Fast and Accurate Background Reconstruction Using Background Bootstrapping

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using acceleration with a graphics processing unit (GPU) and a Python implementation.

Standardized Judgment Method of Shooting Training Action Based on Digital Video Technology

Aiming at the difficulty of standardizing the action of basketball shooting training, a new method of standardizing the action of basketball shooting training is proposed based on digital video technology. The digital video signal representation, video sequence coding data structure, and video sequence compression coding method are analyzed, and the pixels of basketball shooting training action position space are sampled to collect basketball shooting training images. The time difference method is used to extract the movement target of basketball shooting training from a digital video sequence. Based on digital video technology, the initial background image is estimated, and the update rate is introduced to update the background estimation image. According to the pixel value sequence of the basketball shooting training image, the pixel model of the basketball shooting training image is defined and modified. By judging whether the defined pixel value matches the background parameter model, the standardization of shooting training can be realized. The experimental results show that the proposed method has good stability, high precision, and short time in determining the standardization of shooting movement, can correct the wrong shooting movement in real time, and can effectively guide basketball shooting training.

The role of multiparametric magnetic resonance in bladder cancer management

Multiparametric magnetic resonance imaging with VI-RADS is a newly discussed method of diagnosing bladder cancer. There are more studies suggesting implementation of mpMRI with VI-RADS to the modern scheme of treating bladder cancer. It requires much more observation and trials to give a final recommendations. The aim of the summary is to present VI-RADS scale and possibilities that appear with the method. Many studies, that were made by departments of urology or radiology, showed promising results. Background: estimation of bladder cancer depends on proper tumor staging, grading and assessment of its biological potential. It is provided by a multimodal approach using clinical, histopathological and radiological methods. Development of MRI provides the best imaging technique for locoregional staging in several other tumors. Lately it was adjusted in BCa preoperative evaluation leading to significant improvement in differentiating patients with NMIBCs and MIBCs. Objective: this article aims to approximate the fundamentals of MRI in BCa and to provide an overview of the available data on the role of VI-RADS score in the diagnostic pathway of bladder cancer.

Robust and unbiased estimation of the background distribution for automated quantitative imaging

Quantitative analysis of high-throughput microscopy images requires robust automated algorithms. Background estimation is usually the first step and has an impact on all subsequent analysis, in particular for foreground detection and calculation of ratiometric quantities. Most methods recover only a single background value, such as the median. Those that aim to retrieve a background distribution by dividing the intensity histogram yield a biased estimation in images in non-trivial cases. In this work, we present the first method to recover an unbiased estimation of the background distribution directly from an image and without any additional input. Through a robust statistical test, our method leverages the lack of local spatial correlation in background pixels to select a subset of pixels that accurately represent the background distribution. This method is both fast and simple to implement, as it only uses standard mathematical operations and an averaging filter. Additionally, the only parameter, the size of the averaging filter, does not require fine tuning. The obtained background distribution can be used to test for foreground membership of individual pixels, or to estimate confidence intervals in derived quantities. We expect that the concepts described in this work can help to develop a novel family of robust segmentation methods.

Fast and Accurate Background Reconstruction Using Background Bootstrapping

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using GPU acceleration and a Python implementation.

Study of cosmogenic activation in copper for rare event search experiments

Rare event search experiments using germanium detectors are performed in underground laboratories to minimize the background induced by cosmic rays. However, the cosmogenic activation of cupreous detector components on the ground generates long half-life radioisotopes and contributes to the background level. We measured cosmogenic activation with 142.50 kg of copper bricks after 504 days of exposure at an altitude of 2469.4 m outside the China Jinping Underground Laboratory (CJPL). The specific activities of the cosmogenic nuclides produced in the copper bricks were measured using a low-background germanium gamma-ray spectrometer at CJPL. The production rates at sea level, in units of nuclei/kg/day, were $${18.6 \pm 2.0}$$ 18.6 ± 2.0 for $${^{54}}$$ 54 Mn, $${9.9 \pm 1.3}$$ 9.9 ± 1.3 for $${^{56}}$$ 56 Co, $${48.3 \pm 5.5}$$ 48.3 ± 5.5 for $${^{57}}$$ 57 Co, $${51.8 \pm 2.5}$$ 51.8 ± 2.5 for $${^{58}}$$ 58 Co, and $${39.7 \pm 5.7}$$ 39.7 ± 5.7 for $${^{60}}$$ 60 Co. The measurement will help to constrain cosmogenic background estimation for rare event searches using copper as a detector structure and shielding material. Based on the measured production rates, the impact of the cosmogenic background in cupreous components of germanium detectors on the next generation CDEX-100 experiment was assessed with the expected exposure history above ground.

Search for R-parity-violating supersymmetry in a final state containing leptons and many jets with the ATLAS experiment using $$\sqrt{s} = 13\hbox { TeV}$$ proton–proton collision data

A search for R-parity-violating supersymmetry in final states characterized by high jet multiplicity, at least one isolated light lepton and either zero or at least three b-tagged jets is presented. The search uses $${139}\,{\text {fb}^{-1}}$$ 139 fb - 1 of $$\sqrt{s} = {13}\hbox { TeV}$$ s = 13 TeV proton–proton collision data collected by the ATLAS experiment during Run 2 of the Large Hadron Collider. The results are interpreted in the context of R-parity-violating supersymmetry models that feature gluino production, top-squark production, or electroweakino production. The dominant sources of background are estimated using a data-driven model, based on observables at medium jet multiplicity, to predict the b-tagged jet multiplicity distribution at the higher jet multiplicities used in the search. Machine-learning techniques are used to reach sensitivity to electroweakino production, extending the data-driven background estimation to the shape of the machine-learning discriminant. No significant excess over the Standard Model expectation is observed and exclusion limits at the 95% confidence level are extracted, reaching as high as 2.4 TeV in gluino mass, 1.35 TeV in top-squark mass, and 320 (365) GeV in higgsino (wino) mass.