As the critical elements of scientific literature, mathematical formulas contribute significantly to the effectiveness of literature retrieval. Existing formula-based retrieval methods of scientific literature primari...
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Self-supervised learning has gained significant attention in contemporary applications, particularly due to the scarcity of labeled data. While existing SSL methodologies primarily address feature variance and linear ...
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作者：
Tal, YRafael
Computer vision and image processing section
A direct relation between the charge density of a free atom, rho(a)(r), and the cohesive energy of the corresponding metal is proposed, This relation is based on an approximation for the metallic charge density, rho(m...
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A direct relation between the charge density of a free atom, rho(a)(r), and the cohesive energy of the corresponding metal is proposed, This relation is based on an approximation for the metallic charge density, rho(m)(r), that is constructed from rho(a)(r) through rho(m)(r) congruent to rho(a)(r) + <(rho)over bar> where <(rho)over bar> = Ohm(0)(-1) integral(R0)(infinity) rho(a)(r)d(3)r, Ohm(0) being the atomic volume of the metallic atom, and R(0) the corresponding Wigner-Seitz radius. The cohesive energy E(coh) is then related to <(rho)over bar> through E(coh) congruent to (3/10)(3 pi(2))(2/3)Ohm(0 rho)(-5/3). A systematic study of 29 metallic elements including the 3d and 4d transition elements shows that the proposed relation is, in general, at least as accurate as recent ab initio results. In the same fashion, an expression for the metallic bulk modulus is derived. This expression requires, in addition to <(rho)over bar>, the values of rho(a)(R(0)) and its first derivative rho'(a)(R(0)). The computed bulk moduli are, again, at least as good as the ab initio ones for the set of metallic elements studied.
images are important for many biomedical applications. Here, the authors focus on the feature-extraction part of the image analysis process. The following topics are dealt with: feature vectors and feature spaces; bin...
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images are important for many biomedical applications. Here, the authors focus on the feature-extraction part of the image analysis process. The following topics are dealt with: feature vectors and feature spaces; binary object features; histogram features; color features; spectral features; feature extraction using CVIPtools; analysis/preprocessing.
A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage...
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A 1.5V resistive fuse for image smoothing and segmentation using bulk-driven MOSFETs is presented. The circuit switches on only if the differential voltage applied across its input terminals is less than a set voltage;it switches off if the differential voltage is higher than the set value. The useful operation range of the circuit is 0.4V with a supply voltage of 1.5V and threshold voltages of V-Tn = 0.828V and V-Tp = -0.56V for n and g channel MOSFETs, respectively.
This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate i...
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This paper presents a texture segmentation algorithm based on a hierarchical wavelet decomposition. Using Daubechies' four-tap filter, an original image is decomposed into three detail images and one approximate image. The decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.
A new class of morphological filters is proposed for image enhancement. The filter, known as the generalized morphological filter (GMF), uses multiple structuring elements and combines linear and morphological operati...
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A new class of morphological filters is proposed for image enhancement. The filter, known as the generalized morphological filter (GMF), uses multiple structuring elements and combines linear and morphological operations. The GMF can be designed to suppress various types of noise yet preserve geometrical structure in an image. A study of several aspects of the performance of the filter is presented. The study includes geometrical feature preservation, noise suppression, structuring element selection, and the root signal structure. For the sake of comparison, averaging and median filters are also used in the experiments and corresponding figures of merit of the performance of the filter. The empirical study shows that the generalized morphological filter possesses effective noise suppression with reduced geometrical feature blurring.
This paper discusses the relationship between the zero crossings or zeros of band-limited signals and their nonlinear transformations. It is proved that the bandwidth of a signal can be compressed by a ratio of 1/ n i...
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This paper discusses the relationship between the zero crossings or zeros of band-limited signals and their nonlinear transformations. It is proved that the bandwidth of a signal can be compressed by a ratio of 1/ n if and only if the signal has n th-order zero crossings or zeros (if complex). Also, a monotonic nonlinearity in the observation of a band-limited signal can be identified from the zero crossings (or zeros) of the derivative of the observed signal. (The results are for one-dimensional signals. Extensions to two-dimensional signals remain to be addressed.)
We address the problem of reconstructing a smooth curve from sparse and noisy information that is invariant to the choice of the coordinate system. Tikhonov regularization is used to form a well-posed mathematical pro...
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We address the problem of reconstructing a smooth curve from sparse and noisy information that is invariant to the choice of the coordinate system. Tikhonov regularization is used to form a well-posed mathematical problem statement, and conditions for an invariant reconstruction are given. The resulting functional minimization problem is shown to be nonconvex. Approximations to the invariant functional are often used to form a convex problem that can be solved efficiently. Two common approximations, those of cubic and weighted cubic splines, are detailed, and examples are given to show that the approximations are often invalid. To form a valid approximation to the invariant functional we propose a two-step algorithm. The first step forms a piecewise-linear curve, which is invariant to the coordinate system. This piecewise-linear curve is then used to construct a parameterization of the curve for which we can make a valid approximation to the invariant functional. Examples are given to demonstrate the effectiveness of the algorithm, and two example applications for which the invariant property is important are given.
作者：
Mostafa G MostafaAly A FaragEdward EssockComputer Vision and Image Processing Laboratory
Dept. of Electrical and Computer Engineering University of Louisville Louisville KY 40292 USAComputer Vision and Image Processing Laboratory Dept. of Electrical and Computer Engineering University of Louisville Louisville KY 40292 USADept. of Psychology University of Louisville Louisville kY 40292 USA
Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty rem...
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Multimodality image registration and fusion are essential steps in building 3-D models from remotesensing data. We present in this paper a neural network technique for the registration and fusion of multimodali-ty remote sensing data for the reconstruction of 3-D models of terrain regions. A FeedForward neural network isused to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data arepresented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusionresults.
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