Computer vision in sports analytics is gaining in popularity. Monitoring players\' performance using cameras is more flexible and does not interfere with player equipment compared to systems using sensors. This provides a wide set of opportunities for computer vision systems that help coaches, reporters, and audiences. This paper provides an introduction to the problem of measuring boxers\' performance, with a comprehensive survey of approaches in current science. The main goal of the paper is to provide a system to automatically detect punches in Olympic boxing using a single static camera. The authors use Euclidean distance to measure the distance between boxers and convolutional neural networks to classify footage frames. In order to improve classification performance, we provide and test three approaches to manipulating the images prior to fitting the classifier. The proposed solution achieves 95% balanced accuracy, 49% F1 score for frames with punches, and 97% for frames without punches. Finally, we present a working system for analyses of a boxing scene that marks boxers and labelled frames with detected clashes and punches.

An innovative dual-thickness semi-transparent beamstop designed to enhance the performance of small-angle X-ray scattering (SAXS) experiments is introduced. This design integrates two absorbers of differing thicknesses side by side into a single attenuator, known as a beamstop. Instead of completely stopping the direct beam, it attenuates it, allowing the SAXS detector to measure the transmitted beam through the sample. This approach achieves true synchronization in measuring both scattered and transmitted signals and effectively eliminates higher-order harmonic contributions when determining the transmission light intensity through the sample. This facilitates and optimizes signal detection and background subtraction. This contribution details the theoretical basis and practical implementation of this solution at the SAXS station on the 1W2A beamline at the Beijing Synchrotron Radiation Facility. It also anticipates its application at other SAXS stations, including that at the forthcoming High Energy Photon Source, providing an effective solution for high-precision SAXS experiments.

The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging.

Skin cancer affects people of all ages and is a common disease. The death toll from skin cancer rises with a late diagnosis. An automated mechanism for early-stage skin cancer detection is required to diminish the mortality rate. Visual examination with scanning or imaging screening is a common mechanism for detecting this disease, but due to its similarity to other diseases, this mechanism shows the least accuracy. This article introduces an innovative segmentation mechanism that operates on the ISIC dataset to divide skin images into critical and non-critical sections. The main objective of the research is to segment lesions from dermoscopic skin images. The suggested framework is completed in two steps. The first step is to pre-process the image; for this, we have applied a bottom hat filter for hair removal and image enhancement by applying DCT and color coefficient. In the next phase, a background subtraction method with midpoint analysis is applied for segmentation to extract the region of interest and achieves an accuracy of 95.30%. The ground truth for the validation of segmentation is accomplished by comparing the segmented images with validation data provided with the ISIC dataset.

Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called PARSEG, which stands for PArtitioning, Random Selection, Estimation, and Generalization; being the basic steps within this procedure. Suggested method enables us to perform statistical validation of binary images by selecting the minimum number of pixels from the original image to be used for validation without deteriorating the effectiveness of the validation procedure. It utilizes binary classifiers to accomplish image validation and selects the optimal sample of pixels according to a specific objective function. As a result, the computational complexity of the validation experiment is substantially reduced. The procedure\'s effectiveness is illustrated by considering images composed of approximately 13 million pixels from the field of seed recognition. PARSEG provides roughly the same precision of the validation process when extended to the entire image, but it utilizes only about 4% of the original number of pixels, thus reducing, by about 90%, the computing time required to validate a binary segmented image.

Endogenous therapeutic analytes include hormones, neurotransmitters, vitamins, fatty acids and inorganic elements that are naturally present in the body because either the body produces them or they are present in the normal diet. The accurate measurement of endogenous therapeutic analytes poses a challenge when the administered exogenous therapeutic analyte and its endogenous counterpart cannot be distinguished. In this article, real case examples with endogenous therapeutic analyte bioanalysis during drug development in support of regulatory submissions are collected and presented. The article highlights common challenges encountered and lessons learned related to bioanalysis of endogenous therapeutic analytes and provides practical tips and strategies to consider from a regulatory perspective.

Application of an optical method for the identification of antiresorptive drug-related osteonecrosis of the jaw (ARONJ).
We introduce shifted-excitation Raman difference spectroscopy followed by U-Net deep neural network refinement to determine bone tissue viability. The obtained results are validated through established histological methods.
Discrimination of osteonecrosis from physiological tissues was evaluated at 119 distinct measurement loci in 40 surgical specimens from 28 patients. Mean Raman spectra were refined from 11,900 raw spectra, and characteristic peaks were assigned to their respective molecular origin. Then, following principal component and linear discriminant analyses, osteonecrotic lesions were distinguished from physiological tissue entities, such as viable bone, with a sensitivity, specificity, and overall accuracy of 100%. Moreover, bone mineral content, quality, maturity, and crystallinity were quantified, revealing an increased mineral-to-matrix ratio and decreased carbonate-to-phosphate ratio in ARONJ lesions compared to physiological bone.
The results demonstrate feasibility with high classification accuracy in this collective. The differentiation was determined by the spectral features of the organic and mineral composition of bone. This merely optical, noninvasive technique is a promising candidate to ameliorate both the diagnosis and treatment of ARONJ in the future.

The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced.

The local structural characterization of iron oxide nanoparticles is explored using a total scattering analysis method known as pair distribution function (PDF) (also known as reduced density function) analysis. The PDF profiles are derived from background-corrected powder electron diffraction patterns (the e-PDF technique). Due to the strong Coulombic interaction between the electron beam and the sample, electron diffraction generally leads to multiple scattering, causing redistribution of intensities towards higher scattering angles and an increased background in the diffraction profile. In addition to this, the electron-specimen interaction gives rise to an undesirable inelastic scattering signal that contributes primarily to the background. The present work demonstrates the efficacy of a pre-treatment of the underlying complex background function, which is a combination of both incoherent multiple and inelastic scatterings that cannot be identical for different electron beam energies. Therefore, two different background subtraction approaches are proposed for the electron diffraction patterns acquired at 80 kV and 300 kV beam energies. From the least-square refinement (small-box modelling), both approaches are found to be very promising, leading to a successful implementation of the e-PDF technique to study the local structure of the considered nanomaterial.

Concerning the problem that wafer surface defects are easily confused with the background and are difficult to detect, a new detection method for wafer surface defects based on background subtraction and Faster R-CNN is proposed. First, an improved spectral analysis method is proposed to measure the period of the image, and the substructure image can then be obtained on the basis of the period. Then, a local template matching method is adopted to position the substructure image, thereby reconstructing the background image. Then, the interference of the background can be eliminated by an image difference operation. Finally, the difference image is input into an improved Faster R-CNN network for detection. The proposed method has been validated on a self-developed wafer dataset and compared with other detectors. The experimental results show that compared with the original Faster R-CNN, the proposed method increases the mAP effectively by 5.2%, which can meet the requirements of intelligent manufacturing and high detection accuracy.