scholarly journals Segmentation of an Indian Classical Dance Videos using Different Segmentation Methods.

2019 ◽  
Vol 9 (2) ◽  
pp. 982-986
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
System Performance ◽  
Region Of Interest ◽  
Automatic Machine ◽  
Classical Dance ◽  
Machine Learning Model ◽  
Segmentation Methods ◽  
Indian Classical Dance ◽  
Important Objective

Video to frame conversion features are retrieved to categorize the actions in an Indian classical dance video dataset. The goal is to design an automatic machine learning model that identifies the moves of a dancer in a video. A video is a collection of images of specific movements, hence, features representing shapes and color can be used to interpret the dance steps. Image segmentation based features are capable of representing the shape in varying background conditions. Segmentation has become an important objective in image analysis and computer vision. To segment the images, edge detection, thresholding and region of interest are taken for this study. The proposed system performance is analyzed for total number of 50 different movements taken from Indian classicaldances.Bharatanatyam,Kathak,Kuchipudi,Manipuri,Mo hiniytam Odissi,Kathakali and Satrriya in different background conditions

Computational Healthcare System With Image Analysis

2019 ◽  
pp. 89-127 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computer Vision ◽  
Image Analysis ◽  
Social Insurance ◽  
Valuable Data ◽  
Clinical Notes ◽  
Information Reporting ◽  
Segmentation Methods ◽  
Crucial Part

The quickly extending field of huge information examination has begun to assume a crucial part in the advancement of human services practices and research. In this chapter, challenges like gathering information from complex heterogeneous patient sources, utilizing the patient/information relationships in longitudinal records, understanding unstructured clinical notes in the correct setting and efficiently dealing with expansive volumes of medicinal imaging information, and removing conceivably valuable data is shown. Healthcare and IoT and machine learning along with data mining are also discussed. Image analysis and segmentation methods comparative study is given for the examination of computer vision, imaging handling, and example acknowledgment has gained considerable ground amid the previous quite a few years. Examiners have distributed an abundance of essential science and information reporting the advance and social insurance application on medicinal imaging.

scholarly journals Segmentation of Thermogram Based on Region Based Technique using Split and Merge Method

2019 ◽  
Vol 8 (4) ◽  
pp. 9574-9578
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Object Representation ◽  
Region Of Interest ◽  
Region Growing ◽  
Destructive Testing ◽  
Segmentation Methods ◽  
Split And Merge ◽  
Thermographic Image ◽  
Non Destructive

The main aim of segmentation is to identify the Region of Interest for image analysis. The segregation of an image into meaningful structures is often an important phase in image analysis, object representation, visualization and also in various other image processing tasks. Image segmentation is mostly useful in applications like detection where it is difficult to process whole image at a time. In this paper Region based image segmentation is used to identify the delaminations in Thermographic image of Infrared Non-Destructive Testing. There are two basic techniques in Region based segmentation viz. Region growing method, splitting and merging method. New method based Split and Merge segmentation technique is employed to identify the defective regions in thermogram. Results obtained after segmentation as compared with state of art segmentation methods

scholarly journals Loop Current SSH Forecasting: A New Domain Partitioning Approach for a Machine Learning Model

Forecasting ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 570-579
Author(s):  
Justin L. Wang ◽  
Hanqi Zhuang ◽  
Laurent Chérubin ◽  
Ali Muhamed Ali ◽  
Ali Ibrahim
Keyword(s):  
Machine Learning ◽  
Current System ◽  
Model Performance ◽  
Region Of Interest ◽  
Divide And Conquer ◽  
Loop Current ◽  
Event Prediction ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Domain Partitioning

A divide-and-conquer (DAC) machine learning approach was first proposed by Wang et al. to forecast the sea surface height (SSH) of the Loop Current System (LCS) in the Gulf of Mexico. In this DAC approach, the forecast domain was divided into non-overlapping partitions, each of which had their own prediction model. The full domain SSH prediction was recovered by interpolating the SSH across each partition boundaries. Although the original DAC model was able to predict the LCS evolution and eddy shedding more than two months and three months in advance, respectively, growing errors at the partition boundaries negatively affected the model forecasting skills. In the study herein, a new partitioning method, which consists of overlapping partitions is presented. The region of interest is divided into 50%-overlapping partitions. At each prediction step, the SSH value at each point is computed from overlapping partitions, which significantly reduces the occurrence of unrealistic SSH features at partition boundaries. This new approach led to a significant improvement of the overall model performance both in terms of features prediction such as the location of the LC eddy SSH contours but also in terms of event prediction, such as the LC ring separation. We observed an approximate 12% decrease in error over a 10-week prediction, and also show that this method can approximate the location and shedding of eddy Cameron better than the original DAC method.

scholarly journals Computational analysis of tongue image for health diagnosis

2017 ◽  
Author(s):  
◽  
Meng Zhang
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Image Segmentation ◽  
The Body ◽  
Support Vector ◽  
Classification Image ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
User Friendly ◽  
Traditional Chinese Medical

Tongue, the primary taste organ in the mouth, can reflect the whole body's health conditions based on the Traditional Chinese Medical (TCM) theories. Watching the tongue is one of the most common, essential and reliable methods for the TCM doctor to make diagnoses. In this thesis, a new health system is introduced based on tongue image analysis. The technologies adopted in this system ranged from tongue image processing algorithms to machine learning applications. The tongue image algorithms used in this work include image segmentation, tongue recognition and tongue image classification. Image segmentation was used to get rid of other unrelated parts, such as lip, face and neck, while keeping the tongue only. Then two recognition methods were applied to check whether the segmented result is a tongue or not. For different tongue patterns, the Support Vector Machine is applied to train a machine learning model and make predictions to classify the tongue into different labeled groups. An app named 'iTongue' is designed to monitor the body status by taking and processing tongue images in smart phones. The app provides a user-friendly, fast and powerful health tool based on TCM theories. The whole system is implemented in a webbased environment. An advanced portal was developed to connect the users and the TCM doctors. The users will not only obtain the analysis label of tongue images, but also get some life style recommendations based on the tongue image analysis. This portal helps the user understand more about his body status and guide him to adopt a more suitable diet and improve exercise.

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

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