Image and data fusion

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Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

Dynamic changes of wetland resources based on MODIS and Landsat image data fusion

Influenced by factors such as climate, topography, and soil, wetland resources possess the characteristics of complex and changeable land types, and the geograph- ical location of most wetlands is remote with poor trans- portation. Only relying on conventional means to monitor wetland situation not only requires a large amount of material cost with small investigation scope, but also causes damage to the wetland, which has a long period of wetland research and insufficient timeliness. The time-space fusion of remote sensing data effectively gives play to the advantages of different information sources of MODIS and Landsat data; the remote sensing information source obtained by modified STARFM model fusion algorithm has the advantages of multidate, high precision, etc., providing an effective remote sens- ing information source for remote sensing investigation of land resource. Experiment proves that MODIS and Landsat data can be fused in a good manner, the image after fusion has high correlation with TM image, and this method can provide practical reference for the re- mote sensing investigation of traditional land resources including wetland resource. Research results show that the image after fusion can well reflect the vegetation index, water body index, and vegetation coverage of the
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Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques

Colour Image Segmentation Using Homogeneity Method and Data Fusion Techniques

In this context, Gautier et al. [22] aim at providing a help to the doctor for the follow-up of the diseases of the spinal column. The objective is to rebuild each vertebral lumbar rachis starting from a series of cross-sections. From an initial segmentation obtained by using the Snakes, active contour models, one seeks a segmentation which represents as well as possible the anatomical contour of the vertebra, in order to give the doctors a schema of the points really forming part of the vertebra. The methodology is based on the application of the belief theory to fusion information. However, the active contour models do not require image preprocessing and provide a closed contour of the object, however typical problems remain difficult to solve including the initialization of the model.
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A NOVEL APPROACH FOR FUSION OF LOW RESOLUTION MULTISPECTRAL IMAGES IN REMOTE SENSING

A NOVEL APPROACH FOR FUSION OF LOW RESOLUTION MULTISPECTRAL IMAGES IN REMOTE SENSING

It is well known that combining several images of the same scene, characterized by different conditions of acquisition, allows to access a more precise description of the imaged objects by overcoming the intrinsic limitations of each modal- ity considered separately [6]. A key example attracting a large interest in recent years is the pan sharpening, which refers to the generation of synthetic high resolution multichannel satellite images characterized by both a high spatial resolution and spectral diversity. The products of pan sharpening find a wide- spread use in platforms such as Google Earth and Microsoft Bing, as long as base data for scientific studies [7], [8]. Two sensors acquiring a multispectral (MS) image with low spatial resolution and a PAN chromatic (PAN) image with high spatial resolution are often available on board of the same satellite platform (e.g., Quickbird, Ikonos, SPOT, Landsat) producing simultaneous acquisitions that enjoy the favorable condition of being registered. Within data fusion problems, pan sharpening shows some specific characteristics, as for example, the different spatial resolutions of the available images and the need to preserve the characteristics of the MS data. These peculiarities have caused the development of a vast dedicated scientific literature [8], composed by both classical and novel approaches. Classical approaches are in general based on relatively simple fusion schemes and are characterized by a low computational complexity [8]. Some recently proposed techniques depart from the classical architectures, such as those based on sparse representation theory [9]–[11], Bayesian inference [12], or variational methods [13]. However, although some of these latter
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A NEXT-GEN DATA FUSION ? BIG DATA FUSION

A NEXT-GEN DATA FUSION ? BIG DATA FUSION

(2)according to the input/output data types and their nature, as proposed by Dasarathy [4]; (1)data in-data out (DAI-DAO): this type is the most basic or elementary data fusion method that is considered in classification. This type of data fusion process inputs and outputs raw data; the results are typically more reliable or accurate. Data fusion at this level is conducted immediately after the data are gathered from the sensors. The algorithms employed at this level are based on signal and image processing algorithms;(2)data in-feature out (DAI-FEO): at this level, the data fusion process employs raw data from the sources to extract features or characteristics that describe an entity in the environment;(3)feature in-feature out (FEI-FEO): at this level, both the input and output of the data fusion process are features. Thus, the data fusion process addresses a set of features with to improve, refine or obtain new features. This process is also known as feature fusion, symbolic fusion, information fusion or intermediate- level fusion;(4)feature in-decision out (FEI-DEO): this level obtains a set of features as input and provides a set of decisions as output. Most of the classification systems that perform a decision based on a sensor’s inputs fall into this category of classification;(5)Decision In-Decision Out (DEI-DEO): This type of classification is also known as decision fusion. It fuses input decisions to obtain better or new decisions.
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Hybrid PCA-DCT Based Image Fusion For Medical Images

Hybrid PCA-DCT Based Image Fusion For Medical Images

Artificial Neural Networks (ANN) are motivated from the fact that biological neural network is having the potential to learn from inputs for processing attributes and for making global decisions. To identify set of parameters for the input training set, weights are referred for artificial neural network models. The scope of the neural network models to approximate, analyze and deduce information from a given data without going through a rigorous mathematical solution is often seen as a benefit. This makes the neural network more striking to image fusion as the nature of variability between the images is subjected to change every time a new modality is used [4]. The potential to prepare the neural network to accept to these changes permit several applications for medical image fusion such as solving the problems of feature generation, classification , data fusion image fusion, breast cancer detection, medical diagnosis, cancer diagnosis, natural computing methods and classifier fusion. Although ANN recommends generality in terms of having the capability to apply the notion of training, the robustness of ANN methods is limited by the superiority of the training data and the accuracy of convergence of the training algorithm. In order to improve the quality of the features and thereby to improve the strength of the ANN, hybrids of neural networks and sequential processing with other fusion techniques can be employed [5].
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Image Fusion by means of DWT for Improving Classification Accuracy of RS Data

Image Fusion by means of DWT for Improving Classification Accuracy of RS Data

Image fusion is a process of combining two or more images , obtained from new and composite images using a certain algorithm .image fusion is to integrate different data in order to obtain more information than that can be derived from each of the single sensor data alone , image fusion has been applied to achieve a number of objective like image sharpening, improving geometric correction, complete data set for improved classification, change detection, substitute missing information, replace defective data. Data fusion is a formal framework in which are expressed means and tools for the alliance of data originating from different source gives “different quality “ means that will depend upon the application. Some generic requirement can be imposed on the fusion result.
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Grain Sample Quality Assessment using Color Image and Spectra Analysis

Grain Sample Quality Assessment using Color Image and Spectra Analysis

ABSTRACT: The paper presents the approaches, methods and tools for assessment of main quality features of grain samples using analysis of objects color images and spectral characteristics, which are implemented in the frames of INTECHN project. Visible features like color, shape, and dimensions are extracted from the object images. Information about object color and surface texture is obtained from the object spectral characteristics. The categorization of grain sample elements in three quality groups is accomplished using two data fusion approaches. The first approach is based on the fusion of the results about object color and shape characteristics obtained using image analysis only. The second approach fuses the shape data obtained using image analysis and the color and surface texture data obtained by spectra analysis. The results obtained by the two data fusion approaches are compared.
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Implementation of non subsampled shearlet transform for image fusion

Implementation of non subsampled shearlet transform for image fusion

is an emerging field and powerful technology in the area of image processing. The process of integrating multiple input images into a new single composite image with more informative than any of input image. There are different image fusion transform techniques proposed by many researchers. Out of that transform techniques a Non-subsampled shearlet transform invariant, capture more directional information and represent the directions ransform techniques such as discrete wavelet subsampled contourlet transform (NSCT) techniques. This paper presents concept subsampled shearlet transform based decomposition algorithm for fusion using MATLAB Simulink Library. Simulink library Blockset is used to implement a model which is able to do the pixel level averaging image fusion. Non-subsampled shearlet transform is implemented with the filter banks whose levels can be adjusted. The perfect reconstruction can be obtained with the down sampling of the images. NSST decomposition provides a simple hierarchical framework to fuse images with different spatial resolution. It is a powerful tool
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Thought–shape fusion and body image in eating disorders

Thought–shape fusion and body image in eating disorders

the concrete relationship among TSF, specific instruments to assess body image disturbances, and body image quality of life in ED patients. Thus, the aim of this study was to analyze these types of relationships in order to improve the understanding of the links between body image concerns and a specific bias consisting of beliefs about the consequences of thinking about forbidden foods. The specific hypotheses were: (1) considering the transdiagnostic theory of ED, differences between subgroups of ED with respect to the variables included in this study would not be expected; (2) there would be differences in the variables between patients with high versus low TSF; and (3) there would be significant correlations between TSF and body image-related variables that would be maintained after controlling for psychopathological variables.
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A Review of Different Methodologies of Image Fusion

A Review of Different Methodologies of Image Fusion

Y-T et al. (1997) [15] have claimed in this work that the Histogram equalization is extensively employed for contrast amelioration in a range of functions owing to its basic application and usefulness. Instances count healthcare picture treatment and radar signal treatment. One disadvantage of this Histogram equalization may be observed on the actuality that a picture’s brightness may be modified after the Histogram equalization, and this is because of the flattening characteristic of the Histogram equalization. Hence, it is seldom used in client electronic items like television where keeping the source fed brightness can be required so as not to institute pointless optical degradation. This work suggests a new addition to Histogram equalization to surmount this kind of disadvantage of Histogram equalization. The core of the suggested calculation is to use autonomous Histogram equalizations individually over two secondary pictures obtained by disintegrating the original picture founded on its average with a limitation that the ensuing equalized secondary pictures are enclosed by one another around the input average. It is proven arithmetically that the suggested calculation keeps the average brightness of a provided picture consequently well in comparison to normal Histogram equalization whilst improving the contrast and hence, gives an innate improvement that may be used in client electronic items. According to Zaveri et al. (2009) [13], the image fusion is the procedure of merging several source pictures of one scenario into one solitary merged picture, which conserves pertinent data and additionally keeps the essential aspects from every one of the source pictures and renders it more appropriate for machine and human observation. This work recommends a new area founded image fusion technique. Research papers
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Application of Statical Image Fusion in Medic...

Application of Statical Image Fusion in Medic...

Image fusion provides a mechanism to combine two or more images into a single representation to aid human visual perception and image processing tasks. Such algorithms Endeavour to create a fused image containing the salient information from each source image, without introducing artifacts or inconsistencies. Image fusion is applicable for numerous fields including: defense systems, remote sensing and geosciences, robotics and industrial engineering, and medical imaging. In the medical imaging domain, image fusion may aid diagnosis and surgical planning tasks requiring the segmentation, feature extraction, and/or visualization of multi-modal datasets. This paper discusses the implementation of an image fusion toolkit built upon the Insight Toolkit (ITK). Based on an existing architecture, the proposed framework (GIFT) offers a ‘plug-and-play’ environment for the construction of n-D multi-scale image fusion methods.
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Separation of overlapping dental arch objects using digital records of illuminated plaster casts

Separation of overlapping dental arch objects using digital records of illuminated plaster casts

The experimental environment included the camera placed at a fixed position above the observed orthodontic plaster cast. The source of illumination was installed at the side of the plaster cast to obtain an image with a shadow and reflection to improve the location of the boundary of the object. Instead of using a single image, a series of images was acquired and combined for the following segmentation process. The location of the illumination source was defined by its azimuth ( ϕ ) and its elevation ( θ ) angle and a sym- metric kind of illumination was selected. The illumination angles were selected experi- mentally to produce different shadows of the object. A digital camera using a CMOS sensor was used to obtain the set of grey-level images A i (ϕ i , θ i ) , i = 1, 2, . . . as a function
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A New Scrutiny Method for Medical Likeness Synthesis Using Mean -Mean & Min-Max Algorithms

A New Scrutiny Method for Medical Likeness Synthesis Using Mean -Mean & Min-Max Algorithms

With the availability of multi-sensor data in many fields such as remote sensing, medical imaging, machine vision and military applications, sensor fusion has emerged as a new and promising research area. The current definition of sensor fusion is very broad and the fusion can take place at the signal, pixel, feature, and symbol level. In this project we address the problem of pixel-level fusion or the so-called image fusion problem. Multi-sensor data often presents complementary information about the region surveyed, so image fusion provides an effective method to enable comparison and analysis of such data. The goal of image fusion is to create new images that are more suitable for the purposes of human visual perception, object detection and target recognition. The use of multi-sensor data such as visible and infrared images has led to increased recognition rate in applications such as automatic target recognition.[5,6]
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Improved Weighted Least Square Filter Based Pan Sharpening using Fuzzy Logic

Improved Weighted Least Square Filter Based Pan Sharpening using Fuzzy Logic

not require just about every difficult arithmetic hovering place capabilities in particular advise or even variance info, this isn't very difficult and energy efficient.Aribi, M avec aussi al. (2012) [3] defined your evaluation around the specialist perception top quality can be done through a number of tactics of perception fusion. Information and facts in order to normally always be highly processed from the specialist graphics is obviously top-quality by way of combining the knowledge by way of settled upon graphics along with the unification technique's option is determined by your application. During this papers your MRI in addition to PET graphics tend to be taken ideal for instance. Bedi S.S. avec aussi al. (2013) [4] displayed a whole new reassesment on guides of perception unification tactics in addition to perception top quality evaluation parameters tend to be analysed to put together your algorithm criteria ideal for perception unification in which may appear far more acceptable ideal for health care diagnosis.B.K. avec aussi al. (2013) [5] encouraged that you be part of multifocus graphics from the multiresolution DCT place instead of the wavelet place to lower your computational complexity. The actual evaluations on the complete functionality around the merged perception from the encouraged place in addition to that regarding your wavelet place in addition to 4 recently-proposed unification methods is obviously done.The encouraged technique placed to several eyeglass frames of multifocus graphics along with the operation as soon as when compared successfully in addition to quantitatively in addition to that regarding wavelets. Cao avec aussi al. (2010) [6] gifted advice ideal for multi-focus perception unification in addition to planned that must be handling the artwork bin
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FPGA-Based Multi-Focus Image Fusion Techniques

FPGA-Based Multi-Focus Image Fusion Techniques

Image fusion is a process which combines the data from two or more source images from the same scene to generate one single image containing more precise details of the scene than any of the source images. Among many image fusion methods like averaging, principle component analysis and various types of Pyramid Transforms, Discrete cosine transform, Discrete Wavelet Transform special frequency and ANN and they are the most common approaches. In this paper multi-focus image is used as a case study. This paper addresses these issues in image fusion: Fused two images by different techniques which present in this research, Quality assessment of fused images with above methods, Comparison of different techniques to determine the best approach and Implement the best technique by using Field Programmable Gate Arrays (FPGA). First a brief review of these techniques is presented and then each fusion method is performed on various images. In addition experimental results are quantitatively evaluated by calculation of root mean square error, entropy; mutual information, standard deviation and peak signal to noise ratio measures for fused images and a comparison is accomplished between these methods. Then we chose the best techniques to implement them by FPGA.
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An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

An Adaptive Wavelet based Fusion Approach for Efficient Image Restoration

consider each component in a more detailed way. As for functions f(x, y) and g(x, y), everything is quite clear with them. But as for h(x, y) I need to say a couple of words - what is it? In the process of blurring the each pixel of a source image turns into a spot in case of defocusing and into a line segment (or some path) in case of a usual blurring due to movement. Or we can say otherwise, that each pixel of a blurred image is "assembled" from pixels of some nearby area of a source image. Every one of those cover one another, which reality results in a blurred image. The rule, as indicated by which one pixel winds up spread, is known as the blurring capacity. Three sorts of wavelets utilized in the image fusion are Symmetrical; Bi-symmetrical and A-trous (Non- orthogonal).The image fusion strategy dependent on wavelet change has great spatial and ghastly distinction however has restricted directivity to manage the images having bended shapes. The image fusion is ordered into three level first pixel level second Attribute level and third Decision level.
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A New Image Fusion Algorithm Based on Wavelet Transform and Adaptive Neuro Fuzzy Logic Approach

A New Image Fusion Algorithm Based on Wavelet Transform and Adaptive Neuro Fuzzy Logic Approach

It also provided the assessment and systematic performance analysis of conventional and wavelet image fusion techniques quantitatively. Manjusha Deshmukh [4] presented the utilization of image fusion of PET and MRI images. This paper provide PCA based image fusion and also focuses on image fusion algorithm based on wavelet transform to improve resolution of images in which two images to be fused are firstly decomposed then algorithm works and again reconstructed in the result images. Deepak Kumar Sahu, M.P. Parsai [5] presented the literature review on some of the important image fusion techniques like Primitive fusion(Averaging method, Select Maximum, Select Minimum), fusion based on Discrete Wavelet transform, Principal component analysis(PCA) based fusion etc. Zhu Mengyu, Yang Yuliang [6] presented an image fusion algorithm based on fuzzy logic and wavelet transform. Pixel level image fusion algorithms have been analyzed using visible and infrared image fusion and a new algorithm is proposed based on Discrete Wavelet Transform(DWT) and Fuzzy Logic.
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Image Fusion of Brain Images using Redundant Discrete Wavelet Transform

Image Fusion of Brain Images using Redundant Discrete Wavelet Transform

Image fusion has the very wider scope in medical sciences. Medical Images are obtained form different type of equipments and are of different modalities, each of them carries altogether different information. Especially study of brain images and its features is of greater interest for doctors since several centuries. Now because of radiology and evolution computers made this possible to look in to head online. This posed several challenges for software engineers to produce the good quality images or stream of images. Since medical images are from different modalities, which made it difficult to produce a single image from all these images. With the help of several image processing algorithms it is now possible to fuse the images. This gave rise to another challenge for producing efficient algorithm. This paper proposes the Redundant discrete wavelet transform (RDWT) based algorithm for image fusion, and compares with the other DWT based methods. These methods are assessed on the basis of statistical measures such as entropy, mean and standard deviation. According to the assessment made, it is found that the proposed method is giving better results. The Brain atlas based images are considered as input.
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Review of Image Fusion Techniques

Review of Image Fusion Techniques

DWT techniques have number of disadvantages such as they need number of convolution calculations, require more memory resources and takes much time, which hinder its applications for resource constrained battery powered visual sensor nodes[7]. DCT based fusion methods need less energy as compare to the DWT techniques thus it is appropriate to use DCT fusion methods for resource constrained devices. As computational energy required is less than the transmission energy, data is compressed and fused before transmission in automated battlefields where the robots collect image data from sensor network. In this technique input images and fused images both are coded in JPEG (Joint Photographic Experts Group) format. Contrast sensitivity method is used to form the fused image. The contrasts of the consequent AC (Alternating current) coefficients of different blurred images are compared and the AC coefficient having the largest value is particularly chosen as the AC coefficient for the image formed after fusion. DCT representation of the fused image is found by calculating the average of all the DCT representations of all the input images but it has unwanted blurring effects which decreases the quality of the fused image.
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Image Fusion with Probabilistic Approach

Image Fusion with Probabilistic Approach

In our case of image fusion, we used 5 images: two synthetic and three real. To highlight the steps of melting and evaluate our method, we applied two types of noises and sound the same values of different values on different synthetic images. Once obtained, these noisy images are used for melting. The resulting images of the merger have mals placed pixels, this is why we applied for windows of different sizes: 3X3, 5X5, 7X7 and 9X9 to approximate the original image. Multifocal images used in the present work do not undergo any pretreatment.
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