The PASCAL Visual Object Classes Challenge

In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments also confirm excellent robustness under noise and insensitivity to parameter variations.

Highly accurate optic flow computation with theoretically justified warping

Computational Science and Engineering (CSE) is the multi-disciplinary field of computer-based modelling and simulation for studying scientific phenomena and engineering designs. Modelling and simulation helps to validate theory, and makes it possible to analyse scenarios that would otherwise be too time-consuming, expensive, or dangerous to study by experiment. Data exploration helps to turn numbers into insight which is especially challenging in times of Big Data.

Computational Science and Engineering

In this paper,an iris recognition method using wavelet packet transformation and based on score level fusion by using SVM is proposed.First wavelet packet transformation is used to decomposition normalized iris image for 2 levels,and the feature vectors are selected from the area with the highest energy values and the second highest,then two calculated hamming distance are input to SVM in order to reduce the dimension of SVM and improve veracity.The experiment indicates that proposed approach provides a good result for the iris recognition.

Iris recognition based on score level fusion by using SVM

In the engineering field, excessive data dimensions affect the efficiency of machine learning and analysis of the relationships between data or features. To render feature dimensionality reduction more effective and faster, this paper proposes a new feature dimensionality reduction approach combining a sampling survey method with a heuristic intelligent optimization algorithm. Drawing on feature selection, this method builds a feature-scoring system and a reduced-dimension length-scoring system based on the sampling survey method. According to feature scores and reduced-dimension lengths, the method selects a number of features and reduced-dimension lengths that are ranked in the front with high scores. This feature dimensionality reduction method allows for in-depth optimal selection of features and reduced-dimension lengths with high scores using an improved heuristic intelligent optimization algorithm. To verify the effectiveness of the dimensionality reduction method, this paper applies it to road roughness time-domain estimation based on vehicle dynamic response and gene-selection research in bioengineering. Results in the first case show that the proposed method can improve the accuracy of road roughness time-domain estimation to above 0.99 and reduce measured data of the vehicle dynamic response, reducing the experimental workload significantly. Results in the second case show that the method can select a set of genes quickly and effectively with high disease recognition accuracy.

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A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering

We mathematically and experimentally evaluate the validity of dimension-reduction methods for the computation of similarity in image pattern recognition. Image pattern recognition identifies instances of particular objects and distinguishes differences among images. This recognition uses pattern recognition techniques for the classification and categorisation of images. In numerical image pattern recognition techniques, images are sampled using an array of pixels. This sampling procedure derives vectors in a higher-dimensional metric space from image patterns. To ensure the accuracy of pattern recognition techniques, the dimension reduction of the vectors is an essential methodology since the time and space complexities of processing depend on the dimension of the data. Dimension reduction causes information loss of topological and geometrical features of image patterns. Through both theoretical and experimental comparisons, we clarify that dimension-reduction methodologies that preserve the topology and geometry in the image pattern space are essential for linear pattern recognition. For the practical application of methods of dimension reduction, the random projection works well compared with downsampling, the pyramid transform, the two-dimensional random projection, the two-dimensional discrete cosine transform and nonlinear multidimensional scaling if we have no a priori information on the input data.

Dimension Reduction and Construction of Feature Space for Image Pattern Recognition

We clarify the mathematical equivalence between low-dimensional singular value decomposition and low-order tensor principal component analysis for two- and three-dimensional images. Furthermore, we show that the two- and three-dimensional discrete cosine transforms are, respectively, acceptable approximations to two- and three-dimensional singular value decomposition and classical principal component analysis. Moreover, for the practical computation in two-dimensional singular value decomposition, we introduce the marginal eigenvector method, which was proposed for image compression. For three-dimensional singular value decomposition, we also show an iterative algorithm. To evaluate the performances of the marginal eigenvector method and two-dimensional discrete cosine transform for dimension reduction, we compute recognition rates for six datasets of two-dimensional image patterns. To evaluate the performances of the iterative algorithm and three-dimensional discrete cosine transform for dimension reduction, we compute recognition rates for datasets of gait patterns and human organs. For two- and three-dimensional images, the two- and three-dimensional discrete cosine transforms give almost the same recognition rates as the marginal eigenvector method and iterative algorithm, respectively.

Pattern recognition in multilinear space and its applications: mathematics, computational algorithms and numerical validations

We clarify the equivalence between second-order tensor principal component analysis and two-dimensional singular value decomposition. Furthermore, we show that the two-dimensional discrete cosine transform is a good approximation to two-dimensional singular value decomposition and classical principal component analysis. Moreover, for the practical computation in two-dimensional singular value decomposition, we introduce the marginal eigenvector method, which was proposed for image compression. To evaluate the performances of the marginal eigenvector method and two-dimensional discrete cosine transform for dimension reduction, we compute recognition rates for image patterns. The results show that the marginal eigenvector method and two-dimensional discrete cosine transform have almost the same recognition rates for images in six datasets.

Low-Dimensional Tensor Principle Component Analysis

[Problem] To make it possible to more accurately distinguish continuous rhonchi from discontinuous rales in lung sounds of humans, etc. [Solution] The biological sound detection signal processing device (90) comprises a robust principal component analysis unit (40), a continuous sound processing unit (20), and a discontinuous sound processing unit (30). The robust principal component analysis unit (40) receives original biological sounds with an input section (12), performs Fourier transforms in a Fourier transform section (14), and performs robust principal component analysis of original sound matrices generated by a matrix-generating section (16). When a sparse matrix obtained from robust principal component analysis is processed with the continuous sound processing unit (20), continuous biological sounds are obtained from the original sounds. When a low rank matrix obtained from robust principal component analysis is processed with the discontinuous sound processing unit (30), biological sounds in which continuous biological sounds have been excluded from the original sounds are obtained.

Biological sound signal processing device, biological sound signal processing method, and biological sound signal processing program

The purpose of this paper is twofold. First, we introduce fast global image registration using random projection. By generating many transformed images as entries in a dictionary from a reference image, nearest-neighbour-search NNS-based image registration computes the transformation that establishes the best match among the generated transformations. For the reduction in the computational cost for NNS without a significant loss of accuracy, we use random projection. Furthermore, for the reduction in the computational complexity of random projection, we use the spectrum-spreading technique and circular convolution. Second, for the reduction in the space complexity of the dictionary, we introduce an interpolation technique into the dictionary using the linear subspace method and a local linear property of the pattern space.

Tomoya Sakai

Papers

Dimension Reduction and Construction of Feature Space for Image Pattern Recognition

Pattern recognition in multilinear space and its applications: mathematics, computational algorithms and numerical validations

Low-Dimensional Tensor Principle Component Analysis

Biological sound signal processing device, biological sound signal processing method, and biological sound signal processing program

Global Image Registration Using Random Projection and Local Linear Method