Comparison and improvement of wavelet‐based image fusion

The wavelets used in image fusion can be categorized into three general classes: orthogonal, biorthogonal, and non‐orthogonal. Although these wavelets share some common properties, each wavelet also has a unique image decomposition and reconstruction characteristic that leads to different fusion results. This paper focuses on the comparison of the image‐fusion methods that utilize the wavelet of the above three general classes, and theoretically analyses the factors that lead to different fusion results. Normally, when a wavelet transformation alone is used for image fusion, the fusion result is not good. However, if a wavelet transform and a traditional fusion method, such as an IHS transform or a PCA transform, are integrated, better fusion results may be achieved. Therefore, this paper also discusses methods to improve wavelet‐based fusion by integrating an IHS or a PCA transform. As the substitution in the IHS transform or the PCA transform is limited to only one component, the integration of the wavelet transform with the IHS or PCA to improve or modify the component, and the use of IHS or PCA transform to fuse the image, can make the fusion process simpler and faster. This integration can also better preserve colour information. IKONOS and QuickBird image data are used to evaluate the seven kinds of wavelet fusion methods (orthogonal wavelet fusion with decimation, orthogonal wavelet fusion without decimation, biorthogonal wavelet fusion with decimation, biorthogonal wavelet fusion without decimation, wavelet fusion based on the ‘à trous’, wavelet and IHS transformation integration, and wavelet and PCA transformation integration). The fusion results are compared graphically, visually, and statistically, and show that wavelet‐integrated methods can improve the fusion result, reduce the ringing or aliasing effects to some extent, and make the whole image smoother. Comparisons of the final results also show that the final result is affected by the type of wavelets (orthogonal, biorthogonal, and non‐orthogonal), decimation or undecimation, and wavelet‐decomposition levels.     

Speech emotion recognition based on multi‐feature and multi‐lingual fusion

Multimedia Tools and Applications - A speech emotion recognition algorithm based on multi-feature and Multi-lingual fusion is proposed in order to resolve low recognition accuracy caused bylack of...     

Classification of image distortion based on the generalized Benford’s law

Multimedia Tools and Applications - Distortion classification is an important step in blind image quality assessment. In this paper, a new image distortion classification algorithm is presented....     

A dual-branch model for diagnosis of Parkinson’s disease based on the independent and joint features of the left and right gait

Applied Intelligence - Clinical diagnosis of Parkingson’s disease (PD) requires the physician to assess the patient’s gait and other symptoms. A dual-branch model is proposed in this...     

Human Face Recognition and the Face Image Set′s Topology

If we consider an n × n image as an n²-dimensional vector, then images of faces can be considered as points in this n²-dimensional image space. Our previous studies of physical transformations of the face, including translation, small rotations, and illumination changes, showed that the set of face images consists of relatively simple connected subregions in image space. Consequently linear matching techniques can be used to obtain reliable face recognition. However, for more general transformations, such as large rotations or scale changes, the face subregions become highly non-convex. We have therefore developed a scale-space matching technique that allows us to take advantage of knowledge about important geometrical transformations and about the topology of the face subregion in image space. While recognition of faces is the focus of this paper, the algorithm is sufficiently general to be applicable to a large variety of object recognition tasks     

Study on the influencing factors of the flight crew’s TSA based on DEMATEL–ISM method

Cognition, Technology & Work - It is increasingly being recognized that the flight crew’s team situation awareness (TSA) is essential for flight safety. To explore the inherent...     

Transformation of feature space based on Fisher’s linear discriminant

Linear transformation of data in multidimensional feature space based on Fisher’s criterion is considered. The case of two classes with arbitrary distributions is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Example offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher’s linear discriminant.     

Foundations of information technology based on Bunge’s systemist philosophy of reality

Software and Systems Modeling - General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies...     

Note on the Structure of Kruskal’s Algorithm

We study the merging process when Kruskal’s algorithm is run with random graphs as inputs. Our aim is to analyze this process when the underlying graph is the complete graph on n vertices lying in [0,1] ^d , and edge set weighted with the Euclidean distance. The height of the binary tree explaining the merging process is proved to be Θ(n) on average. On the way to the proof, we obtain similar results for the complete graph and the d-dimensional square lattice with i.i.d. edge weights.     

Geometrically robust video water-marking based on wavelet transform

1 Introduction Compared with relevant analog data, digital media (digital audio, image, video, etc.) have some significant properties: high quality, easy manipulation, perfect copying and easy transmission. Because of these good properties, the development and application of digital media are growing explosively. However, these properties also pose a threat of unauthorized possession and illegal usage of digital media. An additional threat is the illegal tampering and modification of digital m…     

Implementation of enhanced canny recognition algorithm and non – natural neural system based speech fusion for sightless persons

Along with the rapid development of information technology, the sightless persons have numerous visual difficulties in doing their day by day activities. In this technological world, we have many resources for the human to live their life, still blind peoples suffers a lot for their survival in this hi-tech world. Through computing the solution can be obtained for their independent survival to the blind peoples. This paper implements a powerful speech fusion system with to support the sightless persons. This system produces the text image documents as voice.

     

Research on construction and application of gene network model for form design based on consumer’s preference

With developments in science and technology, product innovation is the key to enterprise performance in the competitive market. In consumer demand-oriented modern product design, effectively combining product design with the esthetic perceptions and preferences of consumers is a problem that urgently needs to be addressed. However, the traditional design method is completely dominated by designers without user participation. The purpose of this study was to give designers an impetus for restructuring and upgrading a design. A gene network design model was developed with the advantages of a gene network and neural network. This allowed the target elements of a product design to be obtained using a nonlinear network. Finally, a case study was used to show the detailed procedure of the design model. To reveal the advantages of the proposed model, it was compared with other methods such as a gene-based design method, an emotional design method, and a fuzzy Kano design method. The results showed that the proposed model was more efficient and scientific, and provided consumers with a multidimensional evaluation system to determine the optimal design schemes.     

Development of dense models of the Earth’s surface based on the semiglobal matching method for the case of multiple overlapping images

A method is developed for constructing a three-dimensional digital surface model based on the use of aerial images with multiple overlapping. The specific feature of this study is the use of the multiview matching method instead of stereo matching. The method is based on adapting the energy aggregation algorithm, which was proposed in the semiglobal matching (SGM) method, to the object space, as well as using the one-to-many scheme of cost calculation. The reconstructed scene is represented as a voxel grid. A high-performance implementation of the digital surface model construction at all stages is proposed based on the massive parallelization of computations on a graphics processing unit.     

Prediction of tropospheric ozone concentrations: Application of a methodology based on the Darwin’s Theory of Evolution

This study aims to predict the next day hourly average tropospheric ozone (O₃) concentrations using genetic programming (GP). Due to the complexity of this problem, GP is an adequate methodology as it can optimize, simultaneously, the structure of the model and its parameters. It is an artificial intelligence methodology that uses the same principles of the Darwinian Theory of Evolution. GP enables the automatic generation of mathematical expressions that are modified following an iterative process applying genetic operations.The inputs of the models were the hourly average concentrations of carbon monoxide (CO), nitrogen oxide (NO), nitrogen dioxide (NO₂) and O₃, and some meteorological variables (temperature – T; solar radiation – SR; relative humidity – RH; and wind speed – WS) measured 24 h before. GP was also applied to the principal components (PC) obtained from these variables. The analysed period was from May to July 2004 divided in training and test periods.GP was able to select the most relevant variables for prediction of O₃ concentrations. The original variables, T, RH and O₃ measured 24 h before were considered significant inputs for prediction. The selected PC had also important contributions of the same variables and of NO₂. GP models using the original variables presented better performance in training period and worse performance in test period when compared with the models obtained using PC. The results achieved using the GP methodology demonstrated that it can be very useful to solve several environmental complex problems.     

Impact of IT integration on the firm’s knowledge absorption and desorption

This paper examines the impact of the integration of IT systems with other organizations (IT integration) on the knowledge absorption and desorption of the focal firm. We ran an empirical study in Spain and found that IT integration enables the firm to absorb and desorb knowledge with other organizations, which in turn improves firm performance. This research provides two key contributions to the IS discipline: 1) We introduce the concept of desorptive capacity in the IS research and provide a scale for its measure, 2) we provide a theory of IT integration impact on the firm’s knowledge absorption and desorption.     

Synthesis of stabilizing spacecraft control based on generalized Ackermann’s formula

Problem of modal synthesis of controllers and observers using the generalized Ackermann’s formula is solved for a spacecraft as a complex dynamic system with high interconnections. All possible controller matrices (the whole set of controllers) are obtained for solution of the problem of stabilization of orbital orientation of the spacecraft in inseparable channels of bank and yaw angles.     

Early diagnosis of Alzheimer’s disease based on Partial Least Squares and Support Vector Machine

An accurate and early diagnosis of the Alzheimer’s disease (AD) is of fundamental importance for the patient medical treatment. It has been shown that pathological manifestations of AD may be detected thought functional images even before that the patients becomes symptomatic. This fact has led researchers to propose new ways for analyzing functional data in order to get more accurate Computer-Aided Diagnosis (CAD) systems for this disorder. In this paper we show an effective approach for Single Photon Emission Computed Tomography feature extraction that improves the accuracy of CAD systems for AD. The proposed methodology uses a Partial Least Squares algorithm for extracting score vectors and the Out-Of-Bag error for selecting a number of scores that are used as features. Subsequently, a Support Vector Machine (SVM) based classifier determines the underlying class of the images, thus performing diagnostics. In order to test this approach we have used an image database for AD with 97 SPECT images from controls and AD patients. The images were visually labeled by experienced clinicians after the properly normalization. Several experiments have been developed to compare the proposed methodology and previous approaches. The results show that our method yields accuracy rates over 90%, outperforming several recently reported CAD systems for AD diagnosis.