by automatically searching image segmentation region, we accomplish image segmentation with the GrabCut algorithm, which is based on Gaussian Mixture Model and boundary computing. The improved algorithm in this paper can achieve the automation of image segmentation, without user participation in the implementation process, at the same time, it improves the efficiency of image segmentation, and gets a good result of image segmentation in complex background.

Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

This paper studies the real-time compression and storage technology,extraction and collection contains that Images,Image compression standard,Image storage.The paper use JPEG2000 compression standard.Image via IDE interface to hard disk storage.The results show that non-interest areaimage compression and srorage can remove redundant;reduce the amount of data transfer and increase the transmission speed.

According to the characteristic that the gray value difference is quite obvious in different areas of the singles infrared cloud, this paper puts forward an algorithm of the eyed typhoon's center automatic positioning. After pretreating the cloud image, we use the template to segment the airtight cloud wall area which has high gray value. In addition, the difference between gray values of the airtight cloud wall area is small. Then we do binarization processing to the image and use the method combined with the mathematical morphology and artificial intelligence to process the image in filtering smoothly. We remove the isolated interference point. Then we corrode image and adopt the seed filling algorithm to detect the candidate eye area. In the candidate eye area, the paper puts forward the discriminant function which is based on the combination of the gray information and the distance between the inspection points and the circle's center. The candidate eye area corresponded to the minimum value of the discriminant function is the center of the eyed typhoon. The experiment result shows that this algorithm has higher positioning accuracy and it can be used for meteorological service.

At first, in the paper, through the preprocessing of face image by the histogram equalization, enhance the image contrast. Then this paper uses Gabor wavelet transform, by choosing different size and direction to facial features extraction and Principal Component Analysis method for dimension reduction. Finally, input the face feature into the Radial Basis Function neural network for identification. Based on RBF neural network is trained and weights optimization, the efficiency of identification had been improved.

As the number of students who attend the arts exams has been growing, each admission institutions need to score the human head portraits of art sketch with the number of ten thousand and even 100 thousand. Thus it is an innovative research on how to conduct image recognition with the help of advanced computer technology. Image Recognition Technology is to give the computer the intelligence of human vision, so that the computer can quickly and accurately recognize the object on the input images. However, in the recognition process such factors as light, rotation and shield increase the difficulty of identifying the human head portrait images. In order to get better recognition performance, this paper studies the feature extraction of human head portrait based on SIFT (Scale Invariant Feature Transform). From the practical application, it can be seen that the approach proposed in this paper is feasible and is of good recognition performance.

Despite the appearance of high-tech human computer interface (HCI) devices, pattern recognition and gesture recognition with single camera are still playing vital role in research. A real-time human-body based algorithm for hand gesture recognition is proposed in this paper. The basis of our approach is a combination of moving object segmentation process and skin color detector based on human body structure to obtain the moving hands from input images, which is able to deal with the problem of complex background and random noises, and a rotate correction process for better finger detection. With ten fingers detected, more than 1000 gestures can be recognized before concerning motion paths. This paper includes experimental results of five gestures, which can be extended to other conditions. Experiments show that the algorithm can achieve a 99 percent recognition average rate and is suitable for real-time applications.