PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

Diffusion gravity and its consequences

Diffusion quantum mechanics (DQM), proposed recently (Zakir, 2020-21), describes a conservative diffusion of classical particles in a fluctuating classical scalar field and, in a homogeneous field, derives the formalism of quantum mechanics. In an inhomogeneous scalar field, DQM reproduces gravitation, and in the present paper, the following theory of diffusion gravity and its various consequences are considered. In DQM a part of the energy of the scalar field is transferred to particles as their fluctuation energy (“thermal” energy), appearing as their rest energy (mass). The resulting local decrease in the field’s energy density around a macroscopic body generates “thermal” diffusion flux of particles to this region. The properties of this “thermal” part of conservative diffusion are similar to gravitation. A high matter concentration in some region reduces the local energy density of scalar field sufficiently to reduce the local intensity of fluctuations. Due to the conservativity of diffusion, the increments in the drift velocity of particles are cumulative, and “thermal” diffusion acceleration arises, independent on the particle’s mass. The world lines become curved, and all processes with particles slowdown, which means time dilation. On hypersurfaces of simultaneity t = const, where the scalar field is defined, effective metrics, connection, and curvature arise. They obey to Einstein’s equations following from balance between energies of matter and background scalar field.

Detection of Small Moving Objects in Long Range Infrared Videos from a Change Detection Perspective

Detection of small moving objects in long range infrared (IR) videos is challenging due to background clutter, air turbulence, and small target size. In this paper, we present two unsupervised, modular, and flexible frameworks to detect small moving targets. The key idea was inspired by change detection (CD) algorithms where frame differences can help detect motions. Our frameworks consist of change detection, small target detection, and some post-processing algorithms such as image denoising and dilation. Extensive experiments using actual long range mid-wave infrared (MWIR) videos with target distances beyond 3500 m from the camera demonstrated that one approach, using Local Intensity Gradient (LIG) only once in the workflow, performed better than the other, which used LIG in two places, in a 3500 m video, but slightly worse in 4000 m and 5000 m videos. Moreover, we also investigated the use of synthetic bands for target detection and observed promising results for 4000 m and 5000 m videos. Finally, a comparative study with two conventional methods demonstrated that our proposed scheme has comparable performance.

US Virginia governor race will be closely watched

Significance Taking place in a genuinely competitive two-party state, this race offers a rare guide to voter attitudes ahead of the midterm congressional elections in November 2022 as the candidates address issues that are playing out in national as well as state-level politics. Impacts The Republicans hope that a business executive turned novice politician who promises to create jobs can still win votes. The Democrats are betting that a post-pandemic faith in government to provide solutions will prevail over calls for lower taxes. Two other issues with local intensity but national resonance are the funding of local school systems and approaches to policing.

Spectral Hump Formation in Visible Region of Supercontinuum from Shaped Femtosecond Laser Filamentation in Fused Silica

We investigate experimentally the local intensity control in the visible region of the supercontinuum (SC) generated from femtosecond laser filamentation in fused silica by using pulse shaping technology. Based on the genetic algorithm, we show that a distinct spectral hump at any preset wavelength can be formed in the blue-side extension. The local intensity control in the SC could improve the abilities of the SC applications.

Novel Filtering Methods for Image and Video Processing Applications

During the last few years, digital filtering methods for image/video processing applications have reached a satisfactory level. However, their performance degrades in the presence of noise, trend, motion, shape deformation, intensity inhomogeneity, shadows, or low image quality, to name a few. To cope with these challenges, this dissertation presents novel filtering methods for image/video processing applications that outperform the existing and state-of-the-art methods. The dissertation starts by introducing a novel trend filtering method that transforms the inter-frame registration problem into low complexity trend filtering problem. In the proposed method, Laplacian eigenmaps in conjunction with the modified empirical mode decomposition has been used to suppress the noise artifacts and the trend term. In multi-dimensional signals, the trend term is often referred to as non-uniform illumination or global intensity inhomogeneity. This dissertation presents a new filtering method for estimating the global intensity inhomogeneity in two dimensional and volume images. Global intensity inhomogeneity often arises due to the imperfections of data acquisition device, direction of source light, and properties of the subject under study. The proposed method generates a high-pass filter based on the grey-weighted distance transform of the frequency content of an image/volume. It provides an accurate estimation of global intensity inhomogeneity without any parameter tweaking, which makes it applicable to many imaging modalities. The dissertation also presents a filtering methodology to cope with local intensity inhomogeneity that gives rise to shadow artifacts. These artifacts appear as sharp discontinuities and are often corrected at different scales and orientations. The proposed method makes use of decimation-free directional filter bank to suppress the local intensity inhomogeneity and shadow artifacts irrespective of scale and orientation. In addition to intensity inhomogeneity correction, the dissertation also presents a filtering method that utilizes the Gabor filter bank to generate rotation invariant feature codes. The effectiveness of the proposed method has been evaluated in both identification and verification modes for fingerprint recognition. The uniqueness of the presented filtering methods lies in the fact that they are essentially parameter free and can easily be scaled to higher dimensions. This makes them applicable to many different image/video processing applications with least of effort from the end user, i.e., eliminating the user biases.

Novel Filtering Methods for Image and Video Processing Applications

During the last few years, digital filtering methods for image/video processing applications have reached a satisfactory level. However, their performance degrades in the presence of noise, trend, motion, shape deformation, intensity inhomogeneity, shadows, or low image quality, to name a few. To cope with these challenges, this dissertation presents novel filtering methods for image/video processing applications that outperform the existing and state-of-the-art methods. The dissertation starts by introducing a novel trend filtering method that transforms the inter-frame registration problem into low complexity trend filtering problem. In the proposed method, Laplacian eigenmaps in conjunction with the modified empirical mode decomposition has been used to suppress the noise artifacts and the trend term. In multi-dimensional signals, the trend term is often referred to as non-uniform illumination or global intensity inhomogeneity. This dissertation presents a new filtering method for estimating the global intensity inhomogeneity in two dimensional and volume images. Global intensity inhomogeneity often arises due to the imperfections of data acquisition device, direction of source light, and properties of the subject under study. The proposed method generates a high-pass filter based on the grey-weighted distance transform of the frequency content of an image/volume. It provides an accurate estimation of global intensity inhomogeneity without any parameter tweaking, which makes it applicable to many imaging modalities. The dissertation also presents a filtering methodology to cope with local intensity inhomogeneity that gives rise to shadow artifacts. These artifacts appear as sharp discontinuities and are often corrected at different scales and orientations. The proposed method makes use of decimation-free directional filter bank to suppress the local intensity inhomogeneity and shadow artifacts irrespective of scale and orientation. In addition to intensity inhomogeneity correction, the dissertation also presents a filtering method that utilizes the Gabor filter bank to generate rotation invariant feature codes. The effectiveness of the proposed method has been evaluated in both identification and verification modes for fingerprint recognition. The uniqueness of the presented filtering methods lies in the fact that they are essentially parameter free and can easily be scaled to higher dimensions. This makes them applicable to many different image/video processing applications with least of effort from the end user, i.e., eliminating the user biases.

A transverse energy flow at the tight focus of light with higher-order circular-azimuthal polarization

Tight focusing of light with mth-order circular-azimuthal polarization was investigated. This is a new type of inhomogeneous hybrid polarization that combines the properties of mth order cylindrical polarization and circular polarization. Using the Richards-Wolf formalism, we obtained analytical expressions in the focal spot for the projections of the electric and magnetic field, the intensity distribution, the projections of the Poynting vector, and the spin angular momentum. It was shown theoretically and numerically that at the focus, the intensity has 2(m+1) local maxima located on a circle centered on an on-axis intensity null. It was shown that 4m vortices of a transverse energy flow were produced at the focus, with their centers located between the local intensity maxima. It was also shown that in the focal plane, the transverse energy flow changes the direction of rotation 2(2m+1) times around the optical axis. It is interesting that the longitudinal projection of the spin angular momentum at the focus changes sign 4m times. In those areas of the focal plane where the transverse energy flow rotates counterclockwise, the longitudinal projection of the spin angular momentum is positive, and the polarization vector rotates counterclockwise in the focal plane. Conversely, if the energy flow rotates clockwise, the polarization vector rotates clockwise, and the longitudinal projection of the spin angular momentum is negative. Numerical simulations are in agreement with the theoretical investigation.