Color Intensity: A Study of RPPG Algorithm for Heart Rate Estimation

2020 ◽  
Vol 22 (10) ◽  
Author(s):  
Dr. Kavita R. Singh ◽  
◽  
Ruchika Sinhal ◽  
Ravi Wasalwar ◽  
Gupta Dr. K. O ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Heart Rate ◽  
Comparative Analysis ◽  
High Performance ◽  
Color Model ◽  
Color Intensity ◽  
Remote Photoplethysmography ◽  
State Of Art ◽  
Heart Rate Estimation

With the growing advancements and development in the field of digital image processing and computer vision, an individual’s heart pulse can be extracted from the human skin surfaces. This method is termed as remote photoplethysmography (rPPG). The method can be applied from the video recorded from the consumer-based mobile camera also. In this paper, the work presented has mainly twofold goals. Firstly to develop a fruitful yet simple rPPG algorithm that should be simple for any individual to understand and implement that will increase the understanding of the rPPG subject. Secondly, to compare the algorithm designed for the RGB color model with the state-of-art rPPG algorithms developed and presented in the literature. And finally, we present the comparative analysis of rPPG algorithms reported in the literature with our proposed rPPG algorithm which is simple and has demonstrated comparably high performance for the green channel as compared to other algorithms.

A COMPARATIVE ANALYSIS OF ALGORITHMS FOR COUNTING OBJECTS IN IMAGES

2018 ◽  
pp. 310-314
Author(s):  
Tatyana Dineva ◽  
Nely Ruseva ◽  
Mariya Georgieva-Nikolova
Keyword(s):  
Comparative Analysis ◽  
High Performance ◽  
Analysis Of Algorithms ◽  
Small Error ◽  
Color Model ◽  
Image Capturing ◽  
The Impact ◽  
Selection Of

The report presents a comparative analysis of algorithms for counting objects in images. They are used in counting eggs. From the three algorithms compared Threshold, Circular Hough and Wateshed, with high performance and small error values ​​is the algorithm Circular Hough. When recognizing the eggs, it is necessary to make a selection of the color model, by which to separate the eggs from the background according to their color and the breed of birds. More research is needed on the impact of the image capturing conditions on the accuracy of algorithms.

Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants

2017 ◽  
Vol 8 (2) ◽  
pp. 45-56
Author(s):  
Richa Sharma ◽  
Madan Lal
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Digital Image Processing ◽  
High Performance ◽  
Local Binary Pattern ◽  
Texture Classification ◽  
Local Binary Patterns ◽  
Classification Performance ◽  
Powerful Method ◽  
Rotation Invariant

Texture classification is an important issue in digital image processing and the Local Binary pattern (LBP) is a very powerful method used for analysing textures. LBP has gained significant popularity in texture analysis world. However, LBP method is very sensitive to noise and unable to capture the macrostructure information of the image. To address its limitation, some variants of LBP have been defined. In this chapter, the texture classification performance of LBP has been compared with the five latest high-performance LBP variants, like Centre symmetric Local Binary Pattern (CS-LBP), Orthogonal Combination of Local Binary Patterns (OC LBP), Rotation Invariant Local Binary Pattern (RLBP), Dominant Rotated Local Binary Pattern (DRLBP) and Median rotated extended local binary pattern (MRELBP). This was by using the standard images Outex_TC_0010 dataset. From the experimental results it is concluded that DRLBP and MRELBP are the best methods for texture classification.

scholarly journals Rapid single-cell cytometry data visualization with EmbedSOM

2018 ◽  
Author(s):  
Miroslav Kratochvíl ◽  
Abhishek Koladiya ◽  
Jana Balounova ◽  
Vendula Novosadova ◽  
Karel Fišer ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Single Cell ◽  
High Performance ◽  
Cell Populations ◽  
Linear Scaling ◽  
Current State ◽  
Performance Visualization ◽  
Sample Data ◽  
Linear Embedding ◽  
State Of Art

AbstractEfficient unbiased data analysis is a major challenge for laboratories handling large cytometry datasets. We present EmbedSOM, a non-linear embedding algorithm based on FlowSOM that improves the analyses by providing high-performance visualization of complex single cell distributions within cellular populations and their transition states. The algorithm is designed for linear scaling and speed suitable for interactive analyses of millions of cells without downsampling. At the same time, the visualization quality is competitive with current state-of-art algorithms. We demonstrate the properties of EmbedSOM on workflows that improve two essential types of analyses: The native ability of EmbedSOM to align population positions in embedding is used for comparative analysis of multi-sample data, and the connection to FlowSOM is exploited for simplifying the supervised hierarchical dissection of cell populations. Additionally, we discuss the visualization of the trajectories between cellular states facilitated by the local linearity of the embedding.

Comparative Analysis of Intelligent Driving and Safety Assistance Systems Using YOLO and SSD Model of Deep Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 131-146
Author(s):  
Nidhi Sindhwani ◽  
Shekhar Verma ◽  
Tushar Bajaj ◽  
Rohit Anand
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Internet Of Things ◽  
Road Accidents ◽  
The Road ◽  
The World ◽  
Assistance Systems ◽  
On The Road

Bad road conditions are one of the main causes of road accidents around the world. These kinds of accidents prove to be fatal as many lives are lost in these accidents that are mainly caused by potholes or distress on surface of roads. This paper suggests a system that will not only help in reducing the chances of these accidents by making the driver aware of the upcoming distress/potholes on the road but also saving the location of these potholes which can be sent to respective authorities so that they can be repaired. The authors have used technologies like image processing, computer vision, deep learning, and internet of things (IoT) to make this happen. It uses a camera mounted in front near windshield that will capture the images which will be further be processed to get the location of the potholes and distress on road. These detected potholes can be projected on a heads-up display (HUD) placed near windshield which will notify the driver of the potholes.

Sign in / Sign up

Export Citation Format

Share Document