scholarly journals Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Edgar Chavolla ◽  
Arturo Valdivia ◽  
Primitivo Diaz ◽  
Daniel Zaldivar ◽  
Erik Cuevas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Community ◽  
Color Image ◽  
The Other ◽  
Color Image Segmentation ◽  
Color Segmentation ◽  
Color Model ◽  
Wide Range ◽  
Hsv Color Model ◽  
Relevant Topic

Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

A Real-Time and Effective Object Recognition and Localization Method

2014 ◽  
Vol 615 ◽  
pp. 107-112 ◽  
Author(s):  
Xiao Ling Ding ◽  
Qiang Zhao ◽  
Yi Bin Li ◽  
Xin Ma
Keyword(s):  
Object Recognition ◽  
Real Time ◽  
Color Image ◽  
Color Image Segmentation ◽  
Geometry Model ◽  
Perspective Geometry ◽  
Hsv Color Model ◽  
Camera Perspective ◽  
Color Object Recognition ◽  
Image Segmentation Algorithm

In this paper, we realize object recognition and localization in a real time based on appearance features of object. For object recognition, we proposed to use global feauture (color) of images, and with an improved color image segmentation algorithm to realize threshold segmentation based on pixels in the image’s HSV color model by using the tool OpenCV, so we can realize the special color object recognition. Further the object can be localized with the ground constrained method by using the camera perspective geometry model. In the lab conditions, we realized single color object recognition and localization by transplanting the algorithm into Amigobots mobile robot and proved this method is simple, effective and real-time.

scholarly journals Competitor Identification by Use The Sentiment Classification Based on the User Research

2019 ◽  
Vol 8 (9S2) ◽  
pp. 871-874
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
The Other ◽  
Machine Learning Techniques ◽  
Single Product ◽  
User Research ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Wide Range

Online shopping's have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.

COLOR SEGMENTATION VIA IMPROVED MOUNTAIN CLUSTERING TECHNIQUE

2007 ◽  
Vol 07 (02) ◽  
pp. 407-426 ◽  
Author(s):  
NISHCHAL K. VERMA ◽  
M. HANMANDLU
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Color Image ◽  
Visual Assessment ◽  
Color Image Segmentation ◽  
Color Segmentation ◽  
Clustering Techniques ◽  
Clustering Technique

This paper proposes a new improved mountain clustering technique, which is compared with some of the existing techniques such as K-Means, FCM, EM and Modified Mountain Clustering. The performance of all these clustering techniques towards color image segmentation is compared in terms of cluster entropy as a measure of information and observed via computational complexity. The cluster entropy is heuristically determined, but is found to be effective in forming correct clusters as verified by visual assessment.

scholarly journals Machine Learning: An Overview and Applications in Pharmacogenetics

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1511
Author(s):  
Giovanna Cilluffo ◽  
Salvatore Fasola ◽  
Giuliana Ferrante ◽  
Velia Malizia ◽  
Laura Montalbano ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Narrative Review ◽  
The Other ◽  
Underlying Structure ◽  
The Past ◽  
Satisfactory Performance ◽  
Final Goal ◽  
Wide Range ◽  
Anti Cancer

This narrative review aims to provide an overview of the main Machine Learning (ML) techniques and their applications in pharmacogenetics (such as antidepressant, anti-cancer and warfarin drugs) over the past 10 years. ML deals with the study, the design and the development of algorithms that give computers capability to learn without being explicitly programmed. ML is a sub-field of artificial intelligence, and to date, it has demonstrated satisfactory performance on a wide range of tasks in biomedicine. According to the final goal, ML can be defined as Supervised (SML) or as Unsupervised (UML). SML techniques are applied when prediction is the focus of the research. On the other hand, UML techniques are used when the outcome is not known, and the goal of the research is unveiling the underlying structure of the data. The increasing use of sophisticated ML algorithms will likely be instrumental in improving knowledge in pharmacogenetics.

scholarly journals Predictive efficiency of distinct color image segmentation methods for measuring intramuscular fat in beef

2015 ◽  
Vol 45 (10) ◽  
pp. 1865-1871 ◽  
Author(s):  
Renius Mello ◽  
Fabiano Nunes Vaz ◽  
Paulo Santana Pacheco ◽  
Leonir Luiz Pascoal ◽  
Rosa Cristina Prestes ◽  
...  
Keyword(s):  
Image Analysis ◽  
Discriminant Function ◽  
Color Image ◽  
Lipid Extraction ◽  
Intramuscular Fat ◽  
Sampling Interval ◽  
Color Image Segmentation ◽  
Wide Range ◽  
Meat Samples ◽  
The Imf

Intramuscular fat (IMF) influences important quality characteristics of meat, such as flavor, juiciness, palatability, odor and tenderness. Thus, the objective of this study was to apply the following image processing techniques to quantify the IMF in beef: palette; sampling, interval of coordinates; black and white threshold; and discriminant function of colors. Thirty-five samples of beef, with a wide range of IMF, were used. Color images were taken of the meat samples from different muscles, with variability in the IMF content. The IMF of a thin cross-section meat was determined by chemical lipid extraction and was predicted by image analysis. The chemical method was compared with the image analysis. The segmentation procedures were validated by the adjustment of a linear regression equation to the series of values that were observed and predicted, as well as the regression parameters evaluated by the F-test. The predictive power of these approaches was also compared by residual analysis and by the decomposition of the mean square deviations. The results showed that the discriminant function was the best color segmentation method to measure intramuscular fat via digital images, but required adjustments in the prediction pattern.

scholarly journals Lead and Disease

1983 ◽  
Vol 2 (3-4) ◽  
pp. 135-145 ◽  
Author(s):  
Stephen Davies
Keyword(s):  
Lead Poisoning ◽  
Lead Exposure ◽  
Scientific Community ◽  
The Other ◽  
Low Level ◽  
Wide Range

The evidence is reviewed for ‘low’ level lead exposure being an important contributive factor in the development of a wide range of diseases and conditions. “Classical” lead poisoning is not discussed. Also discussed are the other various contributive factors in the genesis of disease. A call is made for doctors to take into consideration these factors when considering their patients and disease processes, and for scientists to take responsibility for informing the non-scientific community of the hazards of lead and other toxins.

scholarly journals Region-based image retrieval using region of interest (ROI) according to incremental frame and clustering color image

2022 ◽  
Vol 9 (1) ◽  
pp. 138-147
Author(s):  
Mamat et al. ◽  
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Region Of Interest ◽  
Color Model ◽  
Retrieval Performance ◽  
Global Features ◽  
Hsv Color Model ◽  
High Level ◽  
Cie Lab ◽  
Large Image Databases

Content-based image retrieval involves the extraction of global feature images for their retrieval performance in large image databases. Extraction of global features image cause problem of the semantic gap between the high-level meaning and low-level visual features images. In this study RBIR, Region of Interest Based (ROI) Image Retrieval Using Incremental Frame of Color Image was proposed. It combines several methods, including filtering process, image partitioning using clustering and incremental frame formation, complementation law of theory set to generate ROI, NROI, or ER of the region. The concept of weighting as well as a significant query is also incorporated as a query strategy. Extensive experiments were also conducted on the Wang database and the color model selected was the CIE lab. Experimental results show the proposed method is efficient in image retrieval. The performance of the proposed method shows a better average IPR value of 3.51% compared to RGB and 22.92% with the HSV color model. Meanwhile, it also performs better by 36%, 5%, and 24% compared to methods CH (8,2,2), CH (8,3,3), and CH (16,4,4).

Unravelling pre-eruptive P-T conditions by machine learning

2020 ◽  
Author(s):  
Maurizio Petrelli ◽  
Luca Caricchi ◽  
Diego Perugini
Keyword(s):  
Machine Learning ◽  
Real World ◽  
The Other ◽  
Calibration Data ◽  
Wide Range ◽  
Volcanic Processes ◽  
Real World Applications ◽  
Complex Models ◽  
Temperature And Pressure ◽  
Use Of Models

<p>Clinopyroxene based thermometers and barometers are widely used tools for estimating temperature and pressure conditions under which magmas are stored before eruptions.</p><p>Several studies reported the development and the application of Clinopyroxene–liquid geothermobarometers in many different volcanic environments, also warning on the potential pitfall in using overly complex models [e.g., 1 and references therein]. The main drawback in the use of models with a large number of parameters is the potential overfitting of the calibration data, yielding a poor accuracy in real-world applications. On the other hand, simpler models cannot account for the complexity of natural magmatic systems, requiring different calibrations for different magma chemistries [e.g., 2, 3].</p><p>In the present study, we report on the development of Clinopyroxene and Clinopyroxene-liquid thermometers and barometers in a wide range of P-T-X conditions using Machine Learning (ML) algorithms. To avoid overfitting and demonstrate the robustness of the different methods, we randomly split the dataset into training and validation portions and repeating this procedure up to 10000 times to trace the performance of each of the used algorithms. We compared the performance of ML algorithms with classical and established Clinopyroxene and Clinopyroxene-liquid thermometers and barometers using local and global calibrations. Finally, we applied the obtained thermometers and barometers to real study cases.</p><p> </p><p>[1]      K. D. Putirka, Thermometers and barometers for volcanic systems, Minerals, Inclusions and Volcanic Processes, 69. 61–120, 2008.</p><p>[2]      D. A. Neave, K. D. Putirka, Am. Mineral., 2017, DOI:10.2138/am-2017-5968.</p><p>[3]      M. Masotta, S. Mollo, C. Freda, M. Gaeta, G. Moore, Contrib. to Mineral. Petrol., 2013, DOI:10.1007/s00410-013-0927-9.</p>

2D Color-Intensity Representation of Ultrasonic Thickness Gauge Measurements

2020 ◽  
pp. 1192-1198
Author(s):  
M.S. Mohammad ◽  
Tibebe Tesfaye ◽  
Kim Ki-Seong
Keyword(s):  
Cost Effective ◽  
The Other ◽  
Thickness Gauge ◽  
Color Intensity ◽  
Digital Readout ◽  
Flat Surfaces ◽  
Video Cameras ◽  
Wide Range ◽  
Cost Effective Alternative ◽  
Ultrasonic Thickness

Ultrasonic thickness gauges are easy to operate and reliable, and can be used to measure a wide range of thicknesses and inspect all engineering materials. Supplementing the simple ultrasonic thickness gauges that present results in either a digital readout or as an A-scan with systems that enable correlating the measured values to their positions on the inspected surface to produce a two-dimensional (2D) thickness representation can extend their benefits and provide a cost-effective alternative to expensive advanced C-scan machines. In previous work, the authors introduced a system for the positioning and mapping of the values measured by the ultrasonic thickness gauges and flaw detectors (Tesfaye et al. 2019). The system is an alternative to the systems that use mechanical scanners, encoders, and sophisticated UT machines. It used a camera to record the probe’s movement and a projected laser grid obtained by a laser pattern generator to locate the probe on the inspected surface. In this paper, a novel system is proposed to be applied to flat surfaces, in addition to overcoming the other limitations posed due to the use of the laser projection. The proposed system uses two video cameras, one to monitor the probe’s movement on the inspected surface and the other to capture the corresponding digital readout of the thickness gauge. The acquired images of the probe’s position and thickness gauge readout are processed to plot the measured data in a 2D color-coded map. The system is meant to be simpler and more effective than the previous development.

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

Sign in / Sign up

Export Citation Format

Share Document