Rally Counting Method Integrating Image and Audio Processing Based on Table Tennis Match Video Characteristics

This study aims to understand the analysis of credit interest calculation using flat rate and sliding rate method at Kospin Jasa Capem Jatibarang. The data collection methods are literature study, documentation, interviews, and observation. The technique used for data analysis is quantitative description analysis with flat rate and sliding rate method formulas. The research findings show that the flat rate calculation method has a greater interest value than the sliding rate calculation method, both for 12 months and 24 months. The flat rate method is more appropriate for short-term loans, and is suitable for debtors with fixed income, while the sliding rate is more appropriate for long-term loans because the nominal interest decreases.

Multicolour fluorescence imaging by STimulated Emission Depletion (STED) superresolution microscopy with doughnut-shaped STED laser beams based on different wavelengths for each colour channel requires precise image registration. This is especially important when STED imaging is used for co-localisation studies of two or more native proteins in biological specimens to analyse nanometric subcellular spatial arrangements. We developed a robust postprocessing image registration protocol, with the aim to verify and ultimately optimise multicolour STED image quality. Importantly, this protocol will support any subsequent quantitative localisation analysis at nanometric scales. Henceforth, using an approach that registers each colour channel present during STED imaging individually, this protocol reliably corrects for optical aberrations and inadvertent sample drift. To achieve the latter goal, the protocol combines the experimental sample information, from corresponding STED and confocal images using the same optical beam path and setup, with that of an independent calibration sample. As a result, image registration is based on a strategy that maximises the cross-correlation between sequentially acquired images of the experimental sample, which are strategically combined by the protocol. We demonstrate the general applicability of the image registration protocol by co-staining of the ryanodine receptor calcium release channel in primary mouse cardiomyocytes. To validate this new approach, we identify user-friendly criteria, which - if fulfilled - support optimal image registration. In summary, we introduce a new method for image registration and rationally based postprocessing steps through a highly standardised protocol for multicolour STED imaging, which directly supports the reproducibility of protein co-localisation analyses. Although the reference protocol is discussed exemplarily for two-colour STED imaging, it can be readily expanded to three or more colours and STED channels.

NODESTUDIO, A VISUAL PROGRAMMING FRAMEWORK FOR MUSCULOSKELETAL MRI ANALYSIS

Camera calibration is a fundamental process for essential sports analytics tasks, including augmented reality, player tracking, and scene reconstruction. In soccer, camera calibration aims to estimate the geometric relationship between the field and broadcast footage. The international guidelines for soccer fields, however, permit a size variance of up to 1850m2 in soccer fields. This paper investigates whether a generic virtual template can serve as a calibration object for soccer broadcast footage from any internationally approved fields. An experiment is conducted to assess if the Fédération Internationale de Football Association (FIFA) recommended field size can be adapted to fit any internationally-approved field. An initial experiment is conducted with regards to four extreme fields and an arbitrary camera view, after which the experiment is enlarged to cover a thousand camera views for all integer-based allowable field shapes. The direct linear transform is utilised to establish a homography matrix between the generic template and the extreme fields. The initial findings indicate that the generic template can achieve accuracies of at least 93%, as calculated by the standard metric, however, the extended analysis indicated that some arbitrary camera perspectives limit the accuracy to less than 90%. This accuracy metric, however, considers the overall area of the field rather than its distinct segments. Consequently, in addition to the standard metrics employed in the literature, a novel approach is proposed to calculate the combined intersection over union (IoU) as the average IoU per field segment within the visible plane.

Supreme Court justices' opinions shape the contours of case law binding throughout the United States. Importantly though, justices do not write their opinions de novo. Rather, they routinely draw on lower court judges' opinion language when crafting opinions. In doing so, justices stretch the substantive impact of lower court judges' reasoning beyond the boundaries of their circuits. However, justices do not draw equally on lower court opinions; while previous work often ties this to judges' professional qualifications, we draw on work stressing female supervisors are more likely to enforce professional norms on subordinates. We argue female justices are more likely to draw upon lower court opinions complying with professional norms because of greater implicit norm internalization over the course of their careers. We test this proposition with a quantitative textual analysis of the justices' opinions and lower court opinions. We find support for our argument. This raises normative concerns about the overall impact of greater judicial diversity.

Table tennis is China 's national game and the proudest sport in China's sports field. During the research and technology service work of the Chinese table tennis team for many years, it has accumulated a large amount of valuable data on the analysis of skills and tactics of training and matches, match video, training monitoring, and so on. This paper discusses the relevant theory of swarm intelligence algorithm processing big data on the table tennis training competition knowledge interaction platform system, as well as the technical support of Nginx and Tomcat, and determines the technical basis of the table tennis training competition knowledge interaction platform. Through the establishment of the firefly algorithm model, the resource search ability is enhanced, and the traditional firefly algorithm is improved. From the results of the system performance test, it can be found that the improved swarm intelligence algorithm adopted in this paper improves the global convergence, and the load balancing degree gradually decreases with the increase of time. The improved firefly algorithm shows good performance when the bandwidth is low, and the resource occupancy rate is greatly reduced. When the bandwidth is 20, it is reduced by 12.55%. It solves the shortcomings of long time and low success rate, so as to verify the convenience of the system operation and the power of functions and make the platform more intelligent and efficient.

Computer-aided information technologies for medical image processing were developed to increase the accuracy and reliability of early lung and breast cancer diagnosis. These technologies include: a) optimal methods of image processing to emphasise diagnostically important patterns and parts in lung tomograms and mammograms; b) methods of quantitative image analysis to classify the objects of interest; c) verified methodologies for optimal image processing and quantitative analysis; d) a manual with examples of the processed images, description of steps for their analysis with true diagnostic decisions.

We propose a comprehensive soccer match video analysis pipeline tailored for broadcast footage, which encompasses three pivotal stages: soccer field localization, player tracking, and soccer ball detection. Firstly, we introduce sports camera calibration to seamlessly map soccer field images from match videos onto a standardized two‐dimensional soccer field template. This addresses the challenge of consistent analysis across video frames amid continuous camera angle changes. Secondly, given challenges such as occlusions, high‐speed movements, and dynamic camera perspectives, obtaining accurate position data for players and the soccer ball is non‐trivial. To mitigate this, we curate a large‐scale, high‐precision soccer ball detection dataset and devise a robust detection model, which achieved the of 80.9%. Additionally, we develop a high‐speed, efficient, and lightweight tracking model to ensure precise player tracking. Through the integration of these modules, our pipeline focuses on real‐time analysis of the current camera lens content during matches, facilitating rapid and accurate computation and analysis while offering intuitive visualizations.

While deep learning has been widely used for video analytics, such as video classification and action detection, dense action detection with fast-moving subjects from sports videos is still challenging. In this work, we release yet another sports video benchmarkP2ANetforPingPong-Action detection, which consists of 2,721 video clips collected from the broadcasting videos of professional table tennis matches in World Table Tennis Championships and Olympiads. We work with a crew of table tennis professionals and referees on a specially designed annotation toolbox to obtain fine-grained action labels (in 14 classes) for every ping-pong action that appeared in the dataset, and formulate two sets of action detection problems—action localizationandaction recognition. We evaluate a number of commonly seen action recognition (e.g., TSM, TSN, Video SwinTransformer, and Slowfast) and action localization models (e.g., BSN, BSN++, BMN, TCANet), usingP2ANetfor both problems, under various settings. These models can only achieve 48% area under the AR-AN curve for localization and 82% top-one accuracy for recognition since the ping-pong actions are dense with fast-moving subjects but broadcasting videos are with only 25 FPS. The results confirm thatP2ANetis still a challenging task and can be used as a special benchmark for dense action detection from videos. We invite readers to examine our dataset by visiting the following link:https://github.com/Fred1991/P2ANET.

In recent years, a large number of applications have been developed for image processing systems in the area of biological imaging. We have already finished the development of a dedicated microcomputer-based image processing and analysis system for quantitative microscopy. The system's primary function has been to facilitate and ultimately automate quantitative image analysis tasks such as the measurement of cellular DNA contents. We have recognized from this development experience, and interaction with system users, biologists and technicians, that the increasingly widespread use of image processing systems, and the development and application of new techniques for utilizing the capabilities of such systems, would generate a need for some kind of inexpensive general purpose image acquisition and processing system specially tailored for the needs of the medical community. We are currently engaged in the development and testing of hardware and software for a fairly high-performance image processing computer system based on a popular personal computer. In this paper, we describe the design and development of this system. Biological image processing computer systems have now reached a level of hardware and software refinement where they could become convenient image analysis tools for biologists. The development of a general purpose image processing system for quantitative image analysis that is inexpensive, flexible, and easy-to-use represents a significant step towards making the microscopic digital image processing techniques more widely applicable not only in a research environment as a biologist's workstation, but also in clinical environments as a diagnostic tool.

Quantitative Analysis of Digital Microscope Images

Chapter 14 - Quantitative Analysis of Digital Microscope Images

Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.

This study investigated the dynamics of ubiquitinated proteins after the inflammatory stimulation of RAW 264.7 macrophage-like cells with bacterial lipopolysaccharide. Ubiquitination is a common protein post-translational modification that regulates many key cellular functions. We demonstrated that levels of global ubiquitination and K48 and K63 polyubiquitin chains change after lipopolysaccharide stimulation. Quantitative proteomic analysis identified 1199 ubiquitinated proteins, 78 of which exhibited significant changes in ubiquitination levels following stimulation. Integrating the ubiquitinome data with global proteomic and transcriptomic results allowed us to identify a subset of 88 proteins that were targeted for degradation after lipopolysaccharide stimulation. Using cellular assays and Western blot analyses, we biochemically validated DBC1 (a histone deacetylase inhibitor) as a degradation substrate that is targeted via an orchestrated mechanism utilizing caspases and the proteasome. The degradation of DBC1 releases histone deacetylase activity, linking lipopolysaccharide activation to chromatin remodeling in caspase- and proteasome-mediated signaling.

Microelectrode Arrays (MEA) are devices for long term electrophysiological recording of extracellular spontaneous or evocated activities on in vitro neuron culture. This work proposes and develops a framework for quantitative and morphological analysis of neuron cultures on MEAs, by processing their corresponding images, acquired by fluorescence microscopy. The neurons are segmented from the fluorescence channel images using a combination of segmentation by thresholding, watershed transform, and object classification. The positioning of microelectrodes is obtained from the transmitted light channel images using the circular Hough transform. The proposed method was applied to images of dissociated culture of rat dorsal root ganglion (DRG) neuronal cells. The morphological and topological quantitative analysis carried out produced information regarding the state of culture, such as population count, neuron-to-neuron and neuron-to-microelectrode distances, soma morphologies, neuron sizes, neuron and microelectrode spatial distributions. Most of the analysis of microscopy images taken from neuronal cultures on MEA only consider simple qualitative analysis. Also, the proposed framework aims to standardize the image processing and to compute quantitative useful measures for integrated image-signal studies and further computational simulations. As results show, the implemented microelectrode identification method is robust and so are the implemented neuron segmentation and classification one (with a correct segmentation rate up to 84%). The quantitative information retrieved by the method is highly relevant to assist the integrated signal-image study of recorded electrophysiological signals as well as the physical aspects of the neuron culture on MEA. Although the experiments deal with DRG cell images, cortical and hippocampal cell images could also be processed with small adjustments in the image processing parameter estimation.