A novel approach of reading analog multimeter based on computer vision

2018 ◽  
Author(s):  
Yung-Sheng Chen ◽  
Jeng-Yau Wang
Keyword(s):  
Computer Vision ◽  
Novel Approach

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Computer Vision Based Asset Surveillance for Smart Buildings

2020 ◽  
Vol 17 (1) ◽  
pp. 456-463
Author(s):  
K. S. Gautam ◽  
Latha Parameswaran ◽  
Senthil Kumar Thangavel
Keyword(s):  
Computer Vision ◽  
Correlation Coefficient ◽  
Real World ◽  
Vision System ◽  
Region Of Interest ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Ip Camera ◽  
Pros And Cons ◽  
Real World Problems

Unraveling meaningful pattern form the video offers a solution to many real-world problems, especially surveillance and security. Detecting and tracking an object under the area of video surveillance, not only automates the security but also leverages smart nature of the buildings. The objective of the manuscript is to detect and track assets inside the building using vision system. In this manuscript, the strategies involved in asset detection and tracking are discussed with their pros and cons. In addition to it, a novel approach has been proposed that detects and tracks the object of interest across all the frames using correlation coefficient. The proposed approach is said to be significant since the user has an option to select the object of interest from any two frames in the video and correlation coefficient is calculated for the region of interest. Based on the arrived correlation coefficient the object of interest is tracked across the rest of the frames. Experimentation is carried out using the 10 videos acquired from IP camera inside the building.

A NOVEL APPROACH TO IMAGE THRESHOLDING BASED ON 2D HOMOGENEITY HISTOGRAM AND MAXIMUM FUZZY ENTROPY

2011 ◽  
Vol 07 (01) ◽  
pp. 105-133 ◽  
Author(s):  
H. D. CHENG ◽  
YANHUI GUO ◽  
YINGTAO ZHANG
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Fuzzy Entropy ◽  
Image Thresholding ◽  
Novel Approach ◽  
Entropy Principle

Image thresholding is an important topic for image processing, pattern recognition and computer vision. Fuzzy set theory has been successfully applied to many areas, and it is generally believed that image processing bears some fuzziness in nature. In this paper, we employ the newly proposed 2D homogeneity histogram (homogram) and the maximum fuzzy entropy principle to perform thresholding. We have conducted experiments on a variety of images. The experimental results demonstrate that the proposed approach can select the thresholds automatically and effectively. Especially, it not only can process "clean" images, but also can process images with different kinds of noises and images with multiple kinds of noise well without knowing the type of the noise, which is the most difficult task for image thresholding. It will be useful for applications in computer vision and image processing.

Probabilistic Nodes Combination (PNC)

2020 ◽  
pp. 1026-1057
Author(s):  
Dariusz Jacek Jakóbczak
Keyword(s):  
Computer Vision ◽  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Shape Representation ◽  
Shape Reconstruction ◽  
Novel Approach ◽  
Curve Modeling ◽  
Inverse Functions ◽  
Vision Function

The method of Probabilistic Nodes Combination (PNC) enables interpolation and modeling of two-dimensional curves using nodes combinations and different coefficients γ: polynomial, sinusoidal, cosinusoidal, tangent, cotangent, logarithmic, exponential, arc sin, arc cos, arc tan, arc cot or power function, also inverse functions. This probabilistic view is novel approach a problem of modeling and interpolation. Computer vision and pattern recognition are interested in appropriate methods of shape representation and curve modeling. PNC method represents the possibilities of shape reconstruction and curve interpolation via the choice of nodes combination and probability distribution function for interpolated points. It seems to be quite new look at the problem of contour representation and curve modeling in artificial intelligence and computer vision. Function for γ calculations is chosen individually at each curve modeling and it is treated as probability distribution function: γ depends on initial requirements and curve specifications.

A novel approach to evaluate blood parameters using computer vision techniques

2016 ◽  
Author(s):  
Vitoantonio Bevilacqua ◽  
Giovanni Dimauro ◽  
Francescomaria Marino ◽  
Antonio Brunetti ◽  
Fabio Cassano ◽  
...  
Keyword(s):  
Computer Vision ◽  
Blood Parameters ◽  
Novel Approach
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