scholarly journals Video-Based Rope Skipping Repetition Counting with ResNet Model

2021 ◽  
Vol 10 (11) ◽  
pp. 25413-25419
Author(s):  
Xinxin Li ◽  
Jiawen Wang
Keyword(s):  
Computer Vision ◽  
Real World ◽  
Important Research ◽  
Research Areas ◽  
Repetition Number ◽  
The Given ◽  
Rope Skipping ◽  
Counting Algorithm

Video Repetition Counting is one of the important research areas in computer vision. It focuses on estimating the number of repeating actions. In this paper, we propose a method for video-based rope skipping repetition counting that combines the ResNet Model and a counting algorithm. Each frame in the given video is first classified into two categories: upward and downward, describing its current motion status. Then the classification sequence of the video is processed by a statistical counting algorithm to obtain the final repetition number. The experiments on real-world videos show the efficiency of our model.

scholarly journals Real-Time Object Detection Model

2020 ◽  
Vol 6 (12) ◽  
pp. 360-364
Author(s):  
Akash Kumar, Dr. Amita Goel Prof. Vasudha Bahl and Prof. Nidhi Sengar
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Object Detection ◽  
Real Time ◽  
Real World ◽  
The Real ◽  
Detection Model ◽  
Bounding Boxes ◽  
The Given ◽  
Single Neural Network

Object Detection is a study in the field of computer vision. An object detection model recognizes objects of the real world present either in a captured image or in real-time video where the object can belong to any class of objects namely humans, animals, objects, etc. This project is an implementation of an algorithm based on object detection called You Only Look Once (YOLO v3). The architecture of yolo model is extremely fast compared to all previous methods. Yolov3 model executes a single neural network to the given image and then divides the image into predetermined bounding boxes. These boxes are weighted by the predicted probabilities. After non max-suppression it gives the result of recognized objects together with bounding boxes. Yolo trains and directly executes object detection on full images.

Vision and Sensor-Based Human Activity Recognition

2020 ◽  
pp. 17-35 ◽  
Author(s):  
Md Atiqur Rahman Ahad
Keyword(s):  
Computer Vision ◽  
Activity Recognition ◽  
Affective Computing ◽  
Intelligent System ◽  
Human Action Recognition ◽  
Human Action ◽  
Important Research ◽  
Healthcare Facilities ◽  
The Core ◽  
Research Areas

Human action or activity or behavior analysis, recognition, and understanding are very important research areas in the field of computer vison, internet of things (IoT) sensor-based analysis, human-computer interaction (HCI), affective computing, intelligent system, healthcare facilities, and so on. There is much importance in human action recognition. This chapter introduces the core aspects of human action or activity recognition (HAR). These are split into two different domains: computer vision-based action/activity recognition, action localization, etc.; and wearable IoT sensor-based HAR. Though cameras are also sensors that provide vision-based information, the author puts camera-based or vision-based methods in another category. The chapter introduces core challenges and strategies to move forward.

Nuklearmedizin trifft Strahlentherapie

2010 ◽  
Vol 49 (S 01) ◽  
pp. S11-S15
Author(s):  
C. Schütze ◽  
M. Krause ◽  
A. Yaromina ◽  
D. Zips ◽  
M. Baumann
Keyword(s):  
Nuclear Medicine ◽  
Radiation Oncology ◽  
External Beam Radiotherapy ◽  
Biological Knowledge ◽  
Important Research ◽  
Tumour Control ◽  
Research Areas ◽  
Normal Tissue Damage ◽  
Local Tumour Control ◽  
Local Tumour

SummaryRadiobiological and cell biological knowledge is increasingly used to further improve local tumour control or to reduce normal tissue damage after radiotherapy. Important research areas are evolving which need to be addressed jointly by nuclear medicine and radiation oncology. For this differences of the biological distribution of diagnostic and therapeutic nuclides compared with the more homogenous dose-distribution of external beam radiotherapy have to be taken into consideration. Examples for interdisciplinary biology-based cancer research in radiation oncology and nuclear medicine include bioimaging of radiobiological parameters characterizing radioresistance, bioimage-guided adaptive radiotherapy, and the combination of radiotherapy with molecular targeted drugs.

scholarly journals A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Data ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 12
Author(s):  
Helder F. Castro ◽  
Jaime S. Cardoso ◽  
Maria T. Andrade
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Qualitative Analysis ◽  
Continuous Growth ◽  
Scientific Databases ◽  
Extensive Survey ◽  
Research Areas ◽  
Current State ◽  
Global View ◽  
Cooperative Production

The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

A Hindi Image Caption Generation Framework Using Deep Learning

2021 ◽  
Vol 20 (2) ◽  
pp. 1-19
Author(s):  
Santosh Kumar Mishra ◽  
Rijul Dhir ◽  
Sriparna Saha ◽  
Pushpak Bhattacharyya
Keyword(s):  
Computer Vision ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
English Language ◽  
Image Captioning ◽  
Textual Description ◽  
Proposed Model ◽  
Hindi Language ◽  
The Given

Image captioning is the process of generating a textual description of an image that aims to describe the salient parts of the given image. It is an important problem, as it involves computer vision and natural language processing, where computer vision is used for understanding images, and natural language processing is used for language modeling. A lot of works have been done for image captioning for the English language. In this article, we have developed a model for image captioning in the Hindi language. Hindi is the official language of India, and it is the fourth most spoken language in the world, spoken in India and South Asia. To the best of our knowledge, this is the first attempt to generate image captions in the Hindi language. A dataset is manually created by translating well known MSCOCO dataset from English to Hindi. Finally, different types of attention-based architectures are developed for image captioning in the Hindi language. These attention mechanisms are new for the Hindi language, as those have never been used for the Hindi language. The obtained results of the proposed model are compared with several baselines in terms of BLEU scores, and the results show that our model performs better than others. Manual evaluation of the obtained captions in terms of adequacy and fluency also reveals the effectiveness of our proposed approach. Availability of resources : The codes of the article are available at https://github.com/santosh1821cs03/Image_Captioning_Hindi_Language ; The dataset will be made available: http://www.iitp.ac.in/∼ai-nlp-ml/resources.html .

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

Transition metal-catalyzed arylation of unstrained C−C single bonds

2021 ◽  
Author(s):  
Long Yang ◽  
Wuxin Zhou ◽  
Qiang Li ◽  
Xiangge Zhou
Keyword(s):  
Organic Chemistry ◽  
Transition Metal ◽  
Bond Activation ◽  
Important Research ◽  
Carbon Bond ◽  
Research Areas ◽  
Carbon Carbon Bond ◽  
Transition Metal Catalyzed ◽  
Metal Catalyzed ◽  
Single Bonds

Carbon−carbon bond activation is one of the most challenging and important research areas in organic chemistry. Selective C−C bond activation of unstrained substrates is difficult to achieve owing to its...

Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

2021 ◽  
Author(s):  
Mohammad Shehab ◽  
Laith Abualigah
Keyword(s):  
Real World ◽  
Optimization Problems ◽  
Second Stage ◽  
Opposition Based Learning ◽  
High Fitness ◽  
Fitness Value ◽  
Speed Up ◽  
Disruption Operator ◽  
The Given ◽  
Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

scholarly journals The Model of Sociological Education in the USSR (the Late 1950s — Early 1980s)

2021 ◽  
pp. 112-117
Author(s):  
Sergei V. Kozin ◽  
Zhanna B. Litvinova
Keyword(s):  
Sociological Research ◽  
Scientific Publications ◽  
Mutual Exchange ◽  
Research Areas ◽  
Time Period ◽  
The Given ◽  
Soviet Sociology

The article provides an analysis of foreign and Soviet scientific publications devoted to the problem of sociological education, the “revival” of Soviet sociology, as well as to the role and place of sociology among the sciences, in society and education. The given study covers the time period of the late 1950s — early 1980s and briefly describes the education of the population of the USSR at that time. Looking through the works on Soviet sociology, the authors show that sociology was introduced not only into the research areas of specific universities and laboratories, but also into many other branches and spheres of activity, as well as into the authorities’ activity. The authors of the article purposefully focus on the role of consolidation and mutual exchange of sociological research from various sociological services.

scholarly journals A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhaoxing Li ◽  
Qionghai Liu ◽  
Li Chen
Keyword(s):  
Multiobjective Optimization ◽  
Complex Networks ◽  
Optimization Algorithm ◽  
Real World ◽  
Network Robustness ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Scale Free ◽  
Scale Free Networks ◽  
The Given

A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness.

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